1 Set some parameters which will be used later
## LogFC cutoff when working on the inclusion sets.
lfc_cutoff <- 0.1
## Adjusted p-value cutoff when working on the inclusion sets.
adjp_cutoff <- 0.1
## Increase the maximum allowed group size when working with clusterProfiler
## This should bring out some of the more general groups like 'cellbody'
max_groupsize <- 2000
## Allow groups higher up in the tree for clusterProfiler results.
go_level <- 2
## Allow 10 GO categories to be displayed when plotting.
go_categories <- 14
## MA plot point outlines
outline <- FALSE
## Speed up clusterProfiler by choosing the correct keytypes
orgdb_from <- "ENSEMBL"
default_fstring <- "~ 0 + condition"2 TODOs
- Check this for correctness.
- Reorganize it
- GSVA (Hänzelmann, Castelo, and Guinney (2013))
- Consider embedding some/many/all of the Excel outputs into the html output via xfun::embed_dir(‘excel/’)
3 Meeting with Theresa
Previous papers did not do an explicit subtraction, instead just compared to WT and kept the genes which are > in delta/het vs. wt. There are multiple ways to deal with this and that query has not yet been defined. Later, Theresa came to the conclusion that the subtraction method is not appropriate.
4 Introduction
In this document I hope to explore the freshly processed samples and perform some comparisons to see that we have the expected similarities and differences from the prior analysis performed by Theresa.
There is one way in which I expect any/all of these analyses to be explicitly different: this should include the changes produced by April’s renaming of some samples.
My intention is to produce a sample sheet which includes one column with non-umi-deduplicated results and one with deduplicated results. With the exception of the previous point, I hope that the first will be identical (or at least very close to identical) to Theresa’s result while the second I expect will be subtly different – but I am hoping subtly enough that it will not significantly change the interpretation but be a little more precise.
Lets see! I need therefore to make a change to my metadata gathering function to include the umi deduplicated result. I am thinking therefore to create a separate specification for umi-barcoded samples because looking through the logs for umi stuff when they are not used will be too much of a pain…
4.1 Small random reminder
I have a couple pictures of RPL22 to help me remember the experimental design:
- The human ribosome with RPL22 in red:
- The mouse ribosome with RPL22 in green at the center:
That second picture came from: (Li et al. (2022))
5 A note about implementation
I would like to improve this document by comparing/contrasting the methodologies performed by other groups and those performed by me in it. I never fully appreciated the suite of computational methods applied by previous groups when examining TRAP data; I instead simply followed Theresa’s notebook without considering other possibilities.
I therefore spent a little time stepping through her thesis and pulling out the relevant papers in the hopes of learning these various methods. I should therefore be able soon to compare/contrast the various methods employed by other labs in addition to copying Theresa’s logic.
5.1 The following block cannot work in the container
The following block assumes the full tree of preprocessed data with the logs from the trimmer, mapping, umi deduplication, counting, etc. As a result it cannot work in the container which has only the various count tables.
As a result, I am including a copy of this sheet after running the following block in my working tree. I suppose for the moment you will have to trust that it worked. (for right now, when testing out this container, I am just sending the R working directory to my tree for this block, then moving it back.
I will need to manually edit one column though, the symlink column from Theresa has a series of paths which do not work in the container.
umi_spec <- make_rnaseq_spec(umi = TRUE)
iprgc_2022_meta <- gather_preprocessing_metadata("sample_sheets/20240606_only_umd_sequenced.xlsx",
spec = umi_spec, species = "mm39_112", verbose = FALSE,
basedir = "preprocessing/umd_sequenced")
colnames(iprgc_2022_meta[["new_meta"]])
head(iprgc_2022_meta[["new_meta"]])sample_sheet <- "sample_sheets/20240606_only_umd_sequenced_modified.xlsx"
msigdb <- "reference/msigdb_v2024.1.Mm.db"
msig_data <- NULL
make_transparent <- function() {
ggplot2::theme(
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent'))
}I will figure out if I can leave mSigDB M2 in this image; if not, then any analyses depending on those gene sets will fail a priori.
m2_gsc <- try(load_gmt_signatures(signatures = msigdb,
signature_category = "M2"), silent = TRUE)
## I do not think I have permission to load the msigdb in the container
## So, if this fails, just load it from GSVAData, oh wait no, GSVAdata is human.
if ("try-error" %in% class(m2_gsc)) {
warning("Unable to load the M2 MsigDB data.")
}## Warning: Unable to load the M2 MsigDB data.From this point on, I am hoping/intending to pull liberally from Theresa’s notebook with a diversion to compare the three datasets:
- Pre-April renaming: E.g. Theresa’s current dataset
- Post renaming: Unless I am mistaken, this should be very similar to the above.
- Post deduplication: Given what I saw from the extracted logs in the sample sheet, I expect this to be similar but not identical to the previous two.
Lets find out! But first, annotations!
6 Annotation data
I am pulling this from Theresa’s anxontrapR_pipeline.Rmd, primarily because it looks similar to the other documents, but was modified more recently. I will change it slightly, primarily because I grabbed a new mmusculus assembly and therefore I will pull the mmusculus annotations from a specific biomart (Smedley et al. (2009)) archive that should match it.
A note from the future: multiple ensembl archive servers have been taken offline since last I ran this. Let us see if Feb. 2023 still works.
6.1 An important note!
In the recent past, ensembl queries have become inconsistent, failing much more often than ever in the past. I do not think this is the fault of ensembl; but I think I need a fallback mechanism for collecting annotation information.
In the case of ensembl, it should be trivial (but less fun) to use a combination of the locally installed orgdb and txdb databases.
This does open a risk that the set of genes with annotations will be different depending on when the container is run due to differences between the orgdb/txdb instance and the Feb 2023 biomart. I am not sure there is much I can do about that except to bundle the set of annotations I downloaded in the container – since load_biomart_annotations() does save a rda copy of its download.
ok, I did both. If you, dear reader, wish to download your own annotations, and ensembl is having troubles, the following should work without a problem; in addition the rda annotations are in /data of the container and should get loaded.
tx_gene_map <- data.frame()
mm_annot <- try(load_biomart_annotations(species = "mmusculus", year = "2023", month = "02"))## The biomart annotations file already exists, loading from it.if ("try-error" %in% class(mm_annot)) {
fields <- c("ACCNUM", "ENSEMBL", "ENSEMBLTRANS", "ENTEZID", "GENENAME", "SYMBOL")
orgdb_annot <- load_orgdb_annotations("org.Mm.eg.db", fields = fields)
gene_info <- orgdb_annot[["genes"]]
## Note, there are a bunch of variants of the txdb package one might use.
## I do not think it matters a lot for our purposes, but I suspect that if we used
## a mismatched BSgenome and tried to pull CDS sequences, that might end badly.
pkg <- "TxDb.Mmusculus.UCSC.mm10.knownGene"
tx_annot <- load_txdb_annotations(pkg)
transcripts <- tx_annot[["TX"]]
transcripts[["tx"]] <- gsub(x = transcripts[["TXNAME"]],
pattern = "\\.\\d+$", replacement = "")
mm_annot <- merge(gene_info, transcripts, by.x = "ensembltrans", by.y = "tx")
rownames(mm_annot) <- make.names(mm_annot[["ensembl"]], unique = TRUE)
} else {
mm_annot <- mm_annot[["annotation"]]
mm_annot[["txid"]] <- paste0(mm_annot[["ensembl_transcript_id"]], ".", mm_annot[["version"]])
rownames(mm_annot) <- make.names(mm_annot[["ensembl_gene_id"]], unique=TRUE)
tx_gene_map <- mm_annot[, c("txid", "ensembl_gene_id")]
}7 Hisat2 summarizedExperiments
The primary difference between my block and Theresa’s are:
- I am pulling the metadata directly from the gather_preprocessing_metadata() above.
- I am using the column ‘symlink’ which is just a copy of the existing ‘file’ column with a small change so I can load it from my directory without having to copy everything.
- I am using the ensembl genome release 39, version 112 and so pulled a somewhat newer copy of the annotation data.
- The original is named ‘v1’, followed by ‘v2’ and ‘v3’ for the other two treatments I performed.
7.1 Color choices and reused parameters
Given that we are excluding a bunch of the older samples, the set of colors I expect to find is different; so I will make explicit here the various colors used to denote location/genotype/time/etc.
April turned me onto this website ‘paletton.com’ for this kind of stuff and I will try and pick out palettes which basically match what I am getting with the original colors.
color_choices <- list(
"all" = list(
"p08_het_dlgn" = "#E7298A",
"p15_het_dlgn" = "#E7298A",
"p08_het_retina" = "#238B45",
"p15_het_retina" = "#238B45",
"p08_het_scn" = "#4292C6",
"p15_het_scn" = "#4292C6",
"p08_ko_dlgn" = "#C994C7",
"p15_ko_dlgn" = "#C994C7",
"p08_ko_retina" = "#74c476",
"p15_ko_retina" = "#74c476",
"p08_ko_scn" = "#9BCAE1",
"p15_ko_scn" = "#9BCAE1",
"p08_wt_dlgn" = "#980043",
"p15_wt_dlgn" = "#980043",
"p08_wt_retina" = "#004008",
"p15_wt_retina" = "#004008",
"p08_wt_scn" = "#08519C",
"p15_wt_scn" = "#08519C",
"p60_wt_dlgn" = "#333333",
"p60_wt_retina" = "#222222",
"p60_wt_scn" = "#111111"),
"geno_loc" = list(
"het_dlgn" = "#E7298A",
"het_retina" = "#238B45",
"het_scn" = "#4292C6",
"ko_dlgn" = "#C994C7",
"ko_retina" = "#74c476",
"ko_scn" = "#9BCAE1",
"wt_dlgn" = "#980043",
"wt_retina" = "#004008",
"wt_scn" = "#08519C"),
"location" = list(
"retina" = "#004008",
"dlgn" = "#980043",
"scn" = "#08519C"),
"genotype" = list(
"wt" = "#74c476",
"het" = "#238B45",
"ko" = "#006D2C"),
"time" = list(
"p08" = "#5E104B",
"p15" = "#4E9231"))
label_column <- "mgi_symbol" ## Set the column used to extract gene symbols rather than ENSG.....
colors <- color_choices[["geno_loc"]]
time_colors <- list(
"p08_het_dlgn" = "#E7298A",
"p15_het_dlgn" = "#8a1852",
"p08_het_retina" = "#238B45",
"p15_het_retina" = "#155329",
"p08_het_scn" = "#4292C6",
"p15_het_scn" = "#275776",
"p08_ko_dlgn" = "#C994C7",
"p15_ko_dlgn" = "#785877",
"p08_ko_retina" = "#74C476",
"p15_ko_retina" = "#457546",
"p08_ko_scn" = "#9BCAE1",
"p15_ko_scn" = "#5d7987")There is one noteworthy sample: iprgc_103, it was effectively replaced when April renamed the samples and so exists in the v1 data, but not v2/v3; they instead have the newly named samples which I called iprgc_123 to iprgc_130. As a result, I copied the annotations for iprgc_123 to my column so that there is no discrepency in terms of genotype/location/time.
7.2 The original count tables
At the moment I have not included the original counts in this container because we made some changes to the mapping strategy and also found that a couple samples were mixed up in sequencing; as a result I documented all of the changes in the sample sheets and preprocessing documents and excluded the original files.
This is also why some columns in the sample sheet have suffixes like ‘adh’ and ‘atb’, those denote from whom the relevant metadata columns came from.
7.3 Recounted tables and the deduplicated result
In the following I make two more versions of the data, one remapped with the changes to the sample identities, and one with deduplication applied.
mm38_hisat_v2 <- create_se(sample_sheet, gene_info = mm_annot,
file_column = "hisat_count_table") %>%
set_conditions(fact = "geno_loc_atb") %>%
set_batches(fact = "time_atb") %>%
set_colors(color_choices[["geno_loc"]])## Reading the sample metadata.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 69 rows(samples) and 76 columns(metadata fields).## Warning in create_se(sample_sheet, gene_info = mm_annot, file_column =
## "hisat_count_table"): Some samples were removed when cross referencing the
## samples against the count data.## Matched 25404 annotations and counts.## Some annotations were lost in merging, setting them to 'undefined'.## The final summarized experiment has 25425 rows and 76 columns.## The numbers of samples by condition are:##
## het_dlgn het_retina het_scn ko_dlgn ko_retina ko_scn wt_dlgn
## 7 7 7 6 6 6 11
## wt_retina wt_scn
## 11 7## The number of samples by batch are:##
## p08 p15 p60
## 31 34 3## class: SummarizedExperiment
## dim: 25425 68
## metadata(7): notes title ... study researcher
## assays(1): ''
## rownames(25425): ENSMUSG00000000001 ENSMUSG00000000003 ...
## ENSMUSG00001074846 ENSMUSG00002076083
## rowData names(15): ensembl_gene_id ensembl_transcript_id ...
## uniprot_gn_symbol txid
## colnames(68): iprgc_62 iprgc_63 ... iprgc_129 iprgc_130
## colData names(76): rownames sampleid ... umi_dedup_mean_umi_per_pos
## umi_dedup_max_umi_per_posmm38_hisat_v3 <- create_se(sample_sheet, gene_info = mm_annot,
file_column = "umi_dedup_output_count") %>%
set_conditions(fact = "geno_loc_atb") %>%
set_batches(fact = "time_atb") %>%
set_colors(color_choices[["geno_loc"]])## Reading the sample metadata.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 69 rows(samples) and 76 columns(metadata fields).## Warning in create_se(sample_sheet, gene_info = mm_annot, file_column =
## "umi_dedup_output_count"): Some samples were removed when cross referencing the
## samples against the count data.## Matched 25404 annotations and counts.## Some annotations were lost in merging, setting them to 'undefined'.## The final summarized experiment has 25425 rows and 76 columns.## The numbers of samples by condition are:##
## het_dlgn het_retina het_scn ko_dlgn ko_retina ko_scn wt_dlgn
## 7 7 7 6 6 6 11
## wt_retina wt_scn
## 11 7## The number of samples by batch are:##
## p08 p15 p60
## 31 34 3## class: SummarizedExperiment
## dim: 25425 68
## metadata(7): notes title ... study researcher
## assays(1): ''
## rownames(25425): ENSMUSG00000000001 ENSMUSG00000000003 ...
## ENSMUSG00001074846 ENSMUSG00002076083
## rowData names(15): ensembl_gene_id ensembl_transcript_id ...
## uniprot_gn_symbol txid
## colnames(68): iprgc_62 iprgc_63 ... iprgc_129 iprgc_130
## colData names(76): rownames sampleid ... umi_dedup_mean_umi_per_pos
## umi_dedup_max_umi_per_posall_fact <- paste0(colData(mm38_hisat_v3)[["time_atb"]], "_",
colData(mm38_hisat_v3)[["geno_loc_atb"]])
colData(mm38_hisat_v3)[["time_geno_loc"]] <- all_factNote the end of the previous block, I created a factor out of the combination of time, genotype, and location. In a future invocation of this notebook, I will change the pairwise comparisons to add each of these three factors to the statistical model instead of this. The code to do that is not quite ready yet.
8 Non-zero Counts per Sample
Let’s look at the number of non-zero genes for all samples versus the coverage.
As above, this does not get run because I did not copy the count tables.
But these do!
## The colors used in the expressionset are: #004008, #08519C, #238B45, #4292C6, #74c476, #980043, #9BCAE1, #C994C7, #E7298A.
## The following samples have less than 16526.25 genes.## [1] "iprgc_62" "iprgc_63" "iprgc_64" "iprgc_66" "iprgc_67" "iprgc_68"
## [7] "iprgc_70" "iprgc_71" "iprgc_72" "iprgc_73" "iprgc_74" "iprgc_75"
## [13] "iprgc_77" "iprgc_78" "iprgc_80" "iprgc_81" "iprgc_82" "iprgc_83"
## [19] "iprgc_84" "iprgc_85" "iprgc_86" "iprgc_87" "iprgc_88" "iprgc_89"
## [25] "iprgc_90" "iprgc_91" "iprgc_92" "iprgc_93" "iprgc_94" "iprgc_95"
## [31] "iprgc_96" "iprgc_97" "iprgc_98" "iprgc_100" "iprgc_102" "iprgc_104"
## [37] "iprgc_105" "iprgc_106" "iprgc_107" "iprgc_108" "iprgc_110" "iprgc_111"
## [43] "iprgc_112" "iprgc_113" "iprgc_114" "iprgc_115" "iprgc_117" "iprgc_118"
## [49] "iprgc_121" "iprgc_123" "iprgc_124" "iprgc_125" "iprgc_126" "iprgc_127"
## [55] "iprgc_128" "iprgc_129" "iprgc_130"## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## i Please use `linewidth` instead.
## i The deprecated feature was likely used in the hpgltools package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## A non-zero genes plot of 68 samples.
## These samples have an average 13.7 CPM coverage and 15744 genes observed, ranging from 13692 to
## 17083.
## Warning in pp(file = "01diagnostic_images/nonzero_v2_unfiltered.pdf"): The
## directory: 01diagnostic_images does not exist, will attempt to create it.v2_nonzero[["plot"]]
plotted <- dev.off()
v3_nonzero <- plot_nonzero(mm38_hisat_v3, y_intercept = 0.65)## The following samples have less than 16526.25 genes.## [1] "iprgc_62" "iprgc_63" "iprgc_64" "iprgc_66" "iprgc_67" "iprgc_68"
## [7] "iprgc_70" "iprgc_71" "iprgc_72" "iprgc_73" "iprgc_74" "iprgc_75"
## [13] "iprgc_77" "iprgc_78" "iprgc_80" "iprgc_81" "iprgc_82" "iprgc_83"
## [19] "iprgc_84" "iprgc_85" "iprgc_86" "iprgc_87" "iprgc_88" "iprgc_89"
## [25] "iprgc_90" "iprgc_91" "iprgc_92" "iprgc_93" "iprgc_94" "iprgc_95"
## [31] "iprgc_96" "iprgc_97" "iprgc_98" "iprgc_100" "iprgc_102" "iprgc_104"
## [37] "iprgc_105" "iprgc_106" "iprgc_107" "iprgc_108" "iprgc_110" "iprgc_111"
## [43] "iprgc_112" "iprgc_113" "iprgc_114" "iprgc_115" "iprgc_117" "iprgc_118"
## [49] "iprgc_119" "iprgc_121" "iprgc_123" "iprgc_124" "iprgc_125" "iprgc_126"
## [55] "iprgc_127" "iprgc_128" "iprgc_129" "iprgc_130"## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.## A non-zero genes plot of 68 samples.
## These samples have an average 4.803 CPM coverage and 15787 genes observed, ranging from 13868 to
## 17101.
pp(file = "01diagnostic_images/nonzero_v3_unfiltered.pdf")
v3_nonzero[["plot"]]
plotted <- dev.off()Oh wow, I did not expect such a profound effect on the cpm values on the more saturated libraries. I guess in retrospect I should have?
Also note to self, we are not messing with p60.
8.1 Exclude p60
8.2 Replot the nonzero gene plots
## The following samples have less than 16526.25 genes.## [1] "iprgc_62" "iprgc_63" "iprgc_64" "iprgc_66" "iprgc_67" "iprgc_68"
## [7] "iprgc_70" "iprgc_71" "iprgc_72" "iprgc_73" "iprgc_74" "iprgc_75"
## [13] "iprgc_77" "iprgc_81" "iprgc_82" "iprgc_83" "iprgc_84" "iprgc_85"
## [19] "iprgc_86" "iprgc_87" "iprgc_88" "iprgc_89" "iprgc_90" "iprgc_91"
## [25] "iprgc_92" "iprgc_93" "iprgc_94" "iprgc_95" "iprgc_96" "iprgc_97"
## [31] "iprgc_98" "iprgc_100" "iprgc_102" "iprgc_104" "iprgc_105" "iprgc_106"
## [37] "iprgc_107" "iprgc_108" "iprgc_110" "iprgc_111" "iprgc_112" "iprgc_113"
## [43] "iprgc_114" "iprgc_115" "iprgc_117" "iprgc_118" "iprgc_121" "iprgc_123"
## [49] "iprgc_124" "iprgc_125" "iprgc_126" "iprgc_127" "iprgc_128" "iprgc_129"
## [55] "iprgc_130"## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Not putting labels on the plot.pp(file = "01diagnostic_images/nonzero_v2_filt.pdf")
v2_nonzero_filt[["plot"]]
plotted <- dev.off()
v3_nonzero_filt <- plot_nonzero(mm38_hisat_v3, plot_labels = FALSE)## The following samples have less than 16526.25 genes.## [1] "iprgc_62" "iprgc_63" "iprgc_64" "iprgc_66" "iprgc_67" "iprgc_68"
## [7] "iprgc_70" "iprgc_71" "iprgc_72" "iprgc_73" "iprgc_74" "iprgc_75"
## [13] "iprgc_77" "iprgc_81" "iprgc_82" "iprgc_83" "iprgc_84" "iprgc_85"
## [19] "iprgc_86" "iprgc_87" "iprgc_88" "iprgc_89" "iprgc_90" "iprgc_91"
## [25] "iprgc_92" "iprgc_93" "iprgc_94" "iprgc_95" "iprgc_96" "iprgc_97"
## [31] "iprgc_98" "iprgc_100" "iprgc_102" "iprgc_104" "iprgc_105" "iprgc_106"
## [37] "iprgc_107" "iprgc_108" "iprgc_110" "iprgc_111" "iprgc_112" "iprgc_113"
## [43] "iprgc_114" "iprgc_115" "iprgc_117" "iprgc_118" "iprgc_119" "iprgc_121"
## [49] "iprgc_123" "iprgc_124" "iprgc_125" "iprgc_126" "iprgc_127" "iprgc_128"
## [55] "iprgc_129" "iprgc_130"## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Not putting labels on the plot.Once again, I do not want to lose the previous code, so here is the v1 invocation
9 Quick PCA, then return to Theresa’s document
v2_norm <- normalize(mm38_hisat_v2, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 10298 low-count genes (15127 remaining).## transform_counts: Found 8465 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by het_dlgn, het_retina, het_scn, ko_dlgn, ko_retina, ko_scn, wt_dlgn, wt_retina, wt_scn
## Shapes are defined by p08, p15.
pp(file = "01diagnostic_images/v2_norm_pca.pdf")
v2_norm_pca[["plot"]]
plotted <- dev.off()
v3_norm <- normalize(mm38_hisat_v3, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 10156 low-count genes (15269 remaining).## transform_counts: Found 9347 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by het_dlgn, het_retina, het_scn, ko_dlgn, ko_retina, ko_scn, wt_dlgn, wt_retina, wt_scn
## Shapes are defined by p08, p15.
Ibid.
v1_norm <- normalize(mm38_hisat_v1, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)
plot_pca(v1_norm)To my eyes it looks like we just have 1 weirdo p15 sample? Deduplication had a minor but significant effect on the PCA.
With that in mind, let us look at Theresa’s WORKING document and see what we can recapitulate.
Theresa’s document: The TRAP protocol has some variability which is introduced at different stpdf including homogenization, antibody labeling, pulldown efficiency/specificity, sample handling during cleanup stpdf, and library prep/sequencing. We know from Rashmi’s QC that there is variability at the level of pulldown efficiency (amount of RNA isolated). She is doing a good job of keeping track of this for all her samples and we have validated her P8 results (attached supplementary figure 3D). We consistently see clear differences between control and cre samples for the retina, which makes sense because the cell bodies are in the retina. The target tissue differences are smaller, which also makes sense for axon-TRAP. We think that some of her P15 samples are not good based on low amounts of isolated RNA from cre(+) retina samples. We plan to drop these samples and not perform additional isolations at this time point. Based on this (and the general lack of large developmental effects), we were planning to focus on presenting the P8 data only in the paper. Interested to hear your thoughts in this…
My notes: Theresa’s first operations in this notebook were to:
- Set location as condition, genotype as batch.
- Perform PCA before/after sva.
v3_loc_geno <- set_conditions(mm38_hisat_v3, fact = "location_atb",
colors = color_choices[["location"]]) %>%
set_batches(fact = "genotype_atb")## The numbers of samples by condition are:##
## dlgn retina scn
## 23 23 19## The number of samples by batch are:##
## het ko wt
## 21 18 269.1 The associated PCA
At different times, it appears to me that Theresa has preferred slightly different normalization methods, primarily a mix of TMM and quantile.
Thus I will use different suffix letters to denote various normalizations employed, and if they turn out the same I will pick one arbitrarily.
loc_geno_nq <- normalize(v3_loc_geno, transform = "log2", convert = "cpm",
filter = TRUE, norm = "quant")## Removing 10156 low-count genes (15269 remaining).## transform_counts: Found 9347 values equal to 0, adding 1 to the matrix.location_genotype_pca <- plot_pca(loc_geno_nq)
pp(file = "01diagnostic_images/location_genotype_norm_pca.pdf")
location_genotype_pca[["plot"]]
plotted <- dev.off()
location_genotype_pca## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by dlgn, retina, scn
## Shapes are defined by het, ko, wt.
## ok, I have two weirdo samples which look very much like they are actually dlgn.
## These are sample IDs iprgc_66 and iprgc_130
loc_geno_nt <- normalize(v3_loc_geno, transform = "log2", convert = "cpm",
filter = TRUE, norm = "tmm")## Removing 10156 low-count genes (15269 remaining).## transform_counts: Found 42869 values equal to 0, adding 1 to the matrix.location_genotype_tmm_pca <- plot_pca(loc_geno_nt)
pp(file = "01diagnostic_images/location_genotype_tmm_pca.pdf")
location_genotype_tmm_pca[["plot"]]## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable
## convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable
## convergence failure## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by dlgn, retina, scn
## Shapes are defined by het, ko, wt.## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable
## convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable
## convergence failure
A random thought about these PCA plots, it might be worth while to add a panel below the legend with the sample numbers per condition/batch.
Of course, the same information is provided in a more fun fashion via my silly sankey function:
sample_sankey <- plot_meta_sankey(v3_loc_geno, color_choices = color_choices,
factors = c("genotype_atb", "location_atb", "time_atb"))## Warning: attributes are not identical across measure variables; they will be
## dropped## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## i Please use the `linewidth` argument instead.
## i The deprecated feature was likely used in the ggsankey package.
## Please report the issue at <https://github.com/davidsjoberg/ggsankey/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.pp(file = "01diagnostic_images/design_sankey.pdf")
sample_sankey[["ggplot"]]
plotted <- dev.off()
sample_sankey## $design
## rownames sampleid genotype location time condition cellcompartment
## iprgc_62 iprgc_62 iprgc_62 het dlgn p15 dlgn axon
## iprgc_63 iprgc_63 iprgc_63 het dlgn p15 dlgn axon
## iprgc_64 iprgc_64 iprgc_64 het retina p15 retina soma
## iprgc_65 iprgc_65 iprgc_65 het retina p15 retina soma
## iprgc_66 iprgc_66 iprgc_66 het scn p15 scn axon
## iprgc_67 iprgc_67 iprgc_67 het scn p15 scn axon
## iprgc_68 iprgc_68 iprgc_68 ko dlgn p15 dlgn axon
## iprgc_69 iprgc_69 iprgc_69 ko retina p15 retina soma
## iprgc_70 iprgc_70 iprgc_70 ko scn p15 scn axon
## iprgc_71 iprgc_71 iprgc_71 wt dlgn p15 dlgn axon
## iprgc_72 iprgc_72 iprgc_72 wt dlgn p15 dlgn axon
## iprgc_73 iprgc_73 iprgc_73 wt dlgn p15 dlgn axon
## iprgc_74 iprgc_74 iprgc_74 wt retina p15 retina soma
## iprgc_75 iprgc_75 iprgc_75 wt retina p15 retina soma
## iprgc_76 iprgc_76 iprgc_76 wt retina p15 retina soma
## iprgc_77 iprgc_77 iprgc_77 wt scn p15 scn axon
## iprgc_81 iprgc_81 iprgc_81 wt dlgn p08 dlgn axon
## iprgc_82 iprgc_82 iprgc_82 wt dlgn p08 dlgn axon
## iprgc_83 iprgc_83 iprgc_83 wt retina p08 retina soma
## iprgc_84 iprgc_84 iprgc_84 wt retina p08 retina soma
## iprgc_85 iprgc_85 iprgc_85 het dlgn p15 scn axon
## iprgc_86 iprgc_86 iprgc_86 ko dlgn p15 retina soma
## iprgc_87 iprgc_87 iprgc_87 het retina p15 dlgn axon
## iprgc_88 iprgc_88 iprgc_88 ko dlgn p08 dlgn axon
## iprgc_89 iprgc_89 iprgc_89 ko retina p15 scn axon
## iprgc_90 iprgc_90 iprgc_90 ko retina p08 retina soma
## iprgc_91 iprgc_91 iprgc_91 wt retina p08 retina soma
## iprgc_92 iprgc_92 iprgc_92 wt retina p15 dlgn axon
## iprgc_93 iprgc_93 iprgc_93 wt dlgn p15 scn axon
## iprgc_94 iprgc_94 iprgc_94 ko scn p15 dlgn axon
## iprgc_95 iprgc_95 iprgc_95 het scn p15 scn axon
## iprgc_96 iprgc_96 iprgc_96 ko scn p08 retina soma
## iprgc_97 iprgc_97 iprgc_97 het scn p08 scn axon
## iprgc_98 iprgc_98 iprgc_98 het dlgn p08 dlgn axon
## iprgc_99 iprgc_99 iprgc_99 het retina p08 retina soma
## iprgc_100 iprgc_100 iprgc_100 wt scn p08 dlgn axon
## iprgc_101 iprgc_101 iprgc_101 wt dlgn p08 retina soma
## iprgc_102 iprgc_102 iprgc_102 wt scn p15 scn axon
## iprgc_104 iprgc_104 iprgc_104 het dlgn p08 dlgn axon
## iprgc_105 iprgc_105 iprgc_105 het dlgn p08 dlgn axon
## iprgc_106 iprgc_106 iprgc_106 het dlgn p15 dlgn axon
## iprgc_107 iprgc_107 iprgc_107 ko dlgn p08 dlgn axon
## iprgc_108 iprgc_108 iprgc_108 ko dlgn p15 dlgn axon
## iprgc_109 iprgc_109 iprgc_109 wt retina p08 retina soma
## iprgc_110 iprgc_110 iprgc_110 het scn p08 scn axon
## iprgc_111 iprgc_111 iprgc_111 het scn p08 scn axon
## iprgc_112 iprgc_112 iprgc_112 het scn p15 scn axon
## iprgc_113 iprgc_113 iprgc_113 ko scn p08 scn axon
## iprgc_114 iprgc_114 iprgc_114 ko scn p15 scn axon
## iprgc_115 iprgc_115 iprgc_115 wt retina p08 retina soma
## iprgc_116 iprgc_116 iprgc_116 wt retina p15 retina soma
## iprgc_117 iprgc_117 iprgc_117 het retina p08 retina soma
## iprgc_118 iprgc_118 iprgc_118 het retina p08 retina soma
## iprgc_119 iprgc_119 iprgc_119 het retina p15 retina soma
## iprgc_120 iprgc_120 iprgc_120 ko retina p08 retina soma
## iprgc_121 iprgc_121 iprgc_121 ko retina p08 retina soma
## iprgc_122 iprgc_122 iprgc_122 ko retina p15 retina soma
## iprgc_123 iprgc_123 iprgc_123 <NA> <NA> <NA> dlgn <NA>
## iprgc_124 iprgc_124 iprgc_124 <NA> <NA> <NA> scn <NA>
## iprgc_125 iprgc_125 iprgc_125 <NA> <NA> <NA> dlgn <NA>
## iprgc_126 iprgc_126 iprgc_126 <NA> <NA> <NA> dlgn <NA>
## iprgc_127 iprgc_127 iprgc_127 <NA> <NA> <NA> dlgn <NA>
## iprgc_128 iprgc_128 iprgc_128 <NA> <NA> <NA> scn <NA>
## iprgc_129 iprgc_129 iprgc_129 <NA> <NA> <NA> scn <NA>
## iprgc_130 iprgc_130 iprgc_130 <NA> <NA> <NA> scn <NA>
## raw_r1
## iprgc_62 P15_Het_dLGN_S1A_S16_R1_001.fastq.gz
## iprgc_63 P15_Het_dLGN_S2A_S17_R1_001.fastq.gz
## iprgc_64 P15_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_65 P15_Het_Ret_S2A_S8_R1_001.fastq.gz
## iprgc_66 P15_Het_SCN_S1A_S21_R1_001.fastq.gz
## iprgc_67 P15_Het_SCN_S2A_S22_R1_001.fastq.gz
## iprgc_68 P15_KO_dLGN_S1A_S18_R1_001.fastq.gz
## iprgc_69 P15_KO_Ret_S1A_S9_R1_001.fastq.gz
## iprgc_70 P15_KO_SCN_S1A_S23_R1_001.fastq.gz
## iprgc_71 P15_WT_dLGN_S1A_S12_R1_001.fastq.gz
## iprgc_72 P15_WT_dLGN_S2A_S13_R1_001.fastq.gz
## iprgc_73 P15_WT_dLGN_S3A_S14_R1_001.fastq.gz
## iprgc_74 P15_WT_Ret_S1A_S3_R1_001.fastq.gz
## iprgc_75 P15_WT_Ret_S2A_S4_R1_001.fastq.gz
## iprgc_76 P15_WT_Ret_S3A_S5_R1_001.fastq.gz
## iprgc_77 P15_WT_SCN_S3A_S19_R1_001.fastq.gz
## iprgc_81 P8_WT_dLGN_S1A_S10_R1_001.fastq.gz
## iprgc_82 P8_WT_dLGN_S2A_S11_R1_001.fastq.gz
## iprgc_83 P8_WT_Ret_S1A_S1_R1_001.fastq.gz
## iprgc_84 P8_WT_Ret_S2A_S2_R1_001.fastq.gz
## iprgc_85 P8_KO_SCN_S1A_S12_R1_001.fastq.gz
## iprgc_86 P8_KO_Ret_S1A_S10_R1_001.fastq.gz
## iprgc_87 P8_KO_dLGN_S1A_S11_R1_001.fastq.gz
## iprgc_88 P8_Het_dLGN_S1A_S8_R1_001.fastq.gz
## iprgc_89 P8_Het_SCN_S1A_S9_R1_001.fastq.gz
## iprgc_90 P8_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_91 P8_WT_Ret_S1A_S1_R2_001.fastq.gz
## iprgc_92 P8_WT_dLGN_S1A_S2_R1_001.fastq.gz
## iprgc_93 P8_WT_SCN_S1A_S3_R1_001.fastq.gz
## iprgc_94 P15_Het_dLGN_S1B_S17_R1_001.fastq.gz
## iprgc_95 P15_Het_SCN_S1B_S18_R1_001.fastq.gz
## iprgc_96 P15_Het_Ret_S1B_S16_R1_001.fastq.gz
## iprgc_97 P15_WT_SCN_S1A_S6_R1_001.fastq.gz
## iprgc_98 P15_WT_dLGN_S1A_S5_R1_001.fastq.gz
## iprgc_99 P15_WT_Ret_S1A_S4_R1_001.fastq.gz
## iprgc_100 P15_KO_dLGN_S1A_S14_R1_001.fastq.gz
## iprgc_101 P15_KO_Ret_S1A_S13_R1_001.fastq.gz
## iprgc_102 P15_KO_SCN_S1A_S15_R1_001.fastq.gz
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R1_001.fastq.gz
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R1_001.fastq.gz
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R1_001.fastq.gz
## iprgc_107 16_P8_KO_dLGN_S2_S9_R1_001.fastq.gz
## iprgc_108 18_P15_KO_dLGN_S2_S10_R1_001.fastq.gz
## iprgc_109 1b_P8_WT_Ret_S2_S16_R1_001.fastq.gz
## iprgc_110 22b_P8_Het_SCN_S2_S11_R1_001.fastq.gz
## iprgc_111 23b_P8_Het_SCN_S3_S12_R1_001.fastq.gz
## iprgc_112 24b_P15_Het_SCN_S2_S13_R1_001.fastq.gz
## iprgc_113 25_P8_KO_SCN_S2_S14_R1_001.fastq.gz
## iprgc_114 27_P15_KO_SCN_S2_S15_R1_001.fastq.gz
## iprgc_115 2b_P8_WT_Ret_S3_S17_R1_001.fastq.gz
## iprgc_116 3b_P15_WT_Ret_S3_S18_R1_001.fastq.gz
## iprgc_117 4b_P8_Het_Ret_S2_S1_R1_001.fastq.gz
## iprgc_118 5_P8_Het_Ret_S3_S2_R1_001.fastq.gz
## iprgc_119 6b_P15_Het_Ret_S2_S3_R1_001.fastq.gz
## iprgc_120 7_P8_KO_Ret_S2_S4_R1_001.fastq.gz
## iprgc_121 8_P8_KO_Ret_S3_S19_R1_001.fastq.gz
## iprgc_122 9_P15_KO_Ret_S2_S5_R1_001.fastq.gz
## iprgc_123 <NA>
## iprgc_124 <NA>
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## iprgc_130 <NA>
## raw_r2 batch date_received
## iprgc_62 P15_Het_dLGN_S1A_S16_R2_001.fastq.gz het b20220518
## iprgc_63 P15_Het_dLGN_S2A_S17_R2_001.fastq.gz het b20220518
## iprgc_64 P15_Het_Ret_S1A_S7_R2_001.fastq.gz het b20220518
## iprgc_65 P15_Het_Ret_S2A_S8_R2_001.fastq.gz het b20220518
## iprgc_66 P15_Het_SCN_S1A_S21_R2_001.fastq.gz het b20220518
## iprgc_67 P15_Het_SCN_S2A_S22_R2_001.fastq.gz het b20220518
## iprgc_68 P15_KO_dLGN_S1A_S18_R2_001.fastq.gz ko b20220518
## iprgc_69 P15_KO_Ret_S1A_S9_R2_001.fastq.gz ko b20220518
## iprgc_70 P15_KO_SCN_S1A_S23_R2_001.fastq.gz ko b20220518
## iprgc_71 P15_WT_dLGN_S1A_S12_R2_001.fastq.gz wt b20220518
## iprgc_72 P15_WT_dLGN_S2A_S13_R2_001.fastq.gz wt b20220518
## iprgc_73 P15_WT_dLGN_S3A_S14_R2_001.fastq.gz wt b20220518
## iprgc_74 P15_WT_Ret_S1A_S3_R2_001.fastq.gz wt b20220518
## iprgc_75 P15_WT_Ret_S2A_S4_R2_001.fastq.gz wt b20220518
## iprgc_76 P15_WT_Ret_S3A_S5_R2_001.fastq.gz wt b20220518
## iprgc_77 P15_WT_SCN_S3A_S19_R2_001.fastq.gz wt b20220518
## iprgc_81 P8_WT_dLGN_S1A_S10_R2_001.fastq.gz wt b20220518
## iprgc_82 P8_WT_dLGN_S2A_S11_R2_001.fastq.gz wt b20220518
## iprgc_83 P8_WT_Ret_S1A_S1_R2_001.fastq.gz wt b20220518
## iprgc_84 P8_WT_Ret_S2A_S2_R2_001.fastq.gz wt b20220518
## iprgc_85 P8_KO_SCN_S1A_S12_R2_001.fastq.gz ko b20230515
## iprgc_86 P8_KO_Ret_S1A_S10_R2_001.fastq.gz ko b20230515
## iprgc_87 P8_KO_dLGN_S1A_S11_R2_001.fastq.gz ko b20230515
## iprgc_88 P8_Het_dLGN_S1A_S8_R2_001.fastq.gz het b20230515
## iprgc_89 P8_Het_SCN_S1A_S9_R2_001.fastq.gz het b20230515
## iprgc_90 P8_Het_Ret_S1A_S7_R2_001.fastq.gz het b20230515
## iprgc_91 P8_WT_Ret_S1A_S1_R2_001.fastq.gz wt b20230515
## iprgc_92 P8_WT_dLGN_S1A_S2_R2_001.fastq.gz wt b20230515
## iprgc_93 P8_WT_SCN_S1A_S3_R2_001.fastq.gz wt b20230515
## iprgc_94 P15_Het_dLGN_S1B_S17_R2_001.fastq.gz het b20230515
## iprgc_95 P15_Het_SCN_S1B_S18_R2_001.fastq.gz het b20230516
## iprgc_96 P15_Het_Ret_S1B_S16_R2_001.fastq.gz het b20230517
## iprgc_97 P15_WT_SCN_S1A_S6_R2_001.fastq.gz wt b20230518
## iprgc_98 P15_WT_dLGN_S1A_S5_R2_001.fastq.gz wt b20230519
## iprgc_99 P15_WT_Ret_S1A_S4_R2_001.fastq.gz wt b20230520
## iprgc_100 P15_KO_dLGN_S1A_S14_R2_001.fastq.gz ko b20230521
## iprgc_101 P15_KO_Ret_S1A_S13_R2_001.fastq.gz ko b20230522
## iprgc_102 P15_KO_SCN_S1A_S15_R2_001.fastq.gz ko b20230523
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R2_001.fastq.gz het b20231010
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R2_001.fastq.gz het b20231010
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R2_001.fastq.gz het b20231010
## iprgc_107 16_P8_KO_dLGN_S2_S9_R2_001.fastq.gz ko b20231010
## iprgc_108 18_P15_KO_dLGN_S2_S10_R2_001.fastq.gz ko b20231010
## iprgc_109 1b_P8_WT_Ret_S2_S16_R2_001.fastq.gz wt b20231010
## iprgc_110 22b_P8_Het_SCN_S2_S11_R2_001.fastq.gz het b20231010
## iprgc_111 23b_P8_Het_SCN_S3_S12_R2_001.fastq.gz het b20231010
## iprgc_112 24b_P15_Het_SCN_S2_S13_R2_001.fastq.gz het b20231010
## iprgc_113 25_P8_KO_SCN_S2_S14_R2_001.fastq.gz ko b20231010
## iprgc_114 27_P15_KO_SCN_S2_S15_R2_001.fastq.gz ko b20231010
## iprgc_115 2b_P8_WT_Ret_S3_S17_R2_001.fastq.gz wt b20231010
## iprgc_116 3b_P15_WT_Ret_S3_S18_R2_001.fastq.gz wt b20231010
## iprgc_117 4b_P8_Het_Ret_S2_S1_R2_001.fastq.gz het b20231010
## iprgc_118 5_P8_Het_Ret_S3_S2_R2_001.fastq.gz het b20231010
## iprgc_119 6b_P15_Het_Ret_S2_S3_R2_001.fastq.gz het b20231010
## iprgc_120 7_P8_KO_Ret_S2_S4_R2_001.fastq.gz ko b20231010
## iprgc_121 8_P8_KO_Ret_S3_S19_R2_001.fastq.gz ko b20231010
## iprgc_122 9_P15_KO_Ret_S2_S5_R2_001.fastq.gz ko b20231010
## iprgc_123 <NA> ko <NA>
## iprgc_124 <NA> ko <NA>
## iprgc_125 <NA> wt <NA>
## iprgc_126 <NA> wt <NA>
## iprgc_127 <NA> wt <NA>
## iprgc_128 <NA> wt <NA>
## iprgc_129 <NA> wt <NA>
## iprgc_130 <NA> wt <NA>
## file
## iprgc_62 preprocessing_new/iprgc_62/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_63 preprocessing_new/iprgc_63/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_64 preprocessing_new/iprgc_64/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_65 preprocessing_new/iprgc_65/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_66 preprocessing_new/iprgc_66/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_67 preprocessing_new/iprgc_67/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_68 preprocessing_new/iprgc_68/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_69 preprocessing_new/iprgc_69/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_70 preprocessing_new/iprgc_70/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_71 preprocessing_new/iprgc_71/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_72 preprocessing_new/iprgc_72/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_73 preprocessing_new/iprgc_73/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_74 preprocessing_new/iprgc_74/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_75 preprocessing_new/iprgc_75/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_76 preprocessing_new/iprgc_76/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_77 preprocessing_new/iprgc_77/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_81 preprocessing/iprgc_81/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_82 preprocessing/iprgc_82/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_83 preprocessing/iprgc_83/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_84 preprocessing/iprgc_84/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_85 preprocessing/iprgc_85/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_86 preprocessing/iprgc_86/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_87 preprocessing/iprgc_87/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_88 preprocessing/iprgc_88/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_89 preprocessing/iprgc_89/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_90 preprocessing/iprgc_90/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_91 preprocessing/iprgc_91/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_92 preprocessing/iprgc_92/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_93 preprocessing/iprgc_93/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_94 preprocessing/iprgc_94/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_95 preprocessing/iprgc_95/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_96 preprocessing/iprgc_96/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_97 preprocessing/iprgc_97/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_98 preprocessing/iprgc_98/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_99 preprocessing/iprgc_99/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_100 preprocessing/iprgc_100/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_101 preprocessing/iprgc_101/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_102 preprocessing/iprgc_102/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_104 preprocessing/iprgc_104/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_105 preprocessing/iprgc_105/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_106 preprocessing/iprgc_106/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_107 preprocessing/iprgc_107/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_108 preprocessing/iprgc_108/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_109 preprocessing/iprgc_109/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_110 preprocessing/iprgc_110/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_111 preprocessing/iprgc_111/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_112 preprocessing/iprgc_112/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_113 preprocessing/iprgc_113/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_114 preprocessing/iprgc_114/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_115 preprocessing/iprgc_115/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_116 preprocessing/iprgc_116/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_117 preprocessing/iprgc_117/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_118 preprocessing/iprgc_118/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_119 preprocessing/iprgc_119/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_120 preprocessing/iprgc_120/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_121 preprocessing/iprgc_121/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_122 preprocessing/iprgc_122/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_123 <NA>
## iprgc_124 <NA>
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## iprgc_130 <NA>
## symlink
## iprgc_62 preprocessing/preprocessing_new_taa/iprgc_62/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_63 preprocessing/preprocessing_new_taa/iprgc_63/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_64 preprocessing/preprocessing_new_taa/iprgc_64/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_65 preprocessing/preprocessing_new_taa/iprgc_65/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_66 preprocessing/preprocessing_new_taa/iprgc_66/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_67 preprocessing/preprocessing_new_taa/iprgc_67/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_68 preprocessing/preprocessing_new_taa/iprgc_68/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_69 preprocessing/preprocessing_new_taa/iprgc_69/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_70 preprocessing/preprocessing_new_taa/iprgc_70/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_71 preprocessing/preprocessing_new_taa/iprgc_71/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_72 preprocessing/preprocessing_new_taa/iprgc_72/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_73 preprocessing/preprocessing_new_taa/iprgc_73/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_74 preprocessing/preprocessing_new_taa/iprgc_74/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_75 preprocessing/preprocessing_new_taa/iprgc_75/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_76 preprocessing/preprocessing_new_taa/iprgc_76/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_77 preprocessing/preprocessing_new_taa/iprgc_77/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_81 preprocessing/preprocessing_taa/iprgc_81/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_82 preprocessing/preprocessing_taa/iprgc_82/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_83 preprocessing/preprocessing_taa/iprgc_83/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_84 preprocessing/preprocessing_taa/iprgc_84/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_85 <NA>
## iprgc_86 preprocessing/preprocessing_taa/iprgc_85/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_87 preprocessing/preprocessing_taa/iprgc_86/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_88 preprocessing/preprocessing_taa/iprgc_87/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_89 preprocessing/preprocessing_taa/iprgc_88/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_90 preprocessing/preprocessing_taa/iprgc_89/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_91 preprocessing/preprocessing_taa/iprgc_90/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_92 preprocessing/preprocessing_taa/iprgc_91/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_93 preprocessing/preprocessing_taa/iprgc_92/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_94 preprocessing/preprocessing_taa/iprgc_93/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_95 preprocessing/preprocessing_taa/iprgc_94/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_96 preprocessing/preprocessing_taa/iprgc_95/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_97 preprocessing/preprocessing_taa/iprgc_96/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_98 preprocessing/preprocessing_taa/iprgc_97/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_99 preprocessing/preprocessing_taa/iprgc_98/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_100 preprocessing/preprocessing_taa/iprgc_99/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_101 preprocessing/preprocessing_taa/iprgc_100/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_102 preprocessing/preprocessing_taa/iprgc_101/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_104 <NA>
## iprgc_105 preprocessing/preprocessing_taa/iprgc_103/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_106 preprocessing/preprocessing_taa/iprgc_104/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_107 preprocessing/preprocessing_taa/iprgc_105/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_108 preprocessing/preprocessing_taa/iprgc_106/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_109 preprocessing/preprocessing_taa/iprgc_107/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_110 preprocessing/preprocessing_taa/iprgc_108/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_111 preprocessing/preprocessing_taa/iprgc_109/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_112 preprocessing/preprocessing_taa/iprgc_110/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_113 preprocessing/preprocessing_taa/iprgc_111/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_114 preprocessing/preprocessing_taa/iprgc_112/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_115 preprocessing/preprocessing_taa/iprgc_113/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_116 preprocessing/preprocessing_taa/iprgc_114/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_117 preprocessing/preprocessing_taa/iprgc_115/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_118 preprocessing/preprocessing_taa/iprgc_116/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_119 preprocessing/preprocessing_taa/iprgc_117/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_120 preprocessing/preprocessing_taa/iprgc_118/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_121 preprocessing/preprocessing_taa/iprgc_119/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_122 preprocessing/preprocessing_taa/iprgc_120/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_123 preprocessing/preprocessing_taa/iprgc_121/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_124 preprocessing/preprocessing_taa/iprgc_122/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## iprgc_130 <NA>
## totalreads trim1_both_surviving trim1_suriving_pct trim1_forward
## iprgc_62 21454044 7307772 0.3406 10575438
## iprgc_63 15326358 5673117 0.3702 8006539
## iprgc_64 18003256 6665716 0.3703 9518432
## iprgc_65 20929623 7076283 0.3381 10013788
## iprgc_66 15724842 6013402 0.3824 8415519
## iprgc_67 15456881 5714266 0.3697 8184877
## iprgc_68 19666257 6900044 0.3509 9066997
## iprgc_69 16552894 6100310 0.3685 8839919
## iprgc_70 13740575 5066146 0.3687 7296079
## iprgc_71 15525499 6038811 0.3890 8406723
## iprgc_72 17094474 6541727 0.3827 9369448
## iprgc_73 15794837 6056416 0.3834 8661904
## iprgc_74 15775224 6094043 0.3863 8876775
## iprgc_75 16328183 6160227 0.3773 8655792
## iprgc_76 24180028 9053454 0.3744 13014766
## iprgc_77 17538867 5891680 0.3359 8619704
## iprgc_81 16532098 6408776 0.3877 8634609
## iprgc_82 15878311 5957695 0.3752 8664412
## iprgc_83 16082507 6370746 0.3961 8795845
## iprgc_84 15958920 6090563 0.3816 8659251
## iprgc_85 NA NA NA NA
## iprgc_86 NA NA NA NA
## iprgc_87 NA NA NA NA
## iprgc_88 NA NA NA NA
## iprgc_89 NA NA NA NA
## iprgc_90 NA NA NA NA
## iprgc_91 NA NA NA NA
## iprgc_92 NA NA NA NA
## iprgc_93 NA NA NA NA
## iprgc_94 NA NA NA NA
## iprgc_95 NA NA NA NA
## iprgc_96 NA NA NA NA
## iprgc_97 NA NA NA NA
## iprgc_98 NA NA NA NA
## iprgc_99 NA NA NA NA
## iprgc_100 NA NA NA NA
## iprgc_101 NA NA NA NA
## iprgc_102 NA NA NA NA
## iprgc_104 NA NA NA NA
## iprgc_105 NA NA NA NA
## iprgc_106 NA NA NA NA
## iprgc_107 NA NA NA NA
## iprgc_108 NA NA NA NA
## iprgc_109 NA NA NA NA
## iprgc_110 NA NA NA NA
## iprgc_111 NA NA NA NA
## iprgc_112 NA NA NA NA
## iprgc_113 NA NA NA NA
## iprgc_114 NA NA NA NA
## iprgc_115 NA NA NA NA
## iprgc_116 NA NA NA NA
## iprgc_117 NA NA NA NA
## iprgc_118 NA NA NA NA
## iprgc_119 NA NA NA NA
## iprgc_120 NA NA NA NA
## iprgc_121 NA NA NA NA
## iprgc_122 NA NA NA NA
## iprgc_123 NA NA NA NA
## iprgc_124 NA NA NA NA
## iprgc_125 NA NA NA NA
## iprgc_126 NA NA NA NA
## iprgc_127 NA NA NA NA
## iprgc_128 NA NA NA NA
## iprgc_129 NA NA NA NA
## iprgc_130 NA NA NA NA
## trim1_reverse trim1_dropped trim2_remaining trim2_surviving_pct
## iprgc_62 161944 3408890 19941862 0.9295
## iprgc_63 126447 1520255 14735531 0.9615
## iprgc_64 147975 1671133 17308611 0.9614
## iprgc_65 183633 3655919 19162876 0.9156
## iprgc_66 135052 1160869 15152058 0.9636
## iprgc_67 129987 1427751 14776608 0.9560
## iprgc_68 154410 3544806 18079308 0.9193
## iprgc_69 153735 1458930 15834102 0.9566
## iprgc_70 107844 1270506 13117568 0.9547
## iprgc_71 140419 939546 15001298 0.9662
## iprgc_72 148260 1035039 16587408 0.9703
## iprgc_73 139349 937168 15290737 0.9681
## iprgc_74 131880 672526 15327351 0.9716
## iprgc_75 155114 1357050 15648822 0.9584
## iprgc_76 212621 1899187 23269354 0.9623
## iprgc_77 126846 2900637 16006650 0.9126
## iprgc_81 160717 1327996 15768927 0.9538
## iprgc_82 137408 1118796 15308622 0.9641
## iprgc_83 137211 778705 15698279 0.9761
## iprgc_84 140920 1068186 15508709 0.9718
## iprgc_85 NA NA NA NA
## iprgc_86 NA NA NA NA
## iprgc_87 NA NA NA NA
## iprgc_88 NA NA NA NA
## iprgc_89 NA NA NA NA
## iprgc_90 NA NA NA NA
## iprgc_91 NA NA NA NA
## iprgc_92 NA NA NA NA
## iprgc_93 NA NA NA NA
## iprgc_94 NA NA NA NA
## iprgc_95 NA NA NA NA
## iprgc_96 NA NA NA NA
## iprgc_97 NA NA NA NA
## iprgc_98 NA NA NA NA
## iprgc_99 NA NA NA NA
## iprgc_100 NA NA NA NA
## iprgc_101 NA NA NA NA
## iprgc_102 NA NA NA NA
## iprgc_104 NA NA NA NA
## iprgc_105 NA NA NA NA
## iprgc_106 NA NA NA NA
## iprgc_107 NA NA NA NA
## iprgc_108 NA NA NA NA
## iprgc_109 NA NA NA NA
## iprgc_110 NA NA NA NA
## iprgc_111 NA NA NA NA
## iprgc_112 NA NA NA NA
## iprgc_113 NA NA NA NA
## iprgc_114 NA NA NA NA
## iprgc_115 NA NA NA NA
## iprgc_116 NA NA NA NA
## iprgc_117 NA NA NA NA
## iprgc_118 NA NA NA NA
## iprgc_119 NA NA NA NA
## iprgc_120 NA NA NA NA
## iprgc_121 NA NA NA NA
## iprgc_122 NA NA NA NA
## iprgc_123 NA NA NA NA
## iprgc_124 NA NA NA NA
## iprgc_125 NA NA NA NA
## iprgc_126 NA NA NA NA
## iprgc_127 NA NA NA NA
## iprgc_128 NA NA NA NA
## iprgc_129 NA NA NA NA
## iprgc_130 NA NA NA NA
## hisat_aligned_cogent_single hisat_aligned_cogent_multi
## iprgc_62 4403419 1083744
## iprgc_63 3913257 1576743
## iprgc_64 8017462 1026624
## iprgc_65 9046156 1156208
## iprgc_66 5991145 1028730
## iprgc_67 5936037 1208092
## iprgc_68 3617748 990881
## iprgc_69 10915725 1053089
## iprgc_70 4302666 866074
## iprgc_71 5866688 1181784
## iprgc_72 6332063 1423466
## iprgc_73 5980501 1233428
## iprgc_74 9320058 1156166
## iprgc_75 8782932 1128430
## iprgc_76 13923768 2032992
## iprgc_77 4119038 884696
## iprgc_81 6300618 1191976
## iprgc_82 6276247 1097569
## iprgc_83 9641795 3309802
## iprgc_84 9095113 2231760
## iprgc_85 NA NA
## iprgc_86 NA NA
## iprgc_87 NA NA
## iprgc_88 NA NA
## iprgc_89 NA NA
## iprgc_90 NA NA
## iprgc_91 NA NA
## iprgc_92 NA NA
## iprgc_93 NA NA
## iprgc_94 NA NA
## iprgc_95 NA NA
## iprgc_96 NA NA
## iprgc_97 NA NA
## iprgc_98 NA NA
## iprgc_99 NA NA
## iprgc_100 NA NA
## iprgc_101 NA NA
## iprgc_102 NA NA
## iprgc_104 NA NA
## iprgc_105 NA NA
## iprgc_106 NA NA
## iprgc_107 NA NA
## iprgc_108 NA NA
## iprgc_109 NA NA
## iprgc_110 NA NA
## iprgc_111 NA NA
## iprgc_112 NA NA
## iprgc_113 NA NA
## iprgc_114 NA NA
## iprgc_115 NA NA
## iprgc_116 NA NA
## iprgc_117 NA NA
## iprgc_118 NA NA
## iprgc_119 NA NA
## iprgc_120 NA NA
## iprgc_121 NA NA
## iprgc_122 NA NA
## iprgc_123 NA NA
## iprgc_124 NA NA
## iprgc_125 NA NA
## iprgc_126 NA NA
## iprgc_127 NA NA
## iprgc_128 NA NA
## iprgc_129 NA NA
## iprgc_130 NA NA
## hisat_aligned_cogent_pct rrna_aligned_single rrna_aligned_single_pct
## iprgc_62 0.2558 9981295 0.5581
## iprgc_63 0.3582 8456038 0.6181
## iprgc_64 0.5024 5806861 0.3588
## iprgc_65 0.4875 4654006 0.2723
## iprgc_66 0.4464 7223609 0.5006
## iprgc_67 0.4622 6686073 0.4810
## iprgc_68 0.2343 8880676 0.5562
## iprgc_69 0.7231 1156001 0.0774
## iprgc_70 0.3762 5198926 0.4205
## iprgc_71 0.4540 8388194 0.5807
## iprgc_72 0.4537 8912327 0.5601
## iprgc_73 0.4567 8572959 0.5825
## iprgc_74 0.6641 4950093 0.3306
## iprgc_75 0.6070 3564544 0.2406
## iprgc_76 0.6599 4499355 0.2039
## iprgc_77 0.2853 5596546 0.3857
## iprgc_81 0.4532 7013653 0.4662
## iprgc_82 0.4644 4686722 0.3205
## iprgc_83 0.8053 2613957 0.1723
## iprgc_84 0.7098 2753845 0.1867
## iprgc_85 NA NA NA
## iprgc_86 NA NA NA
## iprgc_87 NA NA NA
## iprgc_88 NA NA NA
## iprgc_89 NA NA NA
## iprgc_90 NA NA NA
## iprgc_91 NA NA NA
## iprgc_92 NA NA NA
## iprgc_93 NA NA NA
## iprgc_94 NA NA NA
## iprgc_95 NA NA NA
## iprgc_96 NA NA NA
## iprgc_97 NA NA NA
## iprgc_98 NA NA NA
## iprgc_99 NA NA NA
## iprgc_100 NA NA NA
## iprgc_101 NA NA NA
## iprgc_102 NA NA NA
## iprgc_104 NA NA NA
## iprgc_105 NA NA NA
## iprgc_106 NA NA NA
## iprgc_107 NA NA NA
## iprgc_108 NA NA NA
## iprgc_109 NA NA NA
## iprgc_110 NA NA NA
## iprgc_111 NA NA NA
## iprgc_112 NA NA NA
## iprgc_113 NA NA NA
## iprgc_114 NA NA NA
## iprgc_115 NA NA NA
## iprgc_116 NA NA NA
## iprgc_117 NA NA NA
## iprgc_118 NA NA NA
## iprgc_119 NA NA NA
## iprgc_120 NA NA NA
## iprgc_121 NA NA NA
## iprgc_122 NA NA NA
## iprgc_123 NA NA NA
## iprgc_124 NA NA NA
## iprgc_125 NA NA NA
## iprgc_126 NA NA NA
## iprgc_127 NA NA NA
## iprgc_128 NA NA NA
## iprgc_129 NA NA NA
## iprgc_130 NA NA NA
## rrna_aligned_multi rrna_aligned_multi_pct rrna_not_aligned
## iprgc_62 552408 0.0309 7349507
## iprgc_63 554987 0.0406 4668631
## iprgc_64 211357 0.0131 10165930
## iprgc_65 186314 0.0109 12249751
## iprgc_66 505801 0.0351 6699511
## iprgc_67 449142 0.0323 6763928
## iprgc_68 464529 0.0291 6621836
## iprgc_69 30867 0.0021 13753361
## iprgc_70 301939 0.0244 6861360
## iprgc_71 675994 0.0468 5381346
## iprgc_72 784989 0.0493 6213859
## iprgc_73 485581 0.0330 5659780
## iprgc_74 506909 0.0339 9513816
## iprgc_75 282107 0.0190 10969368
## iprgc_76 444836 0.0202 17124029
## iprgc_77 267421 0.0184 8647417
## iprgc_81 334632 0.0222 7695100
## iprgc_82 323766 0.0221 9611619
## iprgc_83 139135 0.0092 12413499
## iprgc_84 249586 0.0169 11746383
## iprgc_85 NA NA NA
## iprgc_86 NA NA NA
## iprgc_87 NA NA NA
## iprgc_88 NA NA NA
## iprgc_89 NA NA NA
## iprgc_90 NA NA NA
## iprgc_91 NA NA NA
## iprgc_92 NA NA NA
## iprgc_93 NA NA NA
## iprgc_94 NA NA NA
## iprgc_95 NA NA NA
## iprgc_96 NA NA NA
## iprgc_97 NA NA NA
## iprgc_98 NA NA NA
## iprgc_99 NA NA NA
## iprgc_100 NA NA NA
## iprgc_101 NA NA NA
## iprgc_102 NA NA NA
## iprgc_104 NA NA NA
## iprgc_105 NA NA NA
## iprgc_106 NA NA NA
## iprgc_107 NA NA NA
## iprgc_108 NA NA NA
## iprgc_109 NA NA NA
## iprgc_110 NA NA NA
## iprgc_111 NA NA NA
## iprgc_112 NA NA NA
## iprgc_113 NA NA NA
## iprgc_114 NA NA NA
## iprgc_115 NA NA NA
## iprgc_116 NA NA NA
## iprgc_117 NA NA NA
## iprgc_118 NA NA NA
## iprgc_119 NA NA NA
## iprgc_120 NA NA NA
## iprgc_121 NA NA NA
## iprgc_122 NA NA NA
## iprgc_123 NA NA NA
## iprgc_124 NA NA NA
## iprgc_125 NA NA NA
## iprgc_126 NA NA NA
## iprgc_127 NA NA NA
## iprgc_128 NA NA NA
## iprgc_129 NA NA NA
## iprgc_130 NA NA NA
## rrna_not_aligned_pct hisat_single hisat_multi hisat_map_pct
## iprgc_62 0.411 1848699 820527 0.1339
## iprgc_63 0.3413 1487847 590756 0.1411
## iprgc_64 0.6281 3115788 634919 0.2167
## iprgc_65 0.7168 3497108 645740 0.2162
## iprgc_66 0.4643 2450261 594272 0.2009
## iprgc_67 0.4866 2410898 657348 0.2076
## iprgc_68 0.4147 1693648 716559 0.1333
## iprgc_69 0.9206 4103748 467294 0.2887
## iprgc_70 0.555 1768274 466076 0.1703
## iprgc_71 0.3725 2429963 731130 0.2107
## iprgc_72 0.3905 2575448 795986 0.2033
## iprgc_73 0.3845 2460492 708855 0.2073
## iprgc_74 0.6355 3522006 601166 0.2690
## iprgc_75 0.7404 3382787 555319 0.2517
## iprgc_76 0.776 5278142 931930 0.2669
## iprgc_77 0.5959 1670154 491774 0.1351
## iprgc_81 0.5115 2673009 654361 0.2110
## iprgc_82 0.6573 2443165 556951 0.1960
## iprgc_83 0.8185 3764124 1427725 0.3307
## iprgc_84 0.7964 3457750 987993 0.2867
## iprgc_85 LIKELY PCT mRNA CONTENT NA NA NA
## iprgc_86 <NA> NA NA NA
## iprgc_87 <NA> NA NA NA
## iprgc_88 <NA> NA NA NA
## iprgc_89 <NA> NA NA NA
## iprgc_90 <NA> NA NA NA
## iprgc_91 <NA> NA NA NA
## iprgc_92 <NA> NA NA NA
## iprgc_93 <NA> NA NA NA
## iprgc_94 <NA> NA NA NA
## iprgc_95 <NA> NA NA NA
## iprgc_96 <NA> NA NA NA
## iprgc_97 <NA> NA NA NA
## iprgc_98 <NA> NA NA NA
## iprgc_99 <NA> NA NA NA
## iprgc_100 <NA> NA NA NA
## iprgc_101 <NA> NA NA NA
## iprgc_102 <NA> NA NA NA
## iprgc_104 <NA> NA NA NA
## iprgc_105 <NA> NA NA NA
## iprgc_106 <NA> NA NA NA
## iprgc_107 <NA> NA NA NA
## iprgc_108 <NA> NA NA NA
## iprgc_109 <NA> NA NA NA
## iprgc_110 <NA> NA NA NA
## iprgc_111 <NA> NA NA NA
## iprgc_112 <NA> NA NA NA
## iprgc_113 <NA> NA NA NA
## iprgc_114 <NA> NA NA NA
## iprgc_115 <NA> NA NA NA
## iprgc_116 <NA> NA NA NA
## iprgc_117 <NA> NA NA NA
## iprgc_118 <NA> NA NA NA
## iprgc_119 <NA> NA NA NA
## iprgc_120 <NA> NA NA NA
## iprgc_121 <NA> NA NA NA
## iprgc_122 <NA> NA NA NA
## iprgc_123 <NA> NA NA NA
## iprgc_124 <NA> NA NA NA
## iprgc_125 <NA> NA NA NA
## iprgc_126 <NA> NA NA NA
## iprgc_127 <NA> NA NA NA
## iprgc_128 <NA> NA NA NA
## iprgc_129 <NA> NA NA NA
## iprgc_130 <NA> NA NA NA
## hisat_mapped_total_reads projectah sampleah rashmissamplenameah
## iprgc_62 0.1244 021_1 16 P15_Het_dLGN_S1A
## iprgc_63 0.1356 021_1 17 P15_Het_dLGN_S2A
## iprgc_64 0.2083 021_1 7 P15_Het_Ret_S1A
## iprgc_65 0.1979 021_1 8 P15_Het_Ret_S2A
## iprgc_66 0.1936 021_1 25 P15_Het_SCN_S1A
## iprgc_67 0.1985 021_1 26 P15_Het_SCN_S2A
## iprgc_68 0.1226 021_1 18 P15_KO_dLGN_S1A
## iprgc_69 0.2761 021_1 9 P15_KO_Ret_S1A
## iprgc_70 0.1626 021_1 27 P15_KO_SCN_S1A
## iprgc_71 0.2036 021_1 12 P15_WT_dLGN_S1A
## iprgc_72 0.1972 021_1 13 P15_WT_dLGN_S2A
## iprgc_73 0.2007 021_1 14 P15_WT_dLGN_S3A
## iprgc_74 0.2614 021_1 3 P15_WT_Ret_S1A
## iprgc_75 0.2412 021_1 4 P15_WT_Ret_S2A
## iprgc_76 0.2568 021_1 5 P15_WT_Ret_S3A
## iprgc_77 0.1233 021_1 23 P15_WT_SCN_S3A
## iprgc_81 0.2013 021_1 10 P8_WT_dLGN_S1A
## iprgc_82 0.1889 021_1 11 P8_WT_dLGN_S2A
## iprgc_83 0.3228 021_1 1 P8_WT_Ret_S1A
## iprgc_84 0.2786 021_1 2 P8_WT_Ret_S2A
## iprgc_85 NA 021_2 15 P8_KO_SCN_S1A
## iprgc_86 NA 021_2 13 P8_KO_Ret_S1A
## iprgc_87 NA 021_2 14 P8_KO_dLGN_S1A
## iprgc_88 NA 021_2 8 P8_Het_dLGN_S1A
## iprgc_89 NA 021_2 9 P8_Het_SCN_S1A
## iprgc_90 NA 021_2 7 P8_Het_Ret_S1A
## iprgc_91 NA 021_2 1 P8_WT_Ret_S1A
## iprgc_92 NA 021_2 2 P8_WT_dLGN_S1A
## iprgc_93 NA 021_2 3 P8_WT_SCN_S1A
## iprgc_94 NA 021_2 20 P15_Het_dLGN_S1B
## iprgc_95 NA 021_2 21 P15_Het_SCN_S1B
## iprgc_96 NA 021_2 19 P15_Het_Ret_S1B
## iprgc_97 NA 021_2 6 P15_WT_SCN_S1A
## iprgc_98 NA 021_2 5 P15_WT_dLGN_S1A
## iprgc_99 NA 021_2 4 P15_WT_Ret_S1A
## iprgc_100 NA 021_2 17 P15_KO_dLGN_S1A
## iprgc_101 NA 021_2 16 P15_KO_Ret_S1A
## iprgc_102 NA 021_2 18 P15_KO_SCN_S1A
## iprgc_104 NA 021_3 13b P8_Het_dLGN_S2
## iprgc_105 NA 021_3 14b P8_Het_dLGN_S3
## iprgc_106 NA 021_3 15b P15_Het_dLGN_S2
## iprgc_107 NA 021_3 16 P8_KO_dLGN_S2
## iprgc_108 NA 021_3 18 P15_KO_dLGN_S2
## iprgc_109 NA 021_3 1b P8_WT_Ret_S2
## iprgc_110 NA 021_3 22b P8_Het_SCN_S2
## iprgc_111 NA 021_3 23b P8_Het_SCN_S3
## iprgc_112 NA 021_3 24b P15_Het_SCN_S2
## iprgc_113 NA 021_3 25 P8_KO_SCN_S2
## iprgc_114 NA 021_3 27 P15_KO_SCN_S2
## iprgc_115 NA 021_3 2b P8_WT_Ret_S3
## iprgc_116 NA 021_3 3b P15_WT_Ret_S3
## iprgc_117 NA 021_3 4b P8_Het_Ret_S2
## iprgc_118 NA 021_3 5 P8_Het_Ret_S3
## iprgc_119 NA 021_3 6b P15_Het_Ret_S2
## iprgc_120 NA 021_3 7 P8_KO_Ret_S2
## iprgc_121 NA 021_3 8 P8_KO_Ret_S3
## iprgc_122 NA 021_3 9 P15_KO_Ret_S2
## iprgc_123 NA 021_3 17 P8_KO_dLGN_S3
## iprgc_124 NA 021_3 26 P8_KO_SCN_S3
## iprgc_125 NA 021_3 10c P8_WT_dLGN_S2
## iprgc_126 NA 021_3 11c P8_WT_dLGN_S3
## iprgc_127 NA 021_3 12b P15_WT_dLGN_S3
## iprgc_128 NA 021_3 19c P8_WT_SCN_S2
## iprgc_129 NA 021_3 20c P8_WT_SCN_S3
## iprgc_130 NA 021_3 21b P15_WT_SCN_S3
## libraryprepcleanupah sequencingrunah
## iprgc_62 successful 220517_VL00136_33_AAC23JWM5
## iprgc_63 successful 220517_VL00136_33_AAC23JWM5
## iprgc_64 successful 220517_VL00136_33_AAC23JWM5
## iprgc_65 successful 220517_VL00136_33_AAC23JWM5
## iprgc_66 successful 220517_VL00136_33_AAC23JWM5
## iprgc_67 successful 220517_VL00136_33_AAC23JWM5
## iprgc_68 successful 220517_VL00136_33_AAC23JWM5
## iprgc_69 successful 220517_VL00136_33_AAC23JWM5
## iprgc_70 successful 220517_VL00136_33_AAC23JWM5
## iprgc_71 successful 220517_VL00136_33_AAC23JWM5
## iprgc_72 successful 220517_VL00136_33_AAC23JWM5
## iprgc_73 successful 220517_VL00136_33_AAC23JWM5
## iprgc_74 successful 220517_VL00136_33_AAC23JWM5
## iprgc_75 successful 220517_VL00136_33_AAC23JWM5
## iprgc_76 successful 220517_VL00136_33_AAC23JWM5
## iprgc_77 successful 220517_VL00136_33_AAC23JWM5
## iprgc_81 successful 220517_VL00136_33_AAC23JWM5
## iprgc_82 successful 220517_VL00136_33_AAC23JWM5
## iprgc_83 successful 220517_VL00136_33_AAC23JWM5
## iprgc_84 successful 220517_VL00136_33_AAC23JWM5
## iprgc_85 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_86 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_87 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_88 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_89 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_90 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_91 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_92 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_93 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_94 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_95 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_96 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_97 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_98 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_99 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_100 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_101 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_102 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_104 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_105 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_106 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_107 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_108 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_109 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_110 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_111 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_112 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_113 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_114 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_115 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_116 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_117 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_118 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_119 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_120 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_121 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_122 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_123 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_124 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_125 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_126 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_127 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_128 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_129 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_130 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## read1ah
## iprgc_62 P15_Het_dLGN_S1A_S16_R1_001.fastq.gz
## iprgc_63 P15_Het_dLGN_S2A_S17_R1_001.fastq.gz
## iprgc_64 P15_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_65 P15_Het_Ret_S2A_S8_R1_001.fastq.gz
## iprgc_66 P15_Het_SCN_S1A_S21_R1_001.fastq.gz
## iprgc_67 P15_Het_SCN_S2A_S22_R1_001.fastq.gz
## iprgc_68 P15_KO_dLGN_S1A_S18_R1_001.fastq.gz
## iprgc_69 P15_KO_Ret_S1A_S9_R1_001.fastq.gz
## iprgc_70 P15_KO_SCN_S1A_S23_R1_001.fastq.gz
## iprgc_71 P15_WT_dLGN_S1A_S12_R1_001.fastq.gz
## iprgc_72 P15_WT_dLGN_S2A_S13_R1_001.fastq.gz
## iprgc_73 P15_WT_dLGN_S3A_S14_R1_001.fastq.gz
## iprgc_74 P15_WT_Ret_S1A_S3_R1_001.fastq.gz
## iprgc_75 P15_WT_Ret_S2A_S4_R1_001.fastq.gz
## iprgc_76 P15_WT_Ret_S3A_S5_R1_001.fastq.gz
## iprgc_77 P15_WT_SCN_S3A_S19_R1_001.fastq.gz
## iprgc_81 P8_WT_dLGN_S1A_S10_R1_001.fastq.gz
## iprgc_82 P8_WT_dLGN_S2A_S11_R1_001.fastq.gz
## iprgc_83 P8_WT_Ret_S1A_S1_R1_001.fastq.gz
## iprgc_84 P8_WT_Ret_S2A_S2_R1_001.fastq.gz
## iprgc_85 P8_KO_SCN_S1A_S12_L001_R1_001.fastq.gz
## iprgc_86 P8_KO_Ret_S1A_S10_L001_R1_001.fastq.gz
## iprgc_87 P8_KO_dLGN_S1A_S11_L001_R1_001.fastq.gz
## iprgc_88 P8_Het_dLGN_S1A_S8_L001_R1_001.fastq.gz
## iprgc_89 P8_Het_SCN_S1A_S9_L001_R1_001.fastq.gz
## iprgc_90 P8_Het_Ret_S1A_S7_L001_R1_001.fastq.gz
## iprgc_91 P8_WT_Ret_S1A_S1_L001_R1_001.fastq.gz
## iprgc_92 P8_WT_dLGN_S1A_S2_L001_R1_001.fastq.gz
## iprgc_93 P8_WT_SCN_S1A_S3_L001_R1_001.fastq.gz
## iprgc_94 P15_Het_dLGN_S1B_S17_L001_R1_001.fastq.gz
## iprgc_95 P15_Het_SCN_S1B_S18_L001_R1_001.fastq.gz
## iprgc_96 P15_Het_Ret_S1B_S16_L001_R1_001.fastq.gz
## iprgc_97 P15_WT_SCN_S1A_S6_L001_R1_001.fastq.gz
## iprgc_98 P15_WT_dLGN_S1A_S5_L001_R1_001.fastq.gz
## iprgc_99 P15_WT_Ret_S1A_S4_L001_R1_001.fastq.gz
## iprgc_100 P15_KO_dLGN_S1A_S14_L001_R1_001.fastq.gz
## iprgc_101 P15_KO_Ret_S1A_S13_L001_R1_001.fastq.gz
## iprgc_102 P15_KO_SCN_S1A_S15_L001_R1_001.fastq.gz
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R1_001.fastq.gz
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R1_001.fastq.gz
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R1_001.fastq.gz
## iprgc_107 16_P8_KO_dLGN_S2_S9_R1_001.fastq.gz
## iprgc_108 18_P15_KO_dLGN_S2_S10_R1_001.fastq.gz
## iprgc_109 1b_P8_WT_Ret_S2_S16_R1_001.fastq.gz
## iprgc_110 22b_P8_Het_SCN_S2_S11_R1_001.fastq.gz
## iprgc_111 23b_P8_Het_SCN_S3_S12_R1_001.fastq.gz
## iprgc_112 24b_P15_Het_SCN_S2_S13_R1_001.fastq.gz
## iprgc_113 25_P8_KO_SCN_S2_S14_R1_001.fastq.gz
## iprgc_114 27_P15_KO_SCN_S2_S15_R1_001.fastq.gz
## iprgc_115 2b_P8_WT_Ret_S3_S17_R1_001.fastq.gz
## iprgc_116 3b_P15_WT_Ret_S3_S18_R1_001.fastq.gz
## iprgc_117 4b_P8_Het_Ret_S2_S1_R1_001.fastq.gz
## iprgc_118 5_P8_Het_Ret_S3_S2_R1_001.fastq.gz
## iprgc_119 6b_P15_Het_Ret_S2_S3_R1_001.fastq.gz
## iprgc_120 7_P8_KO_Ret_S2_S4_R1_001.fastq.gz
## iprgc_121 8_P8_KO_Ret_S3_S19_R1_001.fastq.gz
## iprgc_122 9_P15_KO_Ret_S2_S5_R1_001.fastq.gz
## iprgc_123 17_P8_KO_dLGN_S3_S3_R1_001.fastq.gz
## iprgc_124 26_P8_KO_SCN_S3_S7_R1_001.fastq.gz
## iprgc_125 10c_P8_WT_dLGN_S2_S20_R1_001.fastq.gz
## iprgc_126 11c_P8_WT_dLGN_S3_S1_R1_001.fastq.gz
## iprgc_127 12b_P15_WT_dLGN_S3_S2_R1_001.fastq.gz
## iprgc_128 19c_P8_WT_SCN_S2_S4_R1_001.fastq.gz
## iprgc_129 20c_P8_WT_SCN_S3_S5_R1_001.fastq.gz
## iprgc_130 21b_P15_WT_SCN_S3_S6_R1_001.fastq.gz
## read2ah downloadmapagain
## iprgc_62 P15_Het_dLGN_S1A_S16_R2_001.fastq.gz <NA>
## iprgc_63 P15_Het_dLGN_S2A_S17_R2_001.fastq.gz <NA>
## iprgc_64 P15_Het_Ret_S1A_S7_R2_001.fastq.gz <NA>
## iprgc_65 P15_Het_Ret_S2A_S8_R2_001.fastq.gz <NA>
## iprgc_66 P15_Het_SCN_S1A_S21_R2_001.fastq.gz <NA>
## iprgc_67 P15_Het_SCN_S2A_S22_R2_001.fastq.gz <NA>
## iprgc_68 P15_KO_dLGN_S1A_S18_R2_001.fastq.gz <NA>
## iprgc_69 P15_KO_Ret_S1A_S9_R2_001.fastq.gz <NA>
## iprgc_70 P15_KO_SCN_S1A_S23_R2_001.fastq.gz <NA>
## iprgc_71 P15_WT_dLGN_S1A_S12_R2_001.fastq.gz <NA>
## iprgc_72 P15_WT_dLGN_S2A_S13_R2_001.fastq.gz <NA>
## iprgc_73 P15_WT_dLGN_S3A_S14_R2_001.fastq.gz <NA>
## iprgc_74 P15_WT_Ret_S1A_S3_R2_001.fastq.gz <NA>
## iprgc_75 P15_WT_Ret_S2A_S4_R2_001.fastq.gz <NA>
## iprgc_76 P15_WT_Ret_S3A_S5_R2_001.fastq.gz <NA>
## iprgc_77 P15_WT_SCN_S3A_S19_R2_001.fastq.gz <NA>
## iprgc_81 P8_WT_dLGN_S1A_S10_R2_001.fastq.gz <NA>
## iprgc_82 P8_WT_dLGN_S2A_S11_R2_001.fastq.gz <NA>
## iprgc_83 P8_WT_Ret_S1A_S1_R2_001.fastq.gz <NA>
## iprgc_84 P8_WT_Ret_S2A_S2_R2_001.fastq.gz <NA>
## iprgc_85 P8_KO_SCN_S1A_S12_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_86 P8_KO_Ret_S1A_S10_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_87 P8_KO_dLGN_S1A_S11_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_88 P8_Het_dLGN_S1A_S8_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_89 P8_Het_SCN_S1A_S9_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_90 P8_Het_Ret_S1A_S7_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_91 P8_WT_Ret_S1A_S1_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_92 P8_WT_dLGN_S1A_S2_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_93 P8_WT_SCN_S1A_S3_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_94 P15_Het_dLGN_S1B_S17_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_95 P15_Het_SCN_S1B_S18_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_96 P15_Het_Ret_S1B_S16_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_97 P15_WT_SCN_S1A_S6_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_98 P15_WT_dLGN_S1A_S5_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_99 P15_WT_Ret_S1A_S4_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_100 P15_KO_dLGN_S1A_S14_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_101 P15_KO_Ret_S1A_S13_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_102 P15_KO_SCN_S1A_S15_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R2_001.fastq.gz <NA>
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R2_001.fastq.gz <NA>
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R2_001.fastq.gz <NA>
## iprgc_107 16_P8_KO_dLGN_S2_S9_R2_001.fastq.gz <NA>
## iprgc_108 18_P15_KO_dLGN_S2_S10_R2_001.fastq.gz <NA>
## iprgc_109 1b_P8_WT_Ret_S2_S16_R2_001.fastq.gz <NA>
## iprgc_110 22b_P8_Het_SCN_S2_S11_R2_001.fastq.gz <NA>
## iprgc_111 23b_P8_Het_SCN_S3_S12_R2_001.fastq.gz <NA>
## iprgc_112 24b_P15_Het_SCN_S2_S13_R2_001.fastq.gz <NA>
## iprgc_113 25_P8_KO_SCN_S2_S14_R2_001.fastq.gz <NA>
## iprgc_114 27_P15_KO_SCN_S2_S15_R2_001.fastq.gz <NA>
## iprgc_115 2b_P8_WT_Ret_S3_S17_R2_001.fastq.gz <NA>
## iprgc_116 3b_P15_WT_Ret_S3_S18_R2_001.fastq.gz <NA>
## iprgc_117 4b_P8_Het_Ret_S2_S1_R2_001.fastq.gz <NA>
## iprgc_118 5_P8_Het_Ret_S3_S2_R2_001.fastq.gz <NA>
## iprgc_119 6b_P15_Het_Ret_S2_S3_R2_001.fastq.gz <NA>
## iprgc_120 7_P8_KO_Ret_S2_S4_R2_001.fastq.gz <NA>
## iprgc_121 8_P8_KO_Ret_S3_S19_R2_001.fastq.gz <NA>
## iprgc_122 9_P15_KO_Ret_S2_S5_R2_001.fastq.gz <NA>
## iprgc_123 17_P8_KO_dLGN_S3_S3_R2_001.fastq.gz <NA>
## iprgc_124 26_P8_KO_SCN_S3_S7_R2_001.fastq.gz <NA>
## iprgc_125 10c_P8_WT_dLGN_S2_S20_R2_001.fastq.gz yes, same file renamed
## iprgc_126 11c_P8_WT_dLGN_S3_S1_R2_001.fastq.gz yes, same file renamed
## iprgc_127 12b_P15_WT_dLGN_S3_S2_R2_001.fastq.gz <NA>
## iprgc_128 19c_P8_WT_SCN_S2_S4_R2_001.fastq.gz yes, same file renamed
## iprgc_129 20c_P8_WT_SCN_S3_S5_R2_001.fastq.gz yes, same file renamed
## iprgc_130 21b_P15_WT_SCN_S3_S6_R2_001.fastq.gz yes, same file renamed
## genotype_atb location_atb time_atb geno_loc_atb
## iprgc_62 het dlgn p15 het_dlgn
## iprgc_63 het dlgn p15 het_dlgn
## iprgc_64 het retina p15 het_retina
## iprgc_65 het retina p15 het_retina
## iprgc_66 het scn p15 het_scn
## iprgc_67 het scn p15 het_scn
## iprgc_68 ko dlgn p15 ko_dlgn
## iprgc_69 ko retina p15 ko_retina
## iprgc_70 ko scn p15 ko_scn
## iprgc_71 wt dlgn p15 wt_dlgn
## iprgc_72 wt dlgn p15 wt_dlgn
## iprgc_73 wt dlgn p15 wt_dlgn
## iprgc_74 wt retina p15 wt_retina
## iprgc_75 wt retina p15 wt_retina
## iprgc_76 wt retina p15 wt_retina
## iprgc_77 wt scn p15 wt_scn
## iprgc_81 wt dlgn p08 wt_dlgn
## iprgc_82 wt dlgn p08 wt_dlgn
## iprgc_83 wt retina p08 wt_retina
## iprgc_84 wt retina p08 wt_retina
## iprgc_85 ko scn p08 ko_scn
## iprgc_86 ko retina p08 ko_retina
## iprgc_87 ko dlgn p08 ko_dlgn
## iprgc_88 het dlgn p08 het_dlgn
## iprgc_89 het scn p08 het_scn
## iprgc_90 het retina p08 het_retina
## iprgc_91 wt retina p08 wt_retina
## iprgc_92 wt dlgn p08 wt_dlgn
## iprgc_93 wt scn p08 wt_scn
## iprgc_94 het dlgn p15 het_dlgn
## iprgc_95 het scn p15 het_scn
## iprgc_96 het retina p15 het_retina
## iprgc_97 wt scn p15 wt_scn
## iprgc_98 wt dlgn p15 wt_dlgn
## iprgc_99 wt retina p15 wt_retina
## iprgc_100 ko dlgn p15 ko_dlgn
## iprgc_101 ko retina p15 ko_retina
## iprgc_102 ko scn p15 ko_scn
## iprgc_104 het dlgn p08 het_dlgn
## iprgc_105 het dlgn p08 het_dlgn
## iprgc_106 het dlgn p15 het_dlgn
## iprgc_107 ko dlgn p08 ko_dlgn
## iprgc_108 ko dlgn p15 ko_dlgn
## iprgc_109 wt retina p08 wt_retina
## iprgc_110 het scn p08 het_scn
## iprgc_111 het scn p08 het_scn
## iprgc_112 het scn p15 het_scn
## iprgc_113 ko scn p08 ko_scn
## iprgc_114 ko scn p15 ko_scn
## iprgc_115 wt retina p08 wt_retina
## iprgc_116 wt retina p15 wt_retina
## iprgc_117 het retina p08 het_retina
## iprgc_118 het retina p08 het_retina
## iprgc_119 het retina p15 het_retina
## iprgc_120 ko retina p08 ko_retina
## iprgc_121 ko retina p08 ko_retina
## iprgc_122 ko retina p15 ko_retina
## iprgc_123 ko dlgn p08 ko_dlgn
## iprgc_124 ko scn p08 ko_scn
## iprgc_125 wt dlgn p08 wt_dlgn
## iprgc_126 wt dlgn p08 wt_dlgn
## iprgc_127 wt dlgn p15 wt_dlgn
## iprgc_128 wt scn p08 wt_scn
## iprgc_129 wt scn p08 wt_scn
## iprgc_130 wt scn p15 wt_scn
## time_geno_location_source_atb rashmi_code atb_observed_location
## iprgc_62 AH filename S1A dlgn
## iprgc_63 AH filename S2A dlgn
## iprgc_64 AH filename S1A retina
## iprgc_65 AH filename S2A retina
## iprgc_66 AH filename S1A dlgn
## iprgc_67 AH filename S2A scn
## iprgc_68 AH filename S1A dlgn
## iprgc_69 AH filename S1A retina
## iprgc_70 AH filename S1A scn
## iprgc_71 AH filename S1A dlgn
## iprgc_72 AH filename S2A dlgn
## iprgc_73 AH filename S3A dlgn
## iprgc_74 AH filename S1A retina
## iprgc_75 AH filename S2A retina
## iprgc_76 AH filename S3A retina
## iprgc_77 AH filename S3A scn
## iprgc_81 AH filename S1A dlgn
## iprgc_82 AH filename S2A dlgn
## iprgc_83 AH filename S1A retina
## iprgc_84 AH filename S2A retina
## iprgc_85 AH filename S1A scn
## iprgc_86 AH filename S1A retina
## iprgc_87 AH filename S1A dlgn
## iprgc_88 AH filename S1A dlgn
## iprgc_89 AH filename S1A scn
## iprgc_90 AH filename S1A retina
## iprgc_91 AH filename S1A retina
## iprgc_92 AH filename S1A dlgn
## iprgc_93 AH filename S1A scn
## iprgc_94 AH filename S1B dlgn
## iprgc_95 AH filename S1B scn
## iprgc_96 AH filename S1B retina
## iprgc_97 AH filename S1A scn
## iprgc_98 AH filename S1A dlgn
## iprgc_99 AH filename S1A retina
## iprgc_100 AH filename S1A dlgn
## iprgc_101 AH filename S1A retina
## iprgc_102 AH filename S1A scn
## iprgc_104 AH filename S2 dlgn
## iprgc_105 AH filename S3 dlgn
## iprgc_106 AH filename S2 dlgn
## iprgc_107 AH filename S2 dlgn
## iprgc_108 AH filename S2 dlgn
## iprgc_109 AH filename S2 retina
## iprgc_110 AH filename S2 scn
## iprgc_111 AH filename S3 scn
## iprgc_112 AH filename S2 scn
## iprgc_113 AH filename S2 scn
## iprgc_114 AH filename S2 scn
## iprgc_115 AH filename S3 retina
## iprgc_116 AH filename S3 retina
## iprgc_117 AH filename S2 retina
## iprgc_118 AH filename S3 retina
## iprgc_119 AH filename S2 retina
## iprgc_120 AH filename S2 retina
## iprgc_121 AH filename S3 retina
## iprgc_122 AH filename S2 retina
## iprgc_123 AH filename S3 dlgn
## iprgc_124 AH filename S3 scn
## iprgc_125 AH filename S2 dlgn
## iprgc_126 AH filename S3 dlgn
## iprgc_127 AH filename S3 dlgn
## iprgc_128 AH filename S2 scn
## iprgc_129 AH filename S3 scn
## iprgc_130 AH filename S3 dlgn
## hisat_rrna_input_reads hisat_rrna_single_concordant
## iprgc_62 21454044 1494615
## iprgc_63 15326358 1079397
## iprgc_64 18003256 942929
## iprgc_65 20929623 1195387
## iprgc_66 15724842 1018281
## iprgc_67 15456881 1000025
## iprgc_68 19666257 1628156
## iprgc_69 16552894 218644
## iprgc_70 13740575 700257
## iprgc_71 15525499 1082720
## iprgc_72 17094474 1425296
## iprgc_73 15794837 1285122
## iprgc_74 15775224 790535
## iprgc_75 16328183 877539
## iprgc_76 24180028 1385985
## iprgc_77 17538867 1050779
## iprgc_81 16532098 940095
## iprgc_82 15878311 796807
## iprgc_83 16082507 868003
## iprgc_84 15958920 1050174
## iprgc_85 23680086 1148981
## iprgc_86 20389649 361882
## iprgc_87 27526146 540391
## iprgc_88 29895877 1190844
## iprgc_89 23361256 920771
## iprgc_90 31543306 435071
## iprgc_91 15758794 209248
## iprgc_92 22313362 1157698
## iprgc_93 20934807 1036076
## iprgc_94 28090789 532208
## iprgc_95 23462476 1217287
## iprgc_96 14804663 466300
## iprgc_97 22863130 904924
## iprgc_98 19915507 557667
## iprgc_99 14724866 175966
## iprgc_100 32782165 542666
## iprgc_101 15390215 203176
## iprgc_102 28578632 857116
## iprgc_104 30192805 1038732
## iprgc_105 24528770 523240
## iprgc_106 30802656 390802
## iprgc_107 42462593 642551
## iprgc_108 28855525 290520
## iprgc_109 21695810 338445
## iprgc_110 22927463 1066764
## iprgc_111 18467297 412625
## iprgc_112 19036600 728033
## iprgc_113 27601259 1056238
## iprgc_114 18638104 583967
## iprgc_115 17237384 375309
## iprgc_116 17447459 353770
## iprgc_117 17248353 341716
## iprgc_118 21315056 288212
## iprgc_119 14188002 228078
## iprgc_120 31820880 472691
## iprgc_121 27191863 569880
## iprgc_122 19422096 531934
## iprgc_123 40849547 1075990
## iprgc_124 36378912 1062383
## iprgc_125 16749234 416587
## iprgc_126 22769305 832691
## iprgc_127 23091824 1023687
## iprgc_128 18824955 582920
## iprgc_129 22427478 844116
## iprgc_130 22042940 1111589
## hisat_rrna_multi_concordant hisat_rrna_percent_log
## iprgc_62 170903 9.87
## iprgc_63 182361 10.21
## iprgc_64 72737 7.10
## iprgc_65 58167 7.67
## iprgc_66 87419 8.54
## iprgc_67 108729 8.15
## iprgc_68 207235 12.00
## iprgc_69 9877 1.70
## iprgc_70 63502 7.06
## iprgc_71 152436 8.98
## iprgc_72 246982 11.13
## iprgc_73 112863 10.09
## iprgc_74 165241 6.98
## iprgc_75 96091 7.52
## iprgc_76 199842 7.78
## iprgc_77 62228 8.34
## iprgc_81 77689 7.16
## iprgc_82 135429 6.84
## iprgc_83 80352 6.40
## iprgc_84 123392 8.46
## iprgc_85 28569 5.28
## iprgc_86 13164 2.05
## iprgc_87 18367 2.34
## iprgc_88 61380 4.59
## iprgc_89 54031 4.54
## iprgc_90 13601 1.60
## iprgc_91 7450 1.52
## iprgc_92 20834 5.61
## iprgc_93 29031 5.39
## iprgc_94 16914 2.29
## iprgc_95 22019 5.58
## iprgc_96 5493 3.70
## iprgc_97 8510 4.24
## iprgc_98 25539 3.24
## iprgc_99 6598 1.34
## iprgc_100 13043 1.94
## iprgc_101 3309 1.47
## iprgc_102 11792 3.22
## iprgc_104 64300 3.96
## iprgc_105 27174 2.46
## iprgc_106 12427 1.45
## iprgc_107 30617 1.83
## iprgc_108 16803 1.22
## iprgc_109 32087 1.92
## iprgc_110 20754 5.02
## iprgc_111 8062 2.43
## iprgc_112 7478 4.01
## iprgc_113 62800 4.36
## iprgc_114 11141 3.34
## iprgc_115 30362 2.60
## iprgc_116 17307 2.28
## iprgc_117 10292 2.18
## iprgc_118 7457 1.51
## iprgc_119 6462 1.75
## iprgc_120 25071 1.74
## iprgc_121 48212 2.58
## iprgc_122 17048 2.97
## iprgc_123 86504 3.33
## iprgc_124 41027 3.35
## iprgc_125 21674 2.79
## iprgc_126 50917 4.12
## iprgc_127 38479 4.85
## iprgc_128 15517 3.37
## iprgc_129 37915 4.18
## iprgc_130 12690 5.26
## hisat_genome_input_reads hisat_genome_single_concordant
## iprgc_62 21454044 6916109
## iprgc_63 15326358 4800437
## iprgc_64 18003256 10070534
## iprgc_65 20929623 11589175
## iprgc_66 15724842 7255738
## iprgc_67 15456881 6852704
## iprgc_68 19666257 6195592
## iprgc_69 16552894 12430776
## iprgc_70 13740575 4854312
## iprgc_71 15525499 6307451
## iprgc_72 17094474 7451652
## iprgc_73 15794837 6837350
## iprgc_74 15775224 10294290
## iprgc_75 16328183 10436993
## iprgc_76 24180028 16183985
## iprgc_77 17538867 5292791
## iprgc_81 16532098 7331009
## iprgc_82 15878311 7230838
## iprgc_83 16082507 10368657
## iprgc_84 15958920 10423523
## iprgc_85 23680086 7062000
## iprgc_86 20389649 9744114
## iprgc_87 27526146 6400394
## iprgc_88 29895877 7820843
## iprgc_89 23361256 9311252
## iprgc_90 31543306 15756309
## iprgc_91 15758794 9961930
## iprgc_92 22313362 8488862
## iprgc_93 20934807 7567968
## iprgc_94 28090789 7057050
## iprgc_95 23462476 7263554
## iprgc_96 14804663 7876687
## iprgc_97 22863130 9533254
## iprgc_98 19915507 9711144
## iprgc_99 14724866 11249606
## iprgc_100 32782165 10087273
## iprgc_101 15390215 9017104
## iprgc_102 28578632 13515552
## iprgc_104 30192805 9350995
## iprgc_105 24528770 10945760
## iprgc_106 30802656 11899403
## iprgc_107 42462593 8808064
## iprgc_108 28855525 11980044
## iprgc_109 21695810 14589076
## iprgc_110 22927463 9892455
## iprgc_111 18467297 12034265
## iprgc_112 19036600 13305746
## iprgc_113 27601259 10183581
## iprgc_114 18638104 12163422
## iprgc_115 17237384 11843723
## iprgc_116 17447459 13256126
## iprgc_117 17248353 11047279
## iprgc_118 21315056 12744260
## iprgc_119 14188002 10134357
## iprgc_120 31820880 15877669
## iprgc_121 27191863 13093932
## iprgc_122 19422096 14110700
## iprgc_123 40849547 8776567
## iprgc_124 36378912 9684862
## iprgc_125 16749234 11928896
## iprgc_126 22769305 12094389
## iprgc_127 23091824 13279963
## iprgc_128 18824955 11728805
## iprgc_129 22427478 12119656
## iprgc_130 22042940 14594293
## hisat_genome_multi_concordant hisat_genome_single_all
## iprgc_62 2449957 2717057
## iprgc_63 1878557 1916047
## iprgc_64 1696641 1839841
## iprgc_65 1959086 1724647
## iprgc_66 1845802 1896292
## iprgc_67 1833302 1796579
## iprgc_68 2199833 2603068
## iprgc_69 1371549 740962
## iprgc_70 1398244 1487798
## iprgc_71 2081493 1848749
## iprgc_72 2373441 1919972
## iprgc_73 2140999 2007277
## iprgc_74 1585605 1563688
## iprgc_75 1752174 1352026
## iprgc_76 2802437 1652868
## iprgc_77 1574651 1485198
## iprgc_81 1957318 1652547
## iprgc_82 1695064 1278480
## iprgc_83 3550991 787207
## iprgc_84 2716482 1016602
## iprgc_85 3070475 4982006
## iprgc_86 1584336 3504856
## iprgc_87 2702903 6643829
## iprgc_88 3571442 6566202
## iprgc_89 2670299 4268199
## iprgc_90 2185011 5130035
## iprgc_91 1031455 1952703
## iprgc_92 2227319 4338434
## iprgc_93 1902893 4066044
## iprgc_94 2253262 6394602
## iprgc_95 2462976 4802887
## iprgc_96 1124034 2018582
## iprgc_97 2106698 4168566
## iprgc_98 1646039 2939674
## iprgc_99 899864 1039173
## iprgc_100 2458941 7583206
## iprgc_101 1046189 2151316
## iprgc_102 2414831 4844907
## iprgc_104 3576922 5498302
## iprgc_105 2247030 3714870
## iprgc_106 1745274 5678135
## iprgc_107 3664704 10968566
## iprgc_108 1538013 5173396
## iprgc_109 1554923 2161895
## iprgc_110 3310736 3360928
## iprgc_111 1363554 1699645
## iprgc_112 1364323 1664098
## iprgc_113 3780807 4894243
## iprgc_114 1218954 2151331
## iprgc_115 1353297 1746660
## iprgc_116 1179429 1301001
## iprgc_117 1171597 2019548
## iprgc_118 1398283 2821889
## iprgc_119 912577 1254331
## iprgc_120 2367026 5029094
## iprgc_121 2162058 4353717
## iprgc_122 1539523 1540853
## iprgc_123 4981110 9526129
## iprgc_124 4238972 7831340
## iprgc_125 1177569 1438407
## iprgc_126 2401918 2989185
## iprgc_127 2143572 2771095
## iprgc_128 1564536 2333679
## iprgc_129 2145404 3144006
## iprgc_130 1763157 2278675
## hisat_genome_multi_all hisat_unmapped hisat_genome_percent_log
## iprgc_62 1394098 19607017 54.30
## iprgc_63 746537 14321178 53.28
## iprgc_64 757797 9447512 73.76
## iprgc_65 757000 11613547 72.26
## iprgc_66 509637 10520805 66.55
## iprgc_67 543814 10918707 64.68
## iprgc_68 1201766 18303482 53.46
## iprgc_69 185889 4062569 87.73
## iprgc_70 534908 12784616 53.48
## iprgc_71 483274 11748901 62.16
## iprgc_72 553598 11797040 65.49
## iprgc_73 407155 10978062 65.25
## iprgc_74 262180 5732790 81.83
## iprgc_75 377197 6140625 81.20
## iprgc_76 466627 7631325 84.22
## iprgc_77 738105 18898549 46.12
## iprgc_81 492616 12118069 63.35
## iprgc_82 289604 12077676 61.97
## iprgc_83 189640 3076315 90.44
## iprgc_84 303614 3896608 87.79
## iprgc_85 1452554 20424824 56.87
## iprgc_86 1495480 12892866 68.38
## iprgc_87 3090288 26937105 51.07
## iprgc_88 1484386 28707206 51.99
## iprgc_89 1227818 16934959 63.75
## iprgc_90 2221900 19522087 69.06
## iprgc_91 744446 6646337 78.91
## iprgc_92 1322029 17243053 61.36
## iprgc_93 1072098 17570892 58.03
## iprgc_94 3295727 27633663 50.81
## iprgc_95 1795153 20635750 56.02
## iprgc_96 819084 8565090 71.07
## iprgc_97 1328912 16588598 63.72
## iprgc_98 1353214 12425164 68.81
## iprgc_99 379443 3528780 88.02
## iprgc_100 3726501 28740151 56.16
## iprgc_101 920696 7309862 76.25
## iprgc_102 1526237 18438092 67.74
## iprgc_104 1246712 27517898 54.43
## iprgc_105 841851 17902417 63.51
## iprgc_106 2197041 26013024 57.77
## iprgc_107 3760329 44980319 47.04
## iprgc_108 2337996 22667018 60.72
## iprgc_109 610868 8110837 81.31
## iprgc_110 646097 15094237 67.08
## iprgc_111 450175 7798052 78.89
## iprgc_112 441038 6168522 83.80
## iprgc_113 1460623 20575232 62.73
## iprgc_114 559164 7287021 80.45
## iprgc_115 388933 5750843 83.32
## iprgc_116 259374 4250563 87.82
## iprgc_117 665641 7098103 79.42
## iprgc_118 1118876 10002775 76.54
## iprgc_119 366327 4418372 84.43
## iprgc_120 2032125 19796333 68.89
## iprgc_121 1619685 17615938 67.61
## iprgc_122 447710 5066253 86.96
## iprgc_123 3579817 40815048 50.04
## iprgc_124 2943013 33816629 53.52
## iprgc_125 302868 5337939 84.07
## iprgc_126 490034 12800087 71.89
## iprgc_127 564939 11635882 74.81
## iprgc_128 454792 8063807 78.58
## iprgc_129 632287 12279337 72.62
## iprgc_130 416083 8386154 80.98
## hisat_observed_genes hisat_sum_genes hisat_observed_mean_exprs
## iprgc_62 14545 5642632 718.8
## iprgc_63 13695 3717242 514.1
## iprgc_64 16459 12978365 899.0
## iprgc_65 16884 14696641 1024.8
## iprgc_66 15391 8272382 695.2
## iprgc_67 15379 7908083 670.4
## iprgc_68 14033 4602849 644.5
## iprgc_69 17008 18774803 1039.0
## iprgc_70 14833 5477804 481.6
## iprgc_71 14953 6549329 646.0
## iprgc_72 15339 7357963 751.0
## iprgc_73 15216 7125338 687.1
## iprgc_74 16166 15075002 914.5
## iprgc_75 16452 14804383 928.4
## iprgc_76 17086 22571664 1450.8
## iprgc_77 15168 5038166 524.6
## iprgc_81 14728 8652212 720.5
## iprgc_82 15629 9606265 685.1
## iprgc_83 15724 13230777 1084.3
## iprgc_84 16486 13312390 1003.4
## iprgc_85 15332 6910252 788.7
## iprgc_86 16246 14273411 880.6
## iprgc_87 14025 5647414 710.5
## iprgc_88 15103 7865435 887.4
## iprgc_89 16121 11611851 928.7
## iprgc_90 16393 23919491 1397.2
## iprgc_91 16378 16016160 849.8
## iprgc_92 15400 10312147 834.4
## iprgc_93 15058 8088854 736.8
## iprgc_94 14136 7103946 726.9
## iprgc_95 15275 7471948 757.4
## iprgc_96 16383 11278102 691.3
## iprgc_97 15520 12312129 908.0
## iprgc_98 15863 14054244 880.6
## iprgc_99 16831 17661585 933.6
## iprgc_100 15389 12256388 980.6
## iprgc_101 16561 12622526 767.6
## iprgc_102 15612 18809254 1247.5
## iprgc_104 15400 11375862 1009.0
## iprgc_105 15795 16846374 1028.5
## iprgc_106 15558 17994083 1067.9
## iprgc_107 14636 8231918 974.0
## iprgc_108 15633 18552934 1057.3
## iprgc_109 16728 24270138 1253.8
## iprgc_110 15568 13509357 1031.5
## iprgc_111 15706 19606417 1047.3
## iprgc_112 16153 20911855 1147.5
## iprgc_113 15965 12217700 1087.8
## iprgc_114 16314 19402528 1044.1
## iprgc_115 16441 19560965 1022.9
## iprgc_116 16929 20964695 1113.9
## iprgc_117 16262 18276778 953.3
## iprgc_118 16503 20885024 1105.3
## iprgc_119 16551 16774668 856.7
## iprgc_120 16564 24538069 1421.8
## iprgc_121 16368 19040231 1186.2
## iprgc_122 16794 21808522 1216.0
## iprgc_123 14501 7015938 1074.6
## iprgc_124 15405 8986063 1086.5
## iprgc_125 15940 19243970 1013.9
## iprgc_126 15122 18681748 1134.2
## iprgc_127 16250 20184962 1204.1
## iprgc_128 15505 18172993 1038.3
## iprgc_129 15339 17955093 1117.1
## iprgc_130 16058 23057879 1277.6
## hisat_observed_median_exprs
## iprgc_62 3
## iprgc_63 1
## iprgc_64 21
## iprgc_65 25
## iprgc_66 7
## iprgc_67 13
## iprgc_68 2
## iprgc_69 35
## iprgc_70 8
## iprgc_71 6
## iprgc_72 7
## iprgc_73 7
## iprgc_74 23
## iprgc_75 24
## iprgc_76 43
## iprgc_77 8
## iprgc_81 9
## iprgc_82 15
## iprgc_83 26
## iprgc_84 22
## iprgc_85 12
## iprgc_86 24
## iprgc_87 2
## iprgc_88 8
## iprgc_89 17
## iprgc_90 33
## iprgc_91 26
## iprgc_92 14
## iprgc_93 13
## iprgc_94 2
## iprgc_95 10
## iprgc_96 21
## iprgc_97 18
## iprgc_98 14
## iprgc_99 35
## iprgc_100 8
## iprgc_101 23
## iprgc_102 28
## iprgc_104 20
## iprgc_105 28
## iprgc_106 13
## iprgc_107 4
## iprgc_108 15
## iprgc_109 41
## iprgc_110 22
## iprgc_111 38
## iprgc_112 33
## iprgc_113 24
## iprgc_114 27
## iprgc_115 34
## iprgc_116 46
## iprgc_117 30
## iprgc_118 38
## iprgc_119 31
## iprgc_120 38
## iprgc_121 29
## iprgc_122 44
## iprgc_123 4
## iprgc_124 12
## iprgc_125 31
## iprgc_126 30
## iprgc_127 34
## iprgc_128 37
## iprgc_129 39
## iprgc_130 37
## hisat_count_table
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_66 preprocessing/umd_sequenced/iprgc_66/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_130 preprocessing/umd_sequenced/iprgc_130/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## umi_extract_r1_output
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_66 preprocessing/umd_sequenced/iprgc_66/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_130 preprocessing/umd_sequenced/iprgc_130/outputs/01umi_tools/r1_extracted.fastq.gz
## umi_extract_matches
## iprgc_62 21454044
## iprgc_63 15326358
## iprgc_64 18003256
## iprgc_65 20929623
## iprgc_66 15724842
## iprgc_67 15456881
## iprgc_68 19666257
## iprgc_69 16552894
## iprgc_70 13740575
## iprgc_71 15525499
## iprgc_72 17094474
## iprgc_73 15794837
## iprgc_74 15775224
## iprgc_75 16328183
## iprgc_76 24180028
## iprgc_77 17538867
## iprgc_81 16532098
## iprgc_82 15878311
## iprgc_83 16082507
## iprgc_84 15958920
## iprgc_85 23680086
## iprgc_86 20389649
## iprgc_87 27526146
## iprgc_88 29895877
## iprgc_89 23361256
## iprgc_90 31543306
## iprgc_91 15758794
## iprgc_92 22313362
## iprgc_93 20934807
## iprgc_94 28090789
## iprgc_95 23462476
## iprgc_96 14804663
## iprgc_97 22863130
## iprgc_98 19915507
## iprgc_99 14724866
## iprgc_100 32782165
## iprgc_101 15390215
## iprgc_102 28578632
## iprgc_104 30192805
## iprgc_105 24528770
## iprgc_106 30802656
## iprgc_107 42462593
## iprgc_108 28855525
## iprgc_109 21695810
## iprgc_110 22927463
## iprgc_111 18467297
## iprgc_112 19036600
## iprgc_113 27601259
## iprgc_114 18638104
## iprgc_115 17237384
## iprgc_116 17447459
## iprgc_117 17248353
## iprgc_118 21315056
## iprgc_119 14188002
## iprgc_120 31820880
## iprgc_121 27191863
## iprgc_122 19422096
## iprgc_123 40849547
## iprgc_124 36378912
## iprgc_125 16749234
## iprgc_126 22769305
## iprgc_127 23091824
## iprgc_128 18824955
## iprgc_129 22427478
## iprgc_130 22042940
## umi_dedup_output_bam
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_66 preprocessing/umd_sequenced/iprgc_66/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_130 preprocessing/umd_sequenced/iprgc_130/outputs/04umi_dedup/umi_tools_deduplicated.bam
## umi_dedup_output_count
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_66 preprocessing/umd_sequenced/iprgc_66/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_130 preprocessing/umd_sequenced/iprgc_130/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## umi_dedup_chimeric umi_dedup_num_reads_in umi_dedup_num_reads_out
## iprgc_62 1452937 18614709 8648625
## iprgc_63 709025 13297062 6169628
## iprgc_64 867058 17895655 12438709
## iprgc_65 825134 20305778 14650070
## iprgc_66 0 15209347 7879075
## iprgc_67 0 14911704 6244396
## iprgc_68 1246928 16877415 8001913
## iprgc_69 224846 18312482 13626331
## iprgc_70 0 11291811 4315031
## iprgc_71 0 14899869 6142703
## iprgc_72 0 17508994 8086389
## iprgc_73 0 15705128 7262328
## iprgc_74 444838 16982732 10297731
## iprgc_75 471376 17837101 11666895
## iprgc_76 548725 27435832 17919454
## iprgc_77 0 12622691 5641702
## iprgc_81 0 15696204 4325269
## iprgc_82 0 14420683 5989004
## iprgc_83 278840 20500536 6520845
## iprgc_84 382725 19718919 11961194
## iprgc_85 0 21919740 6297631
## iprgc_86 1662182 19427925 11226412
## iprgc_87 3002303 24375432 6943848
## iprgc_88 0 25825973 7683133
## iprgc_89 1288308 22230551 9988741
## iprgc_90 2506186 29561758 16430666
## iprgc_91 805952 15730918 11449267
## iprgc_92 0 20434181 6517215
## iprgc_93 0 18285032 4763200
## iprgc_94 3231382 23478066 7569580
## iprgc_95 0 20934229 6494203
## iprgc_96 716527 14087698 9401463
## iprgc_97 1230958 20902762 6972570
## iprgc_98 1347661 18716405 10937773
## iprgc_99 428519 15381926 12713186
## iprgc_100 3861642 29132038 10539058
## iprgc_101 946176 15478210 10762654
## iprgc_102 1500337 26398889 7615304
## iprgc_104 0 25659015 5958853
## iprgc_105 0 21634779 7136486
## iprgc_106 2296611 24666207 11852081
## iprgc_107 0 33732409 8324107
## iprgc_108 2555016 23911870 12383587
## iprgc_109 662661 21753591 15616405
## iprgc_110 0 22498630 5560116
## iprgc_111 0 17938608 4975228
## iprgc_112 0 19103684 8418311
## iprgc_113 0 26995038 7519015
## iprgc_114 594219 18353136 11277987
## iprgc_115 490334 17708595 12360074
## iprgc_116 351840 18170428 13514017
## iprgc_117 657983 17093633 12218494
## iprgc_118 1192045 20812806 13909310
## iprgc_119 354617 14328357 10869600
## iprgc_120 2098677 29739491 15991456
## iprgc_121 1844894 25274997 14082779
## iprgc_122 470276 20455761 13355865
## iprgc_123 3524658 35613060 8785532
## iprgc_124 3036013 31992425 7611305
## iprgc_125 367910 17181632 6236874
## iprgc_126 0 22064620 4360402
## iprgc_127 0 22219789 10051459
## iprgc_128 0 18785320 4216887
## iprgc_129 0 21729414 3911603
## iprgc_130 579771 21788444 9135783
## umi_dedup_pct_reads umi_dedup_deduplicated_positions
## iprgc_62 0.465 1753865
## iprgc_63 0.464 1071878
## iprgc_64 0.695 4373152
## iprgc_65 0.721 5419451
## iprgc_66 0.518 2388658
## iprgc_67 0.419 1850476
## iprgc_68 0.474 1436590
## iprgc_69 0.744 6138409
## iprgc_70 0.382 1254243
## iprgc_71 0.412 1571654
## iprgc_72 0.462 2176556
## iprgc_73 0.462 1999845
## iprgc_74 0.606 3551834
## iprgc_75 0.654 4098587
## iprgc_76 0.653 6129190
## iprgc_77 0.447 1562142
## iprgc_81 0.276 1260702
## iprgc_82 0.415 2056722
## iprgc_83 0.318 2151073
## iprgc_84 0.607 3990543
## iprgc_85 0.287 1633097
## iprgc_86 0.578 4040213
## iprgc_87 0.285 992104
## iprgc_88 0.297 1747176
## iprgc_89 0.449 3055929
## iprgc_90 0.556 5517995
## iprgc_91 0.728 4836261
## iprgc_92 0.319 1974476
## iprgc_93 0.260 1639813
## iprgc_94 0.322 1407175
## iprgc_95 0.310 1633630
## iprgc_96 0.667 4043448
## iprgc_97 0.334 2174736
## iprgc_98 0.584 3780285
## iprgc_99 0.827 5930421
## iprgc_100 0.362 2736945
## iprgc_101 0.695 5000352
## iprgc_102 0.288 2468613
## iprgc_104 0.232 1748672
## iprgc_105 0.330 2684344
## iprgc_106 0.480 3598525
## iprgc_107 0.247 1445521
## iprgc_108 0.518 3874112
## iprgc_109 0.718 5888344
## iprgc_110 0.247 1935173
## iprgc_111 0.277 2273128
## iprgc_112 0.441 3260409
## iprgc_113 0.279 2452306
## iprgc_114 0.614 4137689
## iprgc_115 0.698 4863270
## iprgc_116 0.744 6018124
## iprgc_117 0.715 4461309
## iprgc_118 0.668 4602072
## iprgc_119 0.759 4348791
## iprgc_120 0.538 5227916
## iprgc_121 0.557 4731564
## iprgc_122 0.653 5194944
## iprgc_123 0.247 1224820
## iprgc_124 0.238 1735561
## iprgc_125 0.363 3711886
## iprgc_126 0.198 1740851
## iprgc_127 0.452 3552168
## iprgc_128 0.224 2102741
## iprgc_129 0.180 1607974
## iprgc_130 0.419 3507514
## umi_dedup_mean_umi_per_pos umi_dedup_max_umi_per_pos time_geno_loc
## iprgc_62 8.60 38924 p15_het_dlgn
## iprgc_63 9.86 39440 p15_het_dlgn
## iprgc_64 3.59 27371 p15_het_retina
## iprgc_65 3.34 27862 p15_het_retina
## iprgc_66 4.95 37595 p15_het_scn
## iprgc_67 4.54 24406 p15_het_scn
## iprgc_68 9.68 45454 p15_ko_dlgn
## iprgc_69 2.38 15393 p15_ko_retina
## iprgc_70 4.62 20251 p15_ko_scn
## iprgc_71 6.12 36072 p15_wt_dlgn
## iprgc_72 5.93 37992 p15_wt_dlgn
## iprgc_73 5.75 40737 p15_wt_dlgn
## iprgc_74 3.63 25472 p15_wt_retina
## iprgc_75 3.53 28830 p15_wt_retina
## iprgc_76 3.62 38280 p15_wt_retina
## iprgc_77 5.18 23257 p15_wt_scn
## iprgc_81 4.63 22526 p08_wt_dlgn
## iprgc_82 3.71 21315 p08_wt_dlgn
## iprgc_83 3.96 53471 p08_wt_retina
## iprgc_84 3.92 57014 p08_wt_retina
## iprgc_85 5.78 45940 p08_ko_scn
## iprgc_86 3.89 46855 p08_ko_retina
## iprgc_87 16.31 56654 p08_ko_dlgn
## iprgc_88 7.90 55449 p08_het_dlgn
## iprgc_89 4.95 50112 p08_het_scn
## iprgc_90 4.15 55133 p08_het_retina
## iprgc_91 2.83 40894 p08_wt_retina
## iprgc_92 4.63 45886 p08_wt_dlgn
## iprgc_93 3.80 36240 p08_wt_scn
## iprgc_94 11.52 55625 p15_het_dlgn
## iprgc_95 6.17 47815 p15_het_scn
## iprgc_96 2.94 47871 p15_het_retina
## iprgc_97 4.38 43996 p15_wt_scn
## iprgc_98 3.95 49407 p15_wt_dlgn
## iprgc_99 2.33 24195 p15_wt_retina
## iprgc_100 7.43 58830 p15_ko_dlgn
## iprgc_101 2.66 37636 p15_ko_retina
## iprgc_102 4.06 48224 p15_ko_scn
## iprgc_104 4.96 44516 p08_het_dlgn
## iprgc_105 3.51 47370 p08_het_dlgn
## iprgc_106 4.79 54693 p15_het_dlgn
## iprgc_107 12.65 63093 p08_ko_dlgn
## iprgc_108 4.54 51083 p15_ko_dlgn
## iprgc_109 3.08 40990 p08_wt_retina
## iprgc_110 3.68 38373 p08_het_scn
## iprgc_111 2.50 29514 p08_het_scn
## iprgc_112 2.93 33820 p15_het_scn
## iprgc_113 4.20 48638 p08_ko_scn
## iprgc_114 3.13 40491 p15_ko_scn
## iprgc_115 2.93 37559 p08_wt_retina
## iprgc_116 2.46 30096 p15_wt_retina
## iprgc_117 3.22 44668 p08_het_retina
## iprgc_118 3.69 44346 p08_het_retina
## iprgc_119 2.80 32754 p15_het_retina
## iprgc_120 4.14 54664 p08_ko_retina
## iprgc_121 4.11 52455 p08_ko_retina
## iprgc_122 2.92 43252 p15_ko_retina
## iprgc_123 16.25 63117 p08_ko_dlgn
## iprgc_124 7.01 56208 p08_ko_scn
## iprgc_125 1.85 20841 p08_wt_dlgn
## iprgc_126 3.13 24582 p08_wt_dlgn
## iprgc_127 3.52 36871 p15_wt_dlgn
## iprgc_128 2.35 34708 p08_wt_scn
## iprgc_129 2.91 24561 p08_wt_scn
## iprgc_130 3.07 35515 p15_wt_scn
##
## $factors
## [1] "genotype_atb" "location_atb" "time_atb"
##
## $observed_nodes
## [1] "het" "het dlgn" "het dlgn p08" "het dlgn p15"
## [5] "het retina" "het retina p08" "het retina p15" "het scn"
## [9] "het scn p08" "het scn p15" "ko" "ko dlgn"
## [13] "ko dlgn p08" "ko dlgn p15" "ko retina" "ko retina p08"
## [17] "ko retina p15" "ko scn" "ko scn p08" "ko scn p15"
## [21] "wt" "wt dlgn" "wt dlgn p08" "wt dlgn p15"
## [25] "wt retina" "wt retina p08" "wt retina p15" "wt scn"
## [29] "wt scn p08" "wt scn p15"
##
## $ggplot
##
## attr(,"class")
## [1] "hpgltools::meta_sankey"10 A Short conversation with Rashmi
Rashmi came by and we discussed the samples a little. She suggested that is likely that we will need to exclude the 202205 samples, these may be identified by a few ways, most easily I think via the ‘project_ah’ column, they are the 021_1 samples.
My sense was that she concurred with my interpretation of the umi deduplication, so I will continue using the deduplicated results exclusively, at least for now.
11 Melanopsin Sanity Check
One of Theresa’s first checks was wisely for melanopsin. Let us repeat a version of this:
An important note: Indrajeet Patil removed the groupedstats and its associated plotting library from CRAN/github/etc. I am not certain what happened, but that necessitates a change in how I plot this.
opn4_exprs <- data.frame(combined = colData(loc_geno_nt)[["geno_loc_atb"]],
location = colData(loc_geno_nt)[["location_atb"]],
genotype = colData(loc_geno_nt)[["genotype_atb"]],
opn = assay(loc_geno_nt)["ENSMUSG00000021799", ])
## groupedstats::grouped_summary(opn4_exprs, location, opn)
## opn4_location <- ggbetweenstats(data = opn4_exprs, x = location, y = opn)
## pp(file = "images/ggbetween_location.pdf")
## opn4_location
## plotted <- dev.off()
## opn4_location
## opn4_genotype <- ggbetweenstats(data = opn4_exprs, x = genotype, y = opn)
## pp(file = "images/ggbetween_location.pdf")
## opn4_genotype
## plotted <- dev.off()
## opn4_genotype
## opn4_combined <- ggbetweenstats(data = opn4_exprs, x = combined, y = opn)
## pp(file = "images/ggbetween_combined.pdf")
## opn4_combined
## plotted <- dev.off()
## opn4_combinedok, so I plotted the question a bit differently, but got the same answer.
Here is the text of Theresa’s notebook following this analysis:
“Ugh oh, looks like there is at least one retina KO sample that has some melanopsin expression in it. Turns out ipRGC_07 is a bad egg which is supposed to be a KO but has melanopsin expression. It’s friends which were pooled from the same mice are iprgc_06 and iprgc_08, so we need to exclude all these samples.”
I am also seeing some knockout expression with some caveats: I do not have the affected samples in my dataset (iprgc_07) and the levels I am seeing are quite low – I will look in IGV to double check, but I strongly suspect that these are some piddly reads near the UTRs.
Onward!
12 PCA plots
12.1 PCA of all genes by location
Theresa’s first pca was of log2 cpm values. I might add quantile/tmm to this?
v3_location <- set_conditions(mm38_hisat_v3, fact = "location_atb") %>%
set_batches(fact = "genotype_atb") %>%
set_colors(color_choices[["location"]])## The numbers of samples by condition are:##
## dlgn retina scn
## 23 23 19## The number of samples by batch are:##
## het ko wt
## 21 18 26v3_location_norm <- normalize(v3_location, filter = TRUE, norm = "quant",
transform = "log2", convert = "cpm")## Removing 10156 low-count genes (15269 remaining).## transform_counts: Found 9347 values equal to 0, adding 1 to the matrix.v3_location_pca <- plot_pca(v3_location_norm)
pp(file = "01diagnostic_images/v3_location_norm_pca.pdf")
v3_location_pca## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by dlgn, retina, scn
## Shapes are defined by het, ko, wt.## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by dlgn, retina, scn
## Shapes are defined by het, ko, wt.
Once again we see that samples iprgc_66 and iprgc_130 are likely actually DLGN and not SCN. I am therefore going to add a column to the sample sheet noting this, and remove them from the expressionset.
I will thus replot the data after removing those two. If we want to see what it looks like with the re-attributed locations, we can do so.
Theresa has a nice change to the PCA plotter in which she sets the alpha channel as an additional visual queue for a metadata factor…
mm38_hisat_v3 <- subset_se(mm38_hisat_v3, subset="sampleid!='iprgc_130'") %>%
subset_se(subset="sampleid!='iprgc_66'")
v3_location <- set_conditions(mm38_hisat_v3, fact = "location_atb") %>%
set_batches(fact = "genotype_atb") %>%
set_colors(color_choices[["location"]])## The numbers of samples by condition are:##
## dlgn retina scn
## 23 23 17## The number of samples by batch are:##
## het ko wt
## 20 18 25v3_location_norm <- normalize(v3_location, filter = TRUE, norm = "quant",
transform = "log2", convert = "cpm")## Removing 10162 low-count genes (15263 remaining).## transform_counts: Found 8867 values equal to 0, adding 1 to the matrix.filtered_location_pca <- plot_pca(v3_location_norm)
pp(file = "02filtered_images/filtered_location_pca.pdf")## Warning in pp(file = "02filtered_images/filtered_location_pca.pdf"): The
## directory: 02filtered_images does not exist, will attempt to create it.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by dlgn, retina, scn
## Shapes are defined by het, ko, wt.
removed_sankey <- plot_meta_sankey(v3_location, color_choices = color_choices,
factors = c("genotype_atb", "location_atb", "time_atb"))## Warning: attributes are not identical across measure variables; they will be
## droppedpp(file = "02filtered_images/filtered_sankey.pdf")
removed_sankey[["ggplot"]]
plotted <- dev.off()
removed_sankey## $design
## rownames sampleid genotype location time condition cellcompartment
## iprgc_62 iprgc_62 iprgc_62 het dlgn p15 dlgn axon
## iprgc_63 iprgc_63 iprgc_63 het dlgn p15 dlgn axon
## iprgc_64 iprgc_64 iprgc_64 het retina p15 retina soma
## iprgc_65 iprgc_65 iprgc_65 het retina p15 retina soma
## iprgc_67 iprgc_67 iprgc_67 het scn p15 scn axon
## iprgc_68 iprgc_68 iprgc_68 ko dlgn p15 dlgn axon
## iprgc_69 iprgc_69 iprgc_69 ko retina p15 retina soma
## iprgc_70 iprgc_70 iprgc_70 ko scn p15 scn axon
## iprgc_71 iprgc_71 iprgc_71 wt dlgn p15 dlgn axon
## iprgc_72 iprgc_72 iprgc_72 wt dlgn p15 dlgn axon
## iprgc_73 iprgc_73 iprgc_73 wt dlgn p15 dlgn axon
## iprgc_74 iprgc_74 iprgc_74 wt retina p15 retina soma
## iprgc_75 iprgc_75 iprgc_75 wt retina p15 retina soma
## iprgc_76 iprgc_76 iprgc_76 wt retina p15 retina soma
## iprgc_77 iprgc_77 iprgc_77 wt scn p15 scn axon
## iprgc_81 iprgc_81 iprgc_81 wt dlgn p08 dlgn axon
## iprgc_82 iprgc_82 iprgc_82 wt dlgn p08 dlgn axon
## iprgc_83 iprgc_83 iprgc_83 wt retina p08 retina soma
## iprgc_84 iprgc_84 iprgc_84 wt retina p08 retina soma
## iprgc_85 iprgc_85 iprgc_85 het dlgn p15 scn axon
## iprgc_86 iprgc_86 iprgc_86 ko dlgn p15 retina soma
## iprgc_87 iprgc_87 iprgc_87 het retina p15 dlgn axon
## iprgc_88 iprgc_88 iprgc_88 ko dlgn p08 dlgn axon
## iprgc_89 iprgc_89 iprgc_89 ko retina p15 scn axon
## iprgc_90 iprgc_90 iprgc_90 ko retina p08 retina soma
## iprgc_91 iprgc_91 iprgc_91 wt retina p08 retina soma
## iprgc_92 iprgc_92 iprgc_92 wt retina p15 dlgn axon
## iprgc_93 iprgc_93 iprgc_93 wt dlgn p15 scn axon
## iprgc_94 iprgc_94 iprgc_94 ko scn p15 dlgn axon
## iprgc_95 iprgc_95 iprgc_95 het scn p15 scn axon
## iprgc_96 iprgc_96 iprgc_96 ko scn p08 retina soma
## iprgc_97 iprgc_97 iprgc_97 het scn p08 scn axon
## iprgc_98 iprgc_98 iprgc_98 het dlgn p08 dlgn axon
## iprgc_99 iprgc_99 iprgc_99 het retina p08 retina soma
## iprgc_100 iprgc_100 iprgc_100 wt scn p08 dlgn axon
## iprgc_101 iprgc_101 iprgc_101 wt dlgn p08 retina soma
## iprgc_102 iprgc_102 iprgc_102 wt scn p15 scn axon
## iprgc_104 iprgc_104 iprgc_104 het dlgn p08 dlgn axon
## iprgc_105 iprgc_105 iprgc_105 het dlgn p08 dlgn axon
## iprgc_106 iprgc_106 iprgc_106 het dlgn p15 dlgn axon
## iprgc_107 iprgc_107 iprgc_107 ko dlgn p08 dlgn axon
## iprgc_108 iprgc_108 iprgc_108 ko dlgn p15 dlgn axon
## iprgc_109 iprgc_109 iprgc_109 wt retina p08 retina soma
## iprgc_110 iprgc_110 iprgc_110 het scn p08 scn axon
## iprgc_111 iprgc_111 iprgc_111 het scn p08 scn axon
## iprgc_112 iprgc_112 iprgc_112 het scn p15 scn axon
## iprgc_113 iprgc_113 iprgc_113 ko scn p08 scn axon
## iprgc_114 iprgc_114 iprgc_114 ko scn p15 scn axon
## iprgc_115 iprgc_115 iprgc_115 wt retina p08 retina soma
## iprgc_116 iprgc_116 iprgc_116 wt retina p15 retina soma
## iprgc_117 iprgc_117 iprgc_117 het retina p08 retina soma
## iprgc_118 iprgc_118 iprgc_118 het retina p08 retina soma
## iprgc_119 iprgc_119 iprgc_119 het retina p15 retina soma
## iprgc_120 iprgc_120 iprgc_120 ko retina p08 retina soma
## iprgc_121 iprgc_121 iprgc_121 ko retina p08 retina soma
## iprgc_122 iprgc_122 iprgc_122 ko retina p15 retina soma
## iprgc_123 iprgc_123 iprgc_123 <NA> <NA> <NA> dlgn <NA>
## iprgc_124 iprgc_124 iprgc_124 <NA> <NA> <NA> scn <NA>
## iprgc_125 iprgc_125 iprgc_125 <NA> <NA> <NA> dlgn <NA>
## iprgc_126 iprgc_126 iprgc_126 <NA> <NA> <NA> dlgn <NA>
## iprgc_127 iprgc_127 iprgc_127 <NA> <NA> <NA> dlgn <NA>
## iprgc_128 iprgc_128 iprgc_128 <NA> <NA> <NA> scn <NA>
## iprgc_129 iprgc_129 iprgc_129 <NA> <NA> <NA> scn <NA>
## raw_r1
## iprgc_62 P15_Het_dLGN_S1A_S16_R1_001.fastq.gz
## iprgc_63 P15_Het_dLGN_S2A_S17_R1_001.fastq.gz
## iprgc_64 P15_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_65 P15_Het_Ret_S2A_S8_R1_001.fastq.gz
## iprgc_67 P15_Het_SCN_S2A_S22_R1_001.fastq.gz
## iprgc_68 P15_KO_dLGN_S1A_S18_R1_001.fastq.gz
## iprgc_69 P15_KO_Ret_S1A_S9_R1_001.fastq.gz
## iprgc_70 P15_KO_SCN_S1A_S23_R1_001.fastq.gz
## iprgc_71 P15_WT_dLGN_S1A_S12_R1_001.fastq.gz
## iprgc_72 P15_WT_dLGN_S2A_S13_R1_001.fastq.gz
## iprgc_73 P15_WT_dLGN_S3A_S14_R1_001.fastq.gz
## iprgc_74 P15_WT_Ret_S1A_S3_R1_001.fastq.gz
## iprgc_75 P15_WT_Ret_S2A_S4_R1_001.fastq.gz
## iprgc_76 P15_WT_Ret_S3A_S5_R1_001.fastq.gz
## iprgc_77 P15_WT_SCN_S3A_S19_R1_001.fastq.gz
## iprgc_81 P8_WT_dLGN_S1A_S10_R1_001.fastq.gz
## iprgc_82 P8_WT_dLGN_S2A_S11_R1_001.fastq.gz
## iprgc_83 P8_WT_Ret_S1A_S1_R1_001.fastq.gz
## iprgc_84 P8_WT_Ret_S2A_S2_R1_001.fastq.gz
## iprgc_85 P8_KO_SCN_S1A_S12_R1_001.fastq.gz
## iprgc_86 P8_KO_Ret_S1A_S10_R1_001.fastq.gz
## iprgc_87 P8_KO_dLGN_S1A_S11_R1_001.fastq.gz
## iprgc_88 P8_Het_dLGN_S1A_S8_R1_001.fastq.gz
## iprgc_89 P8_Het_SCN_S1A_S9_R1_001.fastq.gz
## iprgc_90 P8_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_91 P8_WT_Ret_S1A_S1_R2_001.fastq.gz
## iprgc_92 P8_WT_dLGN_S1A_S2_R1_001.fastq.gz
## iprgc_93 P8_WT_SCN_S1A_S3_R1_001.fastq.gz
## iprgc_94 P15_Het_dLGN_S1B_S17_R1_001.fastq.gz
## iprgc_95 P15_Het_SCN_S1B_S18_R1_001.fastq.gz
## iprgc_96 P15_Het_Ret_S1B_S16_R1_001.fastq.gz
## iprgc_97 P15_WT_SCN_S1A_S6_R1_001.fastq.gz
## iprgc_98 P15_WT_dLGN_S1A_S5_R1_001.fastq.gz
## iprgc_99 P15_WT_Ret_S1A_S4_R1_001.fastq.gz
## iprgc_100 P15_KO_dLGN_S1A_S14_R1_001.fastq.gz
## iprgc_101 P15_KO_Ret_S1A_S13_R1_001.fastq.gz
## iprgc_102 P15_KO_SCN_S1A_S15_R1_001.fastq.gz
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R1_001.fastq.gz
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R1_001.fastq.gz
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R1_001.fastq.gz
## iprgc_107 16_P8_KO_dLGN_S2_S9_R1_001.fastq.gz
## iprgc_108 18_P15_KO_dLGN_S2_S10_R1_001.fastq.gz
## iprgc_109 1b_P8_WT_Ret_S2_S16_R1_001.fastq.gz
## iprgc_110 22b_P8_Het_SCN_S2_S11_R1_001.fastq.gz
## iprgc_111 23b_P8_Het_SCN_S3_S12_R1_001.fastq.gz
## iprgc_112 24b_P15_Het_SCN_S2_S13_R1_001.fastq.gz
## iprgc_113 25_P8_KO_SCN_S2_S14_R1_001.fastq.gz
## iprgc_114 27_P15_KO_SCN_S2_S15_R1_001.fastq.gz
## iprgc_115 2b_P8_WT_Ret_S3_S17_R1_001.fastq.gz
## iprgc_116 3b_P15_WT_Ret_S3_S18_R1_001.fastq.gz
## iprgc_117 4b_P8_Het_Ret_S2_S1_R1_001.fastq.gz
## iprgc_118 5_P8_Het_Ret_S3_S2_R1_001.fastq.gz
## iprgc_119 6b_P15_Het_Ret_S2_S3_R1_001.fastq.gz
## iprgc_120 7_P8_KO_Ret_S2_S4_R1_001.fastq.gz
## iprgc_121 8_P8_KO_Ret_S3_S19_R1_001.fastq.gz
## iprgc_122 9_P15_KO_Ret_S2_S5_R1_001.fastq.gz
## iprgc_123 <NA>
## iprgc_124 <NA>
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## raw_r2 batch date_received
## iprgc_62 P15_Het_dLGN_S1A_S16_R2_001.fastq.gz het b20220518
## iprgc_63 P15_Het_dLGN_S2A_S17_R2_001.fastq.gz het b20220518
## iprgc_64 P15_Het_Ret_S1A_S7_R2_001.fastq.gz het b20220518
## iprgc_65 P15_Het_Ret_S2A_S8_R2_001.fastq.gz het b20220518
## iprgc_67 P15_Het_SCN_S2A_S22_R2_001.fastq.gz het b20220518
## iprgc_68 P15_KO_dLGN_S1A_S18_R2_001.fastq.gz ko b20220518
## iprgc_69 P15_KO_Ret_S1A_S9_R2_001.fastq.gz ko b20220518
## iprgc_70 P15_KO_SCN_S1A_S23_R2_001.fastq.gz ko b20220518
## iprgc_71 P15_WT_dLGN_S1A_S12_R2_001.fastq.gz wt b20220518
## iprgc_72 P15_WT_dLGN_S2A_S13_R2_001.fastq.gz wt b20220518
## iprgc_73 P15_WT_dLGN_S3A_S14_R2_001.fastq.gz wt b20220518
## iprgc_74 P15_WT_Ret_S1A_S3_R2_001.fastq.gz wt b20220518
## iprgc_75 P15_WT_Ret_S2A_S4_R2_001.fastq.gz wt b20220518
## iprgc_76 P15_WT_Ret_S3A_S5_R2_001.fastq.gz wt b20220518
## iprgc_77 P15_WT_SCN_S3A_S19_R2_001.fastq.gz wt b20220518
## iprgc_81 P8_WT_dLGN_S1A_S10_R2_001.fastq.gz wt b20220518
## iprgc_82 P8_WT_dLGN_S2A_S11_R2_001.fastq.gz wt b20220518
## iprgc_83 P8_WT_Ret_S1A_S1_R2_001.fastq.gz wt b20220518
## iprgc_84 P8_WT_Ret_S2A_S2_R2_001.fastq.gz wt b20220518
## iprgc_85 P8_KO_SCN_S1A_S12_R2_001.fastq.gz ko b20230515
## iprgc_86 P8_KO_Ret_S1A_S10_R2_001.fastq.gz ko b20230515
## iprgc_87 P8_KO_dLGN_S1A_S11_R2_001.fastq.gz ko b20230515
## iprgc_88 P8_Het_dLGN_S1A_S8_R2_001.fastq.gz het b20230515
## iprgc_89 P8_Het_SCN_S1A_S9_R2_001.fastq.gz het b20230515
## iprgc_90 P8_Het_Ret_S1A_S7_R2_001.fastq.gz het b20230515
## iprgc_91 P8_WT_Ret_S1A_S1_R2_001.fastq.gz wt b20230515
## iprgc_92 P8_WT_dLGN_S1A_S2_R2_001.fastq.gz wt b20230515
## iprgc_93 P8_WT_SCN_S1A_S3_R2_001.fastq.gz wt b20230515
## iprgc_94 P15_Het_dLGN_S1B_S17_R2_001.fastq.gz het b20230515
## iprgc_95 P15_Het_SCN_S1B_S18_R2_001.fastq.gz het b20230516
## iprgc_96 P15_Het_Ret_S1B_S16_R2_001.fastq.gz het b20230517
## iprgc_97 P15_WT_SCN_S1A_S6_R2_001.fastq.gz wt b20230518
## iprgc_98 P15_WT_dLGN_S1A_S5_R2_001.fastq.gz wt b20230519
## iprgc_99 P15_WT_Ret_S1A_S4_R2_001.fastq.gz wt b20230520
## iprgc_100 P15_KO_dLGN_S1A_S14_R2_001.fastq.gz ko b20230521
## iprgc_101 P15_KO_Ret_S1A_S13_R2_001.fastq.gz ko b20230522
## iprgc_102 P15_KO_SCN_S1A_S15_R2_001.fastq.gz ko b20230523
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R2_001.fastq.gz het b20231010
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R2_001.fastq.gz het b20231010
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R2_001.fastq.gz het b20231010
## iprgc_107 16_P8_KO_dLGN_S2_S9_R2_001.fastq.gz ko b20231010
## iprgc_108 18_P15_KO_dLGN_S2_S10_R2_001.fastq.gz ko b20231010
## iprgc_109 1b_P8_WT_Ret_S2_S16_R2_001.fastq.gz wt b20231010
## iprgc_110 22b_P8_Het_SCN_S2_S11_R2_001.fastq.gz het b20231010
## iprgc_111 23b_P8_Het_SCN_S3_S12_R2_001.fastq.gz het b20231010
## iprgc_112 24b_P15_Het_SCN_S2_S13_R2_001.fastq.gz het b20231010
## iprgc_113 25_P8_KO_SCN_S2_S14_R2_001.fastq.gz ko b20231010
## iprgc_114 27_P15_KO_SCN_S2_S15_R2_001.fastq.gz ko b20231010
## iprgc_115 2b_P8_WT_Ret_S3_S17_R2_001.fastq.gz wt b20231010
## iprgc_116 3b_P15_WT_Ret_S3_S18_R2_001.fastq.gz wt b20231010
## iprgc_117 4b_P8_Het_Ret_S2_S1_R2_001.fastq.gz het b20231010
## iprgc_118 5_P8_Het_Ret_S3_S2_R2_001.fastq.gz het b20231010
## iprgc_119 6b_P15_Het_Ret_S2_S3_R2_001.fastq.gz het b20231010
## iprgc_120 7_P8_KO_Ret_S2_S4_R2_001.fastq.gz ko b20231010
## iprgc_121 8_P8_KO_Ret_S3_S19_R2_001.fastq.gz ko b20231010
## iprgc_122 9_P15_KO_Ret_S2_S5_R2_001.fastq.gz ko b20231010
## iprgc_123 <NA> ko <NA>
## iprgc_124 <NA> ko <NA>
## iprgc_125 <NA> wt <NA>
## iprgc_126 <NA> wt <NA>
## iprgc_127 <NA> wt <NA>
## iprgc_128 <NA> wt <NA>
## iprgc_129 <NA> wt <NA>
## file
## iprgc_62 preprocessing_new/iprgc_62/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_63 preprocessing_new/iprgc_63/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_64 preprocessing_new/iprgc_64/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_65 preprocessing_new/iprgc_65/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_67 preprocessing_new/iprgc_67/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_68 preprocessing_new/iprgc_68/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_69 preprocessing_new/iprgc_69/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_70 preprocessing_new/iprgc_70/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_71 preprocessing_new/iprgc_71/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_72 preprocessing_new/iprgc_72/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_73 preprocessing_new/iprgc_73/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_74 preprocessing_new/iprgc_74/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_75 preprocessing_new/iprgc_75/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_76 preprocessing_new/iprgc_76/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_77 preprocessing_new/iprgc_77/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_81 preprocessing/iprgc_81/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_82 preprocessing/iprgc_82/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_83 preprocessing/iprgc_83/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_84 preprocessing/iprgc_84/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_85 preprocessing/iprgc_85/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_86 preprocessing/iprgc_86/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_87 preprocessing/iprgc_87/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_88 preprocessing/iprgc_88/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_89 preprocessing/iprgc_89/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_90 preprocessing/iprgc_90/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_91 preprocessing/iprgc_91/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_92 preprocessing/iprgc_92/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_93 preprocessing/iprgc_93/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_94 preprocessing/iprgc_94/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_95 preprocessing/iprgc_95/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_96 preprocessing/iprgc_96/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_97 preprocessing/iprgc_97/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_98 preprocessing/iprgc_98/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_99 preprocessing/iprgc_99/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_100 preprocessing/iprgc_100/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_101 preprocessing/iprgc_101/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_102 preprocessing/iprgc_102/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_104 preprocessing/iprgc_104/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_105 preprocessing/iprgc_105/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_106 preprocessing/iprgc_106/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_107 preprocessing/iprgc_107/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_108 preprocessing/iprgc_108/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_109 preprocessing/iprgc_109/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_110 preprocessing/iprgc_110/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_111 preprocessing/iprgc_111/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_112 preprocessing/iprgc_112/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_113 preprocessing/iprgc_113/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_114 preprocessing/iprgc_114/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_115 preprocessing/iprgc_115/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_116 preprocessing/iprgc_116/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_117 preprocessing/iprgc_117/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_118 preprocessing/iprgc_118/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_119 preprocessing/iprgc_119/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_120 preprocessing/iprgc_120/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_121 preprocessing/iprgc_121/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_122 preprocessing/iprgc_122/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_123 <NA>
## iprgc_124 <NA>
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## symlink
## iprgc_62 preprocessing/preprocessing_new_taa/iprgc_62/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_63 preprocessing/preprocessing_new_taa/iprgc_63/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_64 preprocessing/preprocessing_new_taa/iprgc_64/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_65 preprocessing/preprocessing_new_taa/iprgc_65/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_67 preprocessing/preprocessing_new_taa/iprgc_67/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_68 preprocessing/preprocessing_new_taa/iprgc_68/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_69 preprocessing/preprocessing_new_taa/iprgc_69/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_70 preprocessing/preprocessing_new_taa/iprgc_70/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_71 preprocessing/preprocessing_new_taa/iprgc_71/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_72 preprocessing/preprocessing_new_taa/iprgc_72/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_73 preprocessing/preprocessing_new_taa/iprgc_73/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_74 preprocessing/preprocessing_new_taa/iprgc_74/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_75 preprocessing/preprocessing_new_taa/iprgc_75/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_76 preprocessing/preprocessing_new_taa/iprgc_76/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_77 preprocessing/preprocessing_new_taa/iprgc_77/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_81 preprocessing/preprocessing_taa/iprgc_81/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_82 preprocessing/preprocessing_taa/iprgc_82/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_83 preprocessing/preprocessing_taa/iprgc_83/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_84 preprocessing/preprocessing_taa/iprgc_84/Cogent/outputs/40hisat2_mm38_100/mm38_100_genome-paired_gene_sno_gene_ID.count.xz
## iprgc_85 <NA>
## iprgc_86 preprocessing/preprocessing_taa/iprgc_85/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_87 preprocessing/preprocessing_taa/iprgc_86/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_88 preprocessing/preprocessing_taa/iprgc_87/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_89 preprocessing/preprocessing_taa/iprgc_88/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_90 preprocessing/preprocessing_taa/iprgc_89/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_91 preprocessing/preprocessing_taa/iprgc_90/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_92 preprocessing/preprocessing_taa/iprgc_91/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_93 preprocessing/preprocessing_taa/iprgc_92/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_94 preprocessing/preprocessing_taa/iprgc_93/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_95 preprocessing/preprocessing_taa/iprgc_94/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_96 preprocessing/preprocessing_taa/iprgc_95/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_97 preprocessing/preprocessing_taa/iprgc_96/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_98 preprocessing/preprocessing_taa/iprgc_97/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_99 preprocessing/preprocessing_taa/iprgc_98/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_100 preprocessing/preprocessing_taa/iprgc_99/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_101 preprocessing/preprocessing_taa/iprgc_100/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_102 preprocessing/preprocessing_taa/iprgc_101/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_104 <NA>
## iprgc_105 preprocessing/preprocessing_taa/iprgc_103/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_106 preprocessing/preprocessing_taa/iprgc_104/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_107 preprocessing/preprocessing_taa/iprgc_105/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_108 preprocessing/preprocessing_taa/iprgc_106/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_109 preprocessing/preprocessing_taa/iprgc_107/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_110 preprocessing/preprocessing_taa/iprgc_108/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_111 preprocessing/preprocessing_taa/iprgc_109/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_112 preprocessing/preprocessing_taa/iprgc_110/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_113 preprocessing/preprocessing_taa/iprgc_111/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_114 preprocessing/preprocessing_taa/iprgc_112/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_115 preprocessing/preprocessing_taa/iprgc_113/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_116 preprocessing/preprocessing_taa/iprgc_114/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_117 preprocessing/preprocessing_taa/iprgc_115/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_118 preprocessing/preprocessing_taa/iprgc_116/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_119 preprocessing/preprocessing_taa/iprgc_117/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_120 preprocessing/preprocessing_taa/iprgc_118/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_121 preprocessing/preprocessing_taa/iprgc_119/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_122 preprocessing/preprocessing_taa/iprgc_120/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_123 preprocessing/preprocessing_taa/iprgc_121/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_124 preprocessing/preprocessing_taa/iprgc_122/outputs/40hisat2_mm38_100/mm38_100_genome-paired_sno_gene_gene_id.count.xz
## iprgc_125 <NA>
## iprgc_126 <NA>
## iprgc_127 <NA>
## iprgc_128 <NA>
## iprgc_129 <NA>
## totalreads trim1_both_surviving trim1_suriving_pct trim1_forward
## iprgc_62 21454044 7307772 0.3406 10575438
## iprgc_63 15326358 5673117 0.3702 8006539
## iprgc_64 18003256 6665716 0.3703 9518432
## iprgc_65 20929623 7076283 0.3381 10013788
## iprgc_67 15456881 5714266 0.3697 8184877
## iprgc_68 19666257 6900044 0.3509 9066997
## iprgc_69 16552894 6100310 0.3685 8839919
## iprgc_70 13740575 5066146 0.3687 7296079
## iprgc_71 15525499 6038811 0.3890 8406723
## iprgc_72 17094474 6541727 0.3827 9369448
## iprgc_73 15794837 6056416 0.3834 8661904
## iprgc_74 15775224 6094043 0.3863 8876775
## iprgc_75 16328183 6160227 0.3773 8655792
## iprgc_76 24180028 9053454 0.3744 13014766
## iprgc_77 17538867 5891680 0.3359 8619704
## iprgc_81 16532098 6408776 0.3877 8634609
## iprgc_82 15878311 5957695 0.3752 8664412
## iprgc_83 16082507 6370746 0.3961 8795845
## iprgc_84 15958920 6090563 0.3816 8659251
## iprgc_85 NA NA NA NA
## iprgc_86 NA NA NA NA
## iprgc_87 NA NA NA NA
## iprgc_88 NA NA NA NA
## iprgc_89 NA NA NA NA
## iprgc_90 NA NA NA NA
## iprgc_91 NA NA NA NA
## iprgc_92 NA NA NA NA
## iprgc_93 NA NA NA NA
## iprgc_94 NA NA NA NA
## iprgc_95 NA NA NA NA
## iprgc_96 NA NA NA NA
## iprgc_97 NA NA NA NA
## iprgc_98 NA NA NA NA
## iprgc_99 NA NA NA NA
## iprgc_100 NA NA NA NA
## iprgc_101 NA NA NA NA
## iprgc_102 NA NA NA NA
## iprgc_104 NA NA NA NA
## iprgc_105 NA NA NA NA
## iprgc_106 NA NA NA NA
## iprgc_107 NA NA NA NA
## iprgc_108 NA NA NA NA
## iprgc_109 NA NA NA NA
## iprgc_110 NA NA NA NA
## iprgc_111 NA NA NA NA
## iprgc_112 NA NA NA NA
## iprgc_113 NA NA NA NA
## iprgc_114 NA NA NA NA
## iprgc_115 NA NA NA NA
## iprgc_116 NA NA NA NA
## iprgc_117 NA NA NA NA
## iprgc_118 NA NA NA NA
## iprgc_119 NA NA NA NA
## iprgc_120 NA NA NA NA
## iprgc_121 NA NA NA NA
## iprgc_122 NA NA NA NA
## iprgc_123 NA NA NA NA
## iprgc_124 NA NA NA NA
## iprgc_125 NA NA NA NA
## iprgc_126 NA NA NA NA
## iprgc_127 NA NA NA NA
## iprgc_128 NA NA NA NA
## iprgc_129 NA NA NA NA
## trim1_reverse trim1_dropped trim2_remaining trim2_surviving_pct
## iprgc_62 161944 3408890 19941862 0.9295
## iprgc_63 126447 1520255 14735531 0.9615
## iprgc_64 147975 1671133 17308611 0.9614
## iprgc_65 183633 3655919 19162876 0.9156
## iprgc_67 129987 1427751 14776608 0.9560
## iprgc_68 154410 3544806 18079308 0.9193
## iprgc_69 153735 1458930 15834102 0.9566
## iprgc_70 107844 1270506 13117568 0.9547
## iprgc_71 140419 939546 15001298 0.9662
## iprgc_72 148260 1035039 16587408 0.9703
## iprgc_73 139349 937168 15290737 0.9681
## iprgc_74 131880 672526 15327351 0.9716
## iprgc_75 155114 1357050 15648822 0.9584
## iprgc_76 212621 1899187 23269354 0.9623
## iprgc_77 126846 2900637 16006650 0.9126
## iprgc_81 160717 1327996 15768927 0.9538
## iprgc_82 137408 1118796 15308622 0.9641
## iprgc_83 137211 778705 15698279 0.9761
## iprgc_84 140920 1068186 15508709 0.9718
## iprgc_85 NA NA NA NA
## iprgc_86 NA NA NA NA
## iprgc_87 NA NA NA NA
## iprgc_88 NA NA NA NA
## iprgc_89 NA NA NA NA
## iprgc_90 NA NA NA NA
## iprgc_91 NA NA NA NA
## iprgc_92 NA NA NA NA
## iprgc_93 NA NA NA NA
## iprgc_94 NA NA NA NA
## iprgc_95 NA NA NA NA
## iprgc_96 NA NA NA NA
## iprgc_97 NA NA NA NA
## iprgc_98 NA NA NA NA
## iprgc_99 NA NA NA NA
## iprgc_100 NA NA NA NA
## iprgc_101 NA NA NA NA
## iprgc_102 NA NA NA NA
## iprgc_104 NA NA NA NA
## iprgc_105 NA NA NA NA
## iprgc_106 NA NA NA NA
## iprgc_107 NA NA NA NA
## iprgc_108 NA NA NA NA
## iprgc_109 NA NA NA NA
## iprgc_110 NA NA NA NA
## iprgc_111 NA NA NA NA
## iprgc_112 NA NA NA NA
## iprgc_113 NA NA NA NA
## iprgc_114 NA NA NA NA
## iprgc_115 NA NA NA NA
## iprgc_116 NA NA NA NA
## iprgc_117 NA NA NA NA
## iprgc_118 NA NA NA NA
## iprgc_119 NA NA NA NA
## iprgc_120 NA NA NA NA
## iprgc_121 NA NA NA NA
## iprgc_122 NA NA NA NA
## iprgc_123 NA NA NA NA
## iprgc_124 NA NA NA NA
## iprgc_125 NA NA NA NA
## iprgc_126 NA NA NA NA
## iprgc_127 NA NA NA NA
## iprgc_128 NA NA NA NA
## iprgc_129 NA NA NA NA
## hisat_aligned_cogent_single hisat_aligned_cogent_multi
## iprgc_62 4403419 1083744
## iprgc_63 3913257 1576743
## iprgc_64 8017462 1026624
## iprgc_65 9046156 1156208
## iprgc_67 5936037 1208092
## iprgc_68 3617748 990881
## iprgc_69 10915725 1053089
## iprgc_70 4302666 866074
## iprgc_71 5866688 1181784
## iprgc_72 6332063 1423466
## iprgc_73 5980501 1233428
## iprgc_74 9320058 1156166
## iprgc_75 8782932 1128430
## iprgc_76 13923768 2032992
## iprgc_77 4119038 884696
## iprgc_81 6300618 1191976
## iprgc_82 6276247 1097569
## iprgc_83 9641795 3309802
## iprgc_84 9095113 2231760
## iprgc_85 NA NA
## iprgc_86 NA NA
## iprgc_87 NA NA
## iprgc_88 NA NA
## iprgc_89 NA NA
## iprgc_90 NA NA
## iprgc_91 NA NA
## iprgc_92 NA NA
## iprgc_93 NA NA
## iprgc_94 NA NA
## iprgc_95 NA NA
## iprgc_96 NA NA
## iprgc_97 NA NA
## iprgc_98 NA NA
## iprgc_99 NA NA
## iprgc_100 NA NA
## iprgc_101 NA NA
## iprgc_102 NA NA
## iprgc_104 NA NA
## iprgc_105 NA NA
## iprgc_106 NA NA
## iprgc_107 NA NA
## iprgc_108 NA NA
## iprgc_109 NA NA
## iprgc_110 NA NA
## iprgc_111 NA NA
## iprgc_112 NA NA
## iprgc_113 NA NA
## iprgc_114 NA NA
## iprgc_115 NA NA
## iprgc_116 NA NA
## iprgc_117 NA NA
## iprgc_118 NA NA
## iprgc_119 NA NA
## iprgc_120 NA NA
## iprgc_121 NA NA
## iprgc_122 NA NA
## iprgc_123 NA NA
## iprgc_124 NA NA
## iprgc_125 NA NA
## iprgc_126 NA NA
## iprgc_127 NA NA
## iprgc_128 NA NA
## iprgc_129 NA NA
## hisat_aligned_cogent_pct rrna_aligned_single rrna_aligned_single_pct
## iprgc_62 0.2558 9981295 0.5581
## iprgc_63 0.3582 8456038 0.6181
## iprgc_64 0.5024 5806861 0.3588
## iprgc_65 0.4875 4654006 0.2723
## iprgc_67 0.4622 6686073 0.4810
## iprgc_68 0.2343 8880676 0.5562
## iprgc_69 0.7231 1156001 0.0774
## iprgc_70 0.3762 5198926 0.4205
## iprgc_71 0.4540 8388194 0.5807
## iprgc_72 0.4537 8912327 0.5601
## iprgc_73 0.4567 8572959 0.5825
## iprgc_74 0.6641 4950093 0.3306
## iprgc_75 0.6070 3564544 0.2406
## iprgc_76 0.6599 4499355 0.2039
## iprgc_77 0.2853 5596546 0.3857
## iprgc_81 0.4532 7013653 0.4662
## iprgc_82 0.4644 4686722 0.3205
## iprgc_83 0.8053 2613957 0.1723
## iprgc_84 0.7098 2753845 0.1867
## iprgc_85 NA NA NA
## iprgc_86 NA NA NA
## iprgc_87 NA NA NA
## iprgc_88 NA NA NA
## iprgc_89 NA NA NA
## iprgc_90 NA NA NA
## iprgc_91 NA NA NA
## iprgc_92 NA NA NA
## iprgc_93 NA NA NA
## iprgc_94 NA NA NA
## iprgc_95 NA NA NA
## iprgc_96 NA NA NA
## iprgc_97 NA NA NA
## iprgc_98 NA NA NA
## iprgc_99 NA NA NA
## iprgc_100 NA NA NA
## iprgc_101 NA NA NA
## iprgc_102 NA NA NA
## iprgc_104 NA NA NA
## iprgc_105 NA NA NA
## iprgc_106 NA NA NA
## iprgc_107 NA NA NA
## iprgc_108 NA NA NA
## iprgc_109 NA NA NA
## iprgc_110 NA NA NA
## iprgc_111 NA NA NA
## iprgc_112 NA NA NA
## iprgc_113 NA NA NA
## iprgc_114 NA NA NA
## iprgc_115 NA NA NA
## iprgc_116 NA NA NA
## iprgc_117 NA NA NA
## iprgc_118 NA NA NA
## iprgc_119 NA NA NA
## iprgc_120 NA NA NA
## iprgc_121 NA NA NA
## iprgc_122 NA NA NA
## iprgc_123 NA NA NA
## iprgc_124 NA NA NA
## iprgc_125 NA NA NA
## iprgc_126 NA NA NA
## iprgc_127 NA NA NA
## iprgc_128 NA NA NA
## iprgc_129 NA NA NA
## rrna_aligned_multi rrna_aligned_multi_pct rrna_not_aligned
## iprgc_62 552408 0.0309 7349507
## iprgc_63 554987 0.0406 4668631
## iprgc_64 211357 0.0131 10165930
## iprgc_65 186314 0.0109 12249751
## iprgc_67 449142 0.0323 6763928
## iprgc_68 464529 0.0291 6621836
## iprgc_69 30867 0.0021 13753361
## iprgc_70 301939 0.0244 6861360
## iprgc_71 675994 0.0468 5381346
## iprgc_72 784989 0.0493 6213859
## iprgc_73 485581 0.0330 5659780
## iprgc_74 506909 0.0339 9513816
## iprgc_75 282107 0.0190 10969368
## iprgc_76 444836 0.0202 17124029
## iprgc_77 267421 0.0184 8647417
## iprgc_81 334632 0.0222 7695100
## iprgc_82 323766 0.0221 9611619
## iprgc_83 139135 0.0092 12413499
## iprgc_84 249586 0.0169 11746383
## iprgc_85 NA NA NA
## iprgc_86 NA NA NA
## iprgc_87 NA NA NA
## iprgc_88 NA NA NA
## iprgc_89 NA NA NA
## iprgc_90 NA NA NA
## iprgc_91 NA NA NA
## iprgc_92 NA NA NA
## iprgc_93 NA NA NA
## iprgc_94 NA NA NA
## iprgc_95 NA NA NA
## iprgc_96 NA NA NA
## iprgc_97 NA NA NA
## iprgc_98 NA NA NA
## iprgc_99 NA NA NA
## iprgc_100 NA NA NA
## iprgc_101 NA NA NA
## iprgc_102 NA NA NA
## iprgc_104 NA NA NA
## iprgc_105 NA NA NA
## iprgc_106 NA NA NA
## iprgc_107 NA NA NA
## iprgc_108 NA NA NA
## iprgc_109 NA NA NA
## iprgc_110 NA NA NA
## iprgc_111 NA NA NA
## iprgc_112 NA NA NA
## iprgc_113 NA NA NA
## iprgc_114 NA NA NA
## iprgc_115 NA NA NA
## iprgc_116 NA NA NA
## iprgc_117 NA NA NA
## iprgc_118 NA NA NA
## iprgc_119 NA NA NA
## iprgc_120 NA NA NA
## iprgc_121 NA NA NA
## iprgc_122 NA NA NA
## iprgc_123 NA NA NA
## iprgc_124 NA NA NA
## iprgc_125 NA NA NA
## iprgc_126 NA NA NA
## iprgc_127 NA NA NA
## iprgc_128 NA NA NA
## iprgc_129 NA NA NA
## rrna_not_aligned_pct hisat_single hisat_multi hisat_map_pct
## iprgc_62 0.411 1848699 820527 0.1339
## iprgc_63 0.3413 1487847 590756 0.1411
## iprgc_64 0.6281 3115788 634919 0.2167
## iprgc_65 0.7168 3497108 645740 0.2162
## iprgc_67 0.4866 2410898 657348 0.2076
## iprgc_68 0.4147 1693648 716559 0.1333
## iprgc_69 0.9206 4103748 467294 0.2887
## iprgc_70 0.555 1768274 466076 0.1703
## iprgc_71 0.3725 2429963 731130 0.2107
## iprgc_72 0.3905 2575448 795986 0.2033
## iprgc_73 0.3845 2460492 708855 0.2073
## iprgc_74 0.6355 3522006 601166 0.2690
## iprgc_75 0.7404 3382787 555319 0.2517
## iprgc_76 0.776 5278142 931930 0.2669
## iprgc_77 0.5959 1670154 491774 0.1351
## iprgc_81 0.5115 2673009 654361 0.2110
## iprgc_82 0.6573 2443165 556951 0.1960
## iprgc_83 0.8185 3764124 1427725 0.3307
## iprgc_84 0.7964 3457750 987993 0.2867
## iprgc_85 LIKELY PCT mRNA CONTENT NA NA NA
## iprgc_86 <NA> NA NA NA
## iprgc_87 <NA> NA NA NA
## iprgc_88 <NA> NA NA NA
## iprgc_89 <NA> NA NA NA
## iprgc_90 <NA> NA NA NA
## iprgc_91 <NA> NA NA NA
## iprgc_92 <NA> NA NA NA
## iprgc_93 <NA> NA NA NA
## iprgc_94 <NA> NA NA NA
## iprgc_95 <NA> NA NA NA
## iprgc_96 <NA> NA NA NA
## iprgc_97 <NA> NA NA NA
## iprgc_98 <NA> NA NA NA
## iprgc_99 <NA> NA NA NA
## iprgc_100 <NA> NA NA NA
## iprgc_101 <NA> NA NA NA
## iprgc_102 <NA> NA NA NA
## iprgc_104 <NA> NA NA NA
## iprgc_105 <NA> NA NA NA
## iprgc_106 <NA> NA NA NA
## iprgc_107 <NA> NA NA NA
## iprgc_108 <NA> NA NA NA
## iprgc_109 <NA> NA NA NA
## iprgc_110 <NA> NA NA NA
## iprgc_111 <NA> NA NA NA
## iprgc_112 <NA> NA NA NA
## iprgc_113 <NA> NA NA NA
## iprgc_114 <NA> NA NA NA
## iprgc_115 <NA> NA NA NA
## iprgc_116 <NA> NA NA NA
## iprgc_117 <NA> NA NA NA
## iprgc_118 <NA> NA NA NA
## iprgc_119 <NA> NA NA NA
## iprgc_120 <NA> NA NA NA
## iprgc_121 <NA> NA NA NA
## iprgc_122 <NA> NA NA NA
## iprgc_123 <NA> NA NA NA
## iprgc_124 <NA> NA NA NA
## iprgc_125 <NA> NA NA NA
## iprgc_126 <NA> NA NA NA
## iprgc_127 <NA> NA NA NA
## iprgc_128 <NA> NA NA NA
## iprgc_129 <NA> NA NA NA
## hisat_mapped_total_reads projectah sampleah rashmissamplenameah
## iprgc_62 0.1244 021_1 16 P15_Het_dLGN_S1A
## iprgc_63 0.1356 021_1 17 P15_Het_dLGN_S2A
## iprgc_64 0.2083 021_1 7 P15_Het_Ret_S1A
## iprgc_65 0.1979 021_1 8 P15_Het_Ret_S2A
## iprgc_67 0.1985 021_1 26 P15_Het_SCN_S2A
## iprgc_68 0.1226 021_1 18 P15_KO_dLGN_S1A
## iprgc_69 0.2761 021_1 9 P15_KO_Ret_S1A
## iprgc_70 0.1626 021_1 27 P15_KO_SCN_S1A
## iprgc_71 0.2036 021_1 12 P15_WT_dLGN_S1A
## iprgc_72 0.1972 021_1 13 P15_WT_dLGN_S2A
## iprgc_73 0.2007 021_1 14 P15_WT_dLGN_S3A
## iprgc_74 0.2614 021_1 3 P15_WT_Ret_S1A
## iprgc_75 0.2412 021_1 4 P15_WT_Ret_S2A
## iprgc_76 0.2568 021_1 5 P15_WT_Ret_S3A
## iprgc_77 0.1233 021_1 23 P15_WT_SCN_S3A
## iprgc_81 0.2013 021_1 10 P8_WT_dLGN_S1A
## iprgc_82 0.1889 021_1 11 P8_WT_dLGN_S2A
## iprgc_83 0.3228 021_1 1 P8_WT_Ret_S1A
## iprgc_84 0.2786 021_1 2 P8_WT_Ret_S2A
## iprgc_85 NA 021_2 15 P8_KO_SCN_S1A
## iprgc_86 NA 021_2 13 P8_KO_Ret_S1A
## iprgc_87 NA 021_2 14 P8_KO_dLGN_S1A
## iprgc_88 NA 021_2 8 P8_Het_dLGN_S1A
## iprgc_89 NA 021_2 9 P8_Het_SCN_S1A
## iprgc_90 NA 021_2 7 P8_Het_Ret_S1A
## iprgc_91 NA 021_2 1 P8_WT_Ret_S1A
## iprgc_92 NA 021_2 2 P8_WT_dLGN_S1A
## iprgc_93 NA 021_2 3 P8_WT_SCN_S1A
## iprgc_94 NA 021_2 20 P15_Het_dLGN_S1B
## iprgc_95 NA 021_2 21 P15_Het_SCN_S1B
## iprgc_96 NA 021_2 19 P15_Het_Ret_S1B
## iprgc_97 NA 021_2 6 P15_WT_SCN_S1A
## iprgc_98 NA 021_2 5 P15_WT_dLGN_S1A
## iprgc_99 NA 021_2 4 P15_WT_Ret_S1A
## iprgc_100 NA 021_2 17 P15_KO_dLGN_S1A
## iprgc_101 NA 021_2 16 P15_KO_Ret_S1A
## iprgc_102 NA 021_2 18 P15_KO_SCN_S1A
## iprgc_104 NA 021_3 13b P8_Het_dLGN_S2
## iprgc_105 NA 021_3 14b P8_Het_dLGN_S3
## iprgc_106 NA 021_3 15b P15_Het_dLGN_S2
## iprgc_107 NA 021_3 16 P8_KO_dLGN_S2
## iprgc_108 NA 021_3 18 P15_KO_dLGN_S2
## iprgc_109 NA 021_3 1b P8_WT_Ret_S2
## iprgc_110 NA 021_3 22b P8_Het_SCN_S2
## iprgc_111 NA 021_3 23b P8_Het_SCN_S3
## iprgc_112 NA 021_3 24b P15_Het_SCN_S2
## iprgc_113 NA 021_3 25 P8_KO_SCN_S2
## iprgc_114 NA 021_3 27 P15_KO_SCN_S2
## iprgc_115 NA 021_3 2b P8_WT_Ret_S3
## iprgc_116 NA 021_3 3b P15_WT_Ret_S3
## iprgc_117 NA 021_3 4b P8_Het_Ret_S2
## iprgc_118 NA 021_3 5 P8_Het_Ret_S3
## iprgc_119 NA 021_3 6b P15_Het_Ret_S2
## iprgc_120 NA 021_3 7 P8_KO_Ret_S2
## iprgc_121 NA 021_3 8 P8_KO_Ret_S3
## iprgc_122 NA 021_3 9 P15_KO_Ret_S2
## iprgc_123 NA 021_3 17 P8_KO_dLGN_S3
## iprgc_124 NA 021_3 26 P8_KO_SCN_S3
## iprgc_125 NA 021_3 10c P8_WT_dLGN_S2
## iprgc_126 NA 021_3 11c P8_WT_dLGN_S3
## iprgc_127 NA 021_3 12b P15_WT_dLGN_S3
## iprgc_128 NA 021_3 19c P8_WT_SCN_S2
## iprgc_129 NA 021_3 20c P8_WT_SCN_S3
## libraryprepcleanupah sequencingrunah
## iprgc_62 successful 220517_VL00136_33_AAC23JWM5
## iprgc_63 successful 220517_VL00136_33_AAC23JWM5
## iprgc_64 successful 220517_VL00136_33_AAC23JWM5
## iprgc_65 successful 220517_VL00136_33_AAC23JWM5
## iprgc_67 successful 220517_VL00136_33_AAC23JWM5
## iprgc_68 successful 220517_VL00136_33_AAC23JWM5
## iprgc_69 successful 220517_VL00136_33_AAC23JWM5
## iprgc_70 successful 220517_VL00136_33_AAC23JWM5
## iprgc_71 successful 220517_VL00136_33_AAC23JWM5
## iprgc_72 successful 220517_VL00136_33_AAC23JWM5
## iprgc_73 successful 220517_VL00136_33_AAC23JWM5
## iprgc_74 successful 220517_VL00136_33_AAC23JWM5
## iprgc_75 successful 220517_VL00136_33_AAC23JWM5
## iprgc_76 successful 220517_VL00136_33_AAC23JWM5
## iprgc_77 successful 220517_VL00136_33_AAC23JWM5
## iprgc_81 successful 220517_VL00136_33_AAC23JWM5
## iprgc_82 successful 220517_VL00136_33_AAC23JWM5
## iprgc_83 successful 220517_VL00136_33_AAC23JWM5
## iprgc_84 successful 220517_VL00136_33_AAC23JWM5
## iprgc_85 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_86 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_87 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_88 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_89 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_90 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_91 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_92 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_93 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_94 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_95 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_96 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_97 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_98 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_99 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_100 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_101 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_102 successful 230412_VL00136_54_AACLVMTM5/Analysis/2/
## iprgc_104 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_105 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_106 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_107 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_108 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_109 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_110 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_111 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_112 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_113 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_114 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_115 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_116 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_117 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_118 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_119 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_120 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_121 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_122 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_123 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_124 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_125 successful 231004_VL00136_73_AACWYG2M5/Analysis/1/
## iprgc_126 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_127 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_128 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## iprgc_129 successful 231006_VL00136_74_AACY7TGM5/Analysis/1/
## read1ah
## iprgc_62 P15_Het_dLGN_S1A_S16_R1_001.fastq.gz
## iprgc_63 P15_Het_dLGN_S2A_S17_R1_001.fastq.gz
## iprgc_64 P15_Het_Ret_S1A_S7_R1_001.fastq.gz
## iprgc_65 P15_Het_Ret_S2A_S8_R1_001.fastq.gz
## iprgc_67 P15_Het_SCN_S2A_S22_R1_001.fastq.gz
## iprgc_68 P15_KO_dLGN_S1A_S18_R1_001.fastq.gz
## iprgc_69 P15_KO_Ret_S1A_S9_R1_001.fastq.gz
## iprgc_70 P15_KO_SCN_S1A_S23_R1_001.fastq.gz
## iprgc_71 P15_WT_dLGN_S1A_S12_R1_001.fastq.gz
## iprgc_72 P15_WT_dLGN_S2A_S13_R1_001.fastq.gz
## iprgc_73 P15_WT_dLGN_S3A_S14_R1_001.fastq.gz
## iprgc_74 P15_WT_Ret_S1A_S3_R1_001.fastq.gz
## iprgc_75 P15_WT_Ret_S2A_S4_R1_001.fastq.gz
## iprgc_76 P15_WT_Ret_S3A_S5_R1_001.fastq.gz
## iprgc_77 P15_WT_SCN_S3A_S19_R1_001.fastq.gz
## iprgc_81 P8_WT_dLGN_S1A_S10_R1_001.fastq.gz
## iprgc_82 P8_WT_dLGN_S2A_S11_R1_001.fastq.gz
## iprgc_83 P8_WT_Ret_S1A_S1_R1_001.fastq.gz
## iprgc_84 P8_WT_Ret_S2A_S2_R1_001.fastq.gz
## iprgc_85 P8_KO_SCN_S1A_S12_L001_R1_001.fastq.gz
## iprgc_86 P8_KO_Ret_S1A_S10_L001_R1_001.fastq.gz
## iprgc_87 P8_KO_dLGN_S1A_S11_L001_R1_001.fastq.gz
## iprgc_88 P8_Het_dLGN_S1A_S8_L001_R1_001.fastq.gz
## iprgc_89 P8_Het_SCN_S1A_S9_L001_R1_001.fastq.gz
## iprgc_90 P8_Het_Ret_S1A_S7_L001_R1_001.fastq.gz
## iprgc_91 P8_WT_Ret_S1A_S1_L001_R1_001.fastq.gz
## iprgc_92 P8_WT_dLGN_S1A_S2_L001_R1_001.fastq.gz
## iprgc_93 P8_WT_SCN_S1A_S3_L001_R1_001.fastq.gz
## iprgc_94 P15_Het_dLGN_S1B_S17_L001_R1_001.fastq.gz
## iprgc_95 P15_Het_SCN_S1B_S18_L001_R1_001.fastq.gz
## iprgc_96 P15_Het_Ret_S1B_S16_L001_R1_001.fastq.gz
## iprgc_97 P15_WT_SCN_S1A_S6_L001_R1_001.fastq.gz
## iprgc_98 P15_WT_dLGN_S1A_S5_L001_R1_001.fastq.gz
## iprgc_99 P15_WT_Ret_S1A_S4_L001_R1_001.fastq.gz
## iprgc_100 P15_KO_dLGN_S1A_S14_L001_R1_001.fastq.gz
## iprgc_101 P15_KO_Ret_S1A_S13_L001_R1_001.fastq.gz
## iprgc_102 P15_KO_SCN_S1A_S15_L001_R1_001.fastq.gz
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R1_001.fastq.gz
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R1_001.fastq.gz
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R1_001.fastq.gz
## iprgc_107 16_P8_KO_dLGN_S2_S9_R1_001.fastq.gz
## iprgc_108 18_P15_KO_dLGN_S2_S10_R1_001.fastq.gz
## iprgc_109 1b_P8_WT_Ret_S2_S16_R1_001.fastq.gz
## iprgc_110 22b_P8_Het_SCN_S2_S11_R1_001.fastq.gz
## iprgc_111 23b_P8_Het_SCN_S3_S12_R1_001.fastq.gz
## iprgc_112 24b_P15_Het_SCN_S2_S13_R1_001.fastq.gz
## iprgc_113 25_P8_KO_SCN_S2_S14_R1_001.fastq.gz
## iprgc_114 27_P15_KO_SCN_S2_S15_R1_001.fastq.gz
## iprgc_115 2b_P8_WT_Ret_S3_S17_R1_001.fastq.gz
## iprgc_116 3b_P15_WT_Ret_S3_S18_R1_001.fastq.gz
## iprgc_117 4b_P8_Het_Ret_S2_S1_R1_001.fastq.gz
## iprgc_118 5_P8_Het_Ret_S3_S2_R1_001.fastq.gz
## iprgc_119 6b_P15_Het_Ret_S2_S3_R1_001.fastq.gz
## iprgc_120 7_P8_KO_Ret_S2_S4_R1_001.fastq.gz
## iprgc_121 8_P8_KO_Ret_S3_S19_R1_001.fastq.gz
## iprgc_122 9_P15_KO_Ret_S2_S5_R1_001.fastq.gz
## iprgc_123 17_P8_KO_dLGN_S3_S3_R1_001.fastq.gz
## iprgc_124 26_P8_KO_SCN_S3_S7_R1_001.fastq.gz
## iprgc_125 10c_P8_WT_dLGN_S2_S20_R1_001.fastq.gz
## iprgc_126 11c_P8_WT_dLGN_S3_S1_R1_001.fastq.gz
## iprgc_127 12b_P15_WT_dLGN_S3_S2_R1_001.fastq.gz
## iprgc_128 19c_P8_WT_SCN_S2_S4_R1_001.fastq.gz
## iprgc_129 20c_P8_WT_SCN_S3_S5_R1_001.fastq.gz
## read2ah downloadmapagain
## iprgc_62 P15_Het_dLGN_S1A_S16_R2_001.fastq.gz <NA>
## iprgc_63 P15_Het_dLGN_S2A_S17_R2_001.fastq.gz <NA>
## iprgc_64 P15_Het_Ret_S1A_S7_R2_001.fastq.gz <NA>
## iprgc_65 P15_Het_Ret_S2A_S8_R2_001.fastq.gz <NA>
## iprgc_67 P15_Het_SCN_S2A_S22_R2_001.fastq.gz <NA>
## iprgc_68 P15_KO_dLGN_S1A_S18_R2_001.fastq.gz <NA>
## iprgc_69 P15_KO_Ret_S1A_S9_R2_001.fastq.gz <NA>
## iprgc_70 P15_KO_SCN_S1A_S23_R2_001.fastq.gz <NA>
## iprgc_71 P15_WT_dLGN_S1A_S12_R2_001.fastq.gz <NA>
## iprgc_72 P15_WT_dLGN_S2A_S13_R2_001.fastq.gz <NA>
## iprgc_73 P15_WT_dLGN_S3A_S14_R2_001.fastq.gz <NA>
## iprgc_74 P15_WT_Ret_S1A_S3_R2_001.fastq.gz <NA>
## iprgc_75 P15_WT_Ret_S2A_S4_R2_001.fastq.gz <NA>
## iprgc_76 P15_WT_Ret_S3A_S5_R2_001.fastq.gz <NA>
## iprgc_77 P15_WT_SCN_S3A_S19_R2_001.fastq.gz <NA>
## iprgc_81 P8_WT_dLGN_S1A_S10_R2_001.fastq.gz <NA>
## iprgc_82 P8_WT_dLGN_S2A_S11_R2_001.fastq.gz <NA>
## iprgc_83 P8_WT_Ret_S1A_S1_R2_001.fastq.gz <NA>
## iprgc_84 P8_WT_Ret_S2A_S2_R2_001.fastq.gz <NA>
## iprgc_85 P8_KO_SCN_S1A_S12_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_86 P8_KO_Ret_S1A_S10_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_87 P8_KO_dLGN_S1A_S11_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_88 P8_Het_dLGN_S1A_S8_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_89 P8_Het_SCN_S1A_S9_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_90 P8_Het_Ret_S1A_S7_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_91 P8_WT_Ret_S1A_S1_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_92 P8_WT_dLGN_S1A_S2_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_93 P8_WT_SCN_S1A_S3_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_94 P15_Het_dLGN_S1B_S17_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_95 P15_Het_SCN_S1B_S18_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_96 P15_Het_Ret_S1B_S16_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_97 P15_WT_SCN_S1A_S6_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_98 P15_WT_dLGN_S1A_S5_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_99 P15_WT_Ret_S1A_S4_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_100 P15_KO_dLGN_S1A_S14_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_101 P15_KO_Ret_S1A_S13_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_102 P15_KO_SCN_S1A_S15_L001_R2_001.fastq.gz yes, repeated demux
## iprgc_104 13b_P8_Het_dLGN_S2_S6_R2_001.fastq.gz <NA>
## iprgc_105 14b_P8_Het_dLGN_S3_S7_R2_001.fastq.gz <NA>
## iprgc_106 15b_P15_Het_dLGN_S2_S8_R2_001.fastq.gz <NA>
## iprgc_107 16_P8_KO_dLGN_S2_S9_R2_001.fastq.gz <NA>
## iprgc_108 18_P15_KO_dLGN_S2_S10_R2_001.fastq.gz <NA>
## iprgc_109 1b_P8_WT_Ret_S2_S16_R2_001.fastq.gz <NA>
## iprgc_110 22b_P8_Het_SCN_S2_S11_R2_001.fastq.gz <NA>
## iprgc_111 23b_P8_Het_SCN_S3_S12_R2_001.fastq.gz <NA>
## iprgc_112 24b_P15_Het_SCN_S2_S13_R2_001.fastq.gz <NA>
## iprgc_113 25_P8_KO_SCN_S2_S14_R2_001.fastq.gz <NA>
## iprgc_114 27_P15_KO_SCN_S2_S15_R2_001.fastq.gz <NA>
## iprgc_115 2b_P8_WT_Ret_S3_S17_R2_001.fastq.gz <NA>
## iprgc_116 3b_P15_WT_Ret_S3_S18_R2_001.fastq.gz <NA>
## iprgc_117 4b_P8_Het_Ret_S2_S1_R2_001.fastq.gz <NA>
## iprgc_118 5_P8_Het_Ret_S3_S2_R2_001.fastq.gz <NA>
## iprgc_119 6b_P15_Het_Ret_S2_S3_R2_001.fastq.gz <NA>
## iprgc_120 7_P8_KO_Ret_S2_S4_R2_001.fastq.gz <NA>
## iprgc_121 8_P8_KO_Ret_S3_S19_R2_001.fastq.gz <NA>
## iprgc_122 9_P15_KO_Ret_S2_S5_R2_001.fastq.gz <NA>
## iprgc_123 17_P8_KO_dLGN_S3_S3_R2_001.fastq.gz <NA>
## iprgc_124 26_P8_KO_SCN_S3_S7_R2_001.fastq.gz <NA>
## iprgc_125 10c_P8_WT_dLGN_S2_S20_R2_001.fastq.gz yes, same file renamed
## iprgc_126 11c_P8_WT_dLGN_S3_S1_R2_001.fastq.gz yes, same file renamed
## iprgc_127 12b_P15_WT_dLGN_S3_S2_R2_001.fastq.gz <NA>
## iprgc_128 19c_P8_WT_SCN_S2_S4_R2_001.fastq.gz yes, same file renamed
## iprgc_129 20c_P8_WT_SCN_S3_S5_R2_001.fastq.gz yes, same file renamed
## genotype_atb location_atb time_atb geno_loc_atb
## iprgc_62 het dlgn p15 het_dlgn
## iprgc_63 het dlgn p15 het_dlgn
## iprgc_64 het retina p15 het_retina
## iprgc_65 het retina p15 het_retina
## iprgc_67 het scn p15 het_scn
## iprgc_68 ko dlgn p15 ko_dlgn
## iprgc_69 ko retina p15 ko_retina
## iprgc_70 ko scn p15 ko_scn
## iprgc_71 wt dlgn p15 wt_dlgn
## iprgc_72 wt dlgn p15 wt_dlgn
## iprgc_73 wt dlgn p15 wt_dlgn
## iprgc_74 wt retina p15 wt_retina
## iprgc_75 wt retina p15 wt_retina
## iprgc_76 wt retina p15 wt_retina
## iprgc_77 wt scn p15 wt_scn
## iprgc_81 wt dlgn p08 wt_dlgn
## iprgc_82 wt dlgn p08 wt_dlgn
## iprgc_83 wt retina p08 wt_retina
## iprgc_84 wt retina p08 wt_retina
## iprgc_85 ko scn p08 ko_scn
## iprgc_86 ko retina p08 ko_retina
## iprgc_87 ko dlgn p08 ko_dlgn
## iprgc_88 het dlgn p08 het_dlgn
## iprgc_89 het scn p08 het_scn
## iprgc_90 het retina p08 het_retina
## iprgc_91 wt retina p08 wt_retina
## iprgc_92 wt dlgn p08 wt_dlgn
## iprgc_93 wt scn p08 wt_scn
## iprgc_94 het dlgn p15 het_dlgn
## iprgc_95 het scn p15 het_scn
## iprgc_96 het retina p15 het_retina
## iprgc_97 wt scn p15 wt_scn
## iprgc_98 wt dlgn p15 wt_dlgn
## iprgc_99 wt retina p15 wt_retina
## iprgc_100 ko dlgn p15 ko_dlgn
## iprgc_101 ko retina p15 ko_retina
## iprgc_102 ko scn p15 ko_scn
## iprgc_104 het dlgn p08 het_dlgn
## iprgc_105 het dlgn p08 het_dlgn
## iprgc_106 het dlgn p15 het_dlgn
## iprgc_107 ko dlgn p08 ko_dlgn
## iprgc_108 ko dlgn p15 ko_dlgn
## iprgc_109 wt retina p08 wt_retina
## iprgc_110 het scn p08 het_scn
## iprgc_111 het scn p08 het_scn
## iprgc_112 het scn p15 het_scn
## iprgc_113 ko scn p08 ko_scn
## iprgc_114 ko scn p15 ko_scn
## iprgc_115 wt retina p08 wt_retina
## iprgc_116 wt retina p15 wt_retina
## iprgc_117 het retina p08 het_retina
## iprgc_118 het retina p08 het_retina
## iprgc_119 het retina p15 het_retina
## iprgc_120 ko retina p08 ko_retina
## iprgc_121 ko retina p08 ko_retina
## iprgc_122 ko retina p15 ko_retina
## iprgc_123 ko dlgn p08 ko_dlgn
## iprgc_124 ko scn p08 ko_scn
## iprgc_125 wt dlgn p08 wt_dlgn
## iprgc_126 wt dlgn p08 wt_dlgn
## iprgc_127 wt dlgn p15 wt_dlgn
## iprgc_128 wt scn p08 wt_scn
## iprgc_129 wt scn p08 wt_scn
## time_geno_location_source_atb rashmi_code atb_observed_location
## iprgc_62 AH filename S1A dlgn
## iprgc_63 AH filename S2A dlgn
## iprgc_64 AH filename S1A retina
## iprgc_65 AH filename S2A retina
## iprgc_67 AH filename S2A scn
## iprgc_68 AH filename S1A dlgn
## iprgc_69 AH filename S1A retina
## iprgc_70 AH filename S1A scn
## iprgc_71 AH filename S1A dlgn
## iprgc_72 AH filename S2A dlgn
## iprgc_73 AH filename S3A dlgn
## iprgc_74 AH filename S1A retina
## iprgc_75 AH filename S2A retina
## iprgc_76 AH filename S3A retina
## iprgc_77 AH filename S3A scn
## iprgc_81 AH filename S1A dlgn
## iprgc_82 AH filename S2A dlgn
## iprgc_83 AH filename S1A retina
## iprgc_84 AH filename S2A retina
## iprgc_85 AH filename S1A scn
## iprgc_86 AH filename S1A retina
## iprgc_87 AH filename S1A dlgn
## iprgc_88 AH filename S1A dlgn
## iprgc_89 AH filename S1A scn
## iprgc_90 AH filename S1A retina
## iprgc_91 AH filename S1A retina
## iprgc_92 AH filename S1A dlgn
## iprgc_93 AH filename S1A scn
## iprgc_94 AH filename S1B dlgn
## iprgc_95 AH filename S1B scn
## iprgc_96 AH filename S1B retina
## iprgc_97 AH filename S1A scn
## iprgc_98 AH filename S1A dlgn
## iprgc_99 AH filename S1A retina
## iprgc_100 AH filename S1A dlgn
## iprgc_101 AH filename S1A retina
## iprgc_102 AH filename S1A scn
## iprgc_104 AH filename S2 dlgn
## iprgc_105 AH filename S3 dlgn
## iprgc_106 AH filename S2 dlgn
## iprgc_107 AH filename S2 dlgn
## iprgc_108 AH filename S2 dlgn
## iprgc_109 AH filename S2 retina
## iprgc_110 AH filename S2 scn
## iprgc_111 AH filename S3 scn
## iprgc_112 AH filename S2 scn
## iprgc_113 AH filename S2 scn
## iprgc_114 AH filename S2 scn
## iprgc_115 AH filename S3 retina
## iprgc_116 AH filename S3 retina
## iprgc_117 AH filename S2 retina
## iprgc_118 AH filename S3 retina
## iprgc_119 AH filename S2 retina
## iprgc_120 AH filename S2 retina
## iprgc_121 AH filename S3 retina
## iprgc_122 AH filename S2 retina
## iprgc_123 AH filename S3 dlgn
## iprgc_124 AH filename S3 scn
## iprgc_125 AH filename S2 dlgn
## iprgc_126 AH filename S3 dlgn
## iprgc_127 AH filename S3 dlgn
## iprgc_128 AH filename S2 scn
## iprgc_129 AH filename S3 scn
## hisat_rrna_input_reads hisat_rrna_single_concordant
## iprgc_62 21454044 1494615
## iprgc_63 15326358 1079397
## iprgc_64 18003256 942929
## iprgc_65 20929623 1195387
## iprgc_67 15456881 1000025
## iprgc_68 19666257 1628156
## iprgc_69 16552894 218644
## iprgc_70 13740575 700257
## iprgc_71 15525499 1082720
## iprgc_72 17094474 1425296
## iprgc_73 15794837 1285122
## iprgc_74 15775224 790535
## iprgc_75 16328183 877539
## iprgc_76 24180028 1385985
## iprgc_77 17538867 1050779
## iprgc_81 16532098 940095
## iprgc_82 15878311 796807
## iprgc_83 16082507 868003
## iprgc_84 15958920 1050174
## iprgc_85 23680086 1148981
## iprgc_86 20389649 361882
## iprgc_87 27526146 540391
## iprgc_88 29895877 1190844
## iprgc_89 23361256 920771
## iprgc_90 31543306 435071
## iprgc_91 15758794 209248
## iprgc_92 22313362 1157698
## iprgc_93 20934807 1036076
## iprgc_94 28090789 532208
## iprgc_95 23462476 1217287
## iprgc_96 14804663 466300
## iprgc_97 22863130 904924
## iprgc_98 19915507 557667
## iprgc_99 14724866 175966
## iprgc_100 32782165 542666
## iprgc_101 15390215 203176
## iprgc_102 28578632 857116
## iprgc_104 30192805 1038732
## iprgc_105 24528770 523240
## iprgc_106 30802656 390802
## iprgc_107 42462593 642551
## iprgc_108 28855525 290520
## iprgc_109 21695810 338445
## iprgc_110 22927463 1066764
## iprgc_111 18467297 412625
## iprgc_112 19036600 728033
## iprgc_113 27601259 1056238
## iprgc_114 18638104 583967
## iprgc_115 17237384 375309
## iprgc_116 17447459 353770
## iprgc_117 17248353 341716
## iprgc_118 21315056 288212
## iprgc_119 14188002 228078
## iprgc_120 31820880 472691
## iprgc_121 27191863 569880
## iprgc_122 19422096 531934
## iprgc_123 40849547 1075990
## iprgc_124 36378912 1062383
## iprgc_125 16749234 416587
## iprgc_126 22769305 832691
## iprgc_127 23091824 1023687
## iprgc_128 18824955 582920
## iprgc_129 22427478 844116
## hisat_rrna_multi_concordant hisat_rrna_percent_log
## iprgc_62 170903 9.87
## iprgc_63 182361 10.21
## iprgc_64 72737 7.10
## iprgc_65 58167 7.67
## iprgc_67 108729 8.15
## iprgc_68 207235 12.00
## iprgc_69 9877 1.70
## iprgc_70 63502 7.06
## iprgc_71 152436 8.98
## iprgc_72 246982 11.13
## iprgc_73 112863 10.09
## iprgc_74 165241 6.98
## iprgc_75 96091 7.52
## iprgc_76 199842 7.78
## iprgc_77 62228 8.34
## iprgc_81 77689 7.16
## iprgc_82 135429 6.84
## iprgc_83 80352 6.40
## iprgc_84 123392 8.46
## iprgc_85 28569 5.28
## iprgc_86 13164 2.05
## iprgc_87 18367 2.34
## iprgc_88 61380 4.59
## iprgc_89 54031 4.54
## iprgc_90 13601 1.60
## iprgc_91 7450 1.52
## iprgc_92 20834 5.61
## iprgc_93 29031 5.39
## iprgc_94 16914 2.29
## iprgc_95 22019 5.58
## iprgc_96 5493 3.70
## iprgc_97 8510 4.24
## iprgc_98 25539 3.24
## iprgc_99 6598 1.34
## iprgc_100 13043 1.94
## iprgc_101 3309 1.47
## iprgc_102 11792 3.22
## iprgc_104 64300 3.96
## iprgc_105 27174 2.46
## iprgc_106 12427 1.45
## iprgc_107 30617 1.83
## iprgc_108 16803 1.22
## iprgc_109 32087 1.92
## iprgc_110 20754 5.02
## iprgc_111 8062 2.43
## iprgc_112 7478 4.01
## iprgc_113 62800 4.36
## iprgc_114 11141 3.34
## iprgc_115 30362 2.60
## iprgc_116 17307 2.28
## iprgc_117 10292 2.18
## iprgc_118 7457 1.51
## iprgc_119 6462 1.75
## iprgc_120 25071 1.74
## iprgc_121 48212 2.58
## iprgc_122 17048 2.97
## iprgc_123 86504 3.33
## iprgc_124 41027 3.35
## iprgc_125 21674 2.79
## iprgc_126 50917 4.12
## iprgc_127 38479 4.85
## iprgc_128 15517 3.37
## iprgc_129 37915 4.18
## hisat_genome_input_reads hisat_genome_single_concordant
## iprgc_62 21454044 6916109
## iprgc_63 15326358 4800437
## iprgc_64 18003256 10070534
## iprgc_65 20929623 11589175
## iprgc_67 15456881 6852704
## iprgc_68 19666257 6195592
## iprgc_69 16552894 12430776
## iprgc_70 13740575 4854312
## iprgc_71 15525499 6307451
## iprgc_72 17094474 7451652
## iprgc_73 15794837 6837350
## iprgc_74 15775224 10294290
## iprgc_75 16328183 10436993
## iprgc_76 24180028 16183985
## iprgc_77 17538867 5292791
## iprgc_81 16532098 7331009
## iprgc_82 15878311 7230838
## iprgc_83 16082507 10368657
## iprgc_84 15958920 10423523
## iprgc_85 23680086 7062000
## iprgc_86 20389649 9744114
## iprgc_87 27526146 6400394
## iprgc_88 29895877 7820843
## iprgc_89 23361256 9311252
## iprgc_90 31543306 15756309
## iprgc_91 15758794 9961930
## iprgc_92 22313362 8488862
## iprgc_93 20934807 7567968
## iprgc_94 28090789 7057050
## iprgc_95 23462476 7263554
## iprgc_96 14804663 7876687
## iprgc_97 22863130 9533254
## iprgc_98 19915507 9711144
## iprgc_99 14724866 11249606
## iprgc_100 32782165 10087273
## iprgc_101 15390215 9017104
## iprgc_102 28578632 13515552
## iprgc_104 30192805 9350995
## iprgc_105 24528770 10945760
## iprgc_106 30802656 11899403
## iprgc_107 42462593 8808064
## iprgc_108 28855525 11980044
## iprgc_109 21695810 14589076
## iprgc_110 22927463 9892455
## iprgc_111 18467297 12034265
## iprgc_112 19036600 13305746
## iprgc_113 27601259 10183581
## iprgc_114 18638104 12163422
## iprgc_115 17237384 11843723
## iprgc_116 17447459 13256126
## iprgc_117 17248353 11047279
## iprgc_118 21315056 12744260
## iprgc_119 14188002 10134357
## iprgc_120 31820880 15877669
## iprgc_121 27191863 13093932
## iprgc_122 19422096 14110700
## iprgc_123 40849547 8776567
## iprgc_124 36378912 9684862
## iprgc_125 16749234 11928896
## iprgc_126 22769305 12094389
## iprgc_127 23091824 13279963
## iprgc_128 18824955 11728805
## iprgc_129 22427478 12119656
## hisat_genome_multi_concordant hisat_genome_single_all
## iprgc_62 2449957 2717057
## iprgc_63 1878557 1916047
## iprgc_64 1696641 1839841
## iprgc_65 1959086 1724647
## iprgc_67 1833302 1796579
## iprgc_68 2199833 2603068
## iprgc_69 1371549 740962
## iprgc_70 1398244 1487798
## iprgc_71 2081493 1848749
## iprgc_72 2373441 1919972
## iprgc_73 2140999 2007277
## iprgc_74 1585605 1563688
## iprgc_75 1752174 1352026
## iprgc_76 2802437 1652868
## iprgc_77 1574651 1485198
## iprgc_81 1957318 1652547
## iprgc_82 1695064 1278480
## iprgc_83 3550991 787207
## iprgc_84 2716482 1016602
## iprgc_85 3070475 4982006
## iprgc_86 1584336 3504856
## iprgc_87 2702903 6643829
## iprgc_88 3571442 6566202
## iprgc_89 2670299 4268199
## iprgc_90 2185011 5130035
## iprgc_91 1031455 1952703
## iprgc_92 2227319 4338434
## iprgc_93 1902893 4066044
## iprgc_94 2253262 6394602
## iprgc_95 2462976 4802887
## iprgc_96 1124034 2018582
## iprgc_97 2106698 4168566
## iprgc_98 1646039 2939674
## iprgc_99 899864 1039173
## iprgc_100 2458941 7583206
## iprgc_101 1046189 2151316
## iprgc_102 2414831 4844907
## iprgc_104 3576922 5498302
## iprgc_105 2247030 3714870
## iprgc_106 1745274 5678135
## iprgc_107 3664704 10968566
## iprgc_108 1538013 5173396
## iprgc_109 1554923 2161895
## iprgc_110 3310736 3360928
## iprgc_111 1363554 1699645
## iprgc_112 1364323 1664098
## iprgc_113 3780807 4894243
## iprgc_114 1218954 2151331
## iprgc_115 1353297 1746660
## iprgc_116 1179429 1301001
## iprgc_117 1171597 2019548
## iprgc_118 1398283 2821889
## iprgc_119 912577 1254331
## iprgc_120 2367026 5029094
## iprgc_121 2162058 4353717
## iprgc_122 1539523 1540853
## iprgc_123 4981110 9526129
## iprgc_124 4238972 7831340
## iprgc_125 1177569 1438407
## iprgc_126 2401918 2989185
## iprgc_127 2143572 2771095
## iprgc_128 1564536 2333679
## iprgc_129 2145404 3144006
## hisat_genome_multi_all hisat_unmapped hisat_genome_percent_log
## iprgc_62 1394098 19607017 54.30
## iprgc_63 746537 14321178 53.28
## iprgc_64 757797 9447512 73.76
## iprgc_65 757000 11613547 72.26
## iprgc_67 543814 10918707 64.68
## iprgc_68 1201766 18303482 53.46
## iprgc_69 185889 4062569 87.73
## iprgc_70 534908 12784616 53.48
## iprgc_71 483274 11748901 62.16
## iprgc_72 553598 11797040 65.49
## iprgc_73 407155 10978062 65.25
## iprgc_74 262180 5732790 81.83
## iprgc_75 377197 6140625 81.20
## iprgc_76 466627 7631325 84.22
## iprgc_77 738105 18898549 46.12
## iprgc_81 492616 12118069 63.35
## iprgc_82 289604 12077676 61.97
## iprgc_83 189640 3076315 90.44
## iprgc_84 303614 3896608 87.79
## iprgc_85 1452554 20424824 56.87
## iprgc_86 1495480 12892866 68.38
## iprgc_87 3090288 26937105 51.07
## iprgc_88 1484386 28707206 51.99
## iprgc_89 1227818 16934959 63.75
## iprgc_90 2221900 19522087 69.06
## iprgc_91 744446 6646337 78.91
## iprgc_92 1322029 17243053 61.36
## iprgc_93 1072098 17570892 58.03
## iprgc_94 3295727 27633663 50.81
## iprgc_95 1795153 20635750 56.02
## iprgc_96 819084 8565090 71.07
## iprgc_97 1328912 16588598 63.72
## iprgc_98 1353214 12425164 68.81
## iprgc_99 379443 3528780 88.02
## iprgc_100 3726501 28740151 56.16
## iprgc_101 920696 7309862 76.25
## iprgc_102 1526237 18438092 67.74
## iprgc_104 1246712 27517898 54.43
## iprgc_105 841851 17902417 63.51
## iprgc_106 2197041 26013024 57.77
## iprgc_107 3760329 44980319 47.04
## iprgc_108 2337996 22667018 60.72
## iprgc_109 610868 8110837 81.31
## iprgc_110 646097 15094237 67.08
## iprgc_111 450175 7798052 78.89
## iprgc_112 441038 6168522 83.80
## iprgc_113 1460623 20575232 62.73
## iprgc_114 559164 7287021 80.45
## iprgc_115 388933 5750843 83.32
## iprgc_116 259374 4250563 87.82
## iprgc_117 665641 7098103 79.42
## iprgc_118 1118876 10002775 76.54
## iprgc_119 366327 4418372 84.43
## iprgc_120 2032125 19796333 68.89
## iprgc_121 1619685 17615938 67.61
## iprgc_122 447710 5066253 86.96
## iprgc_123 3579817 40815048 50.04
## iprgc_124 2943013 33816629 53.52
## iprgc_125 302868 5337939 84.07
## iprgc_126 490034 12800087 71.89
## iprgc_127 564939 11635882 74.81
## iprgc_128 454792 8063807 78.58
## iprgc_129 632287 12279337 72.62
## hisat_observed_genes hisat_sum_genes hisat_observed_mean_exprs
## iprgc_62 14545 5642632 718.8
## iprgc_63 13695 3717242 514.1
## iprgc_64 16459 12978365 899.0
## iprgc_65 16884 14696641 1024.8
## iprgc_67 15379 7908083 670.4
## iprgc_68 14033 4602849 644.5
## iprgc_69 17008 18774803 1039.0
## iprgc_70 14833 5477804 481.6
## iprgc_71 14953 6549329 646.0
## iprgc_72 15339 7357963 751.0
## iprgc_73 15216 7125338 687.1
## iprgc_74 16166 15075002 914.5
## iprgc_75 16452 14804383 928.4
## iprgc_76 17086 22571664 1450.8
## iprgc_77 15168 5038166 524.6
## iprgc_81 14728 8652212 720.5
## iprgc_82 15629 9606265 685.1
## iprgc_83 15724 13230777 1084.3
## iprgc_84 16486 13312390 1003.4
## iprgc_85 15332 6910252 788.7
## iprgc_86 16246 14273411 880.6
## iprgc_87 14025 5647414 710.5
## iprgc_88 15103 7865435 887.4
## iprgc_89 16121 11611851 928.7
## iprgc_90 16393 23919491 1397.2
## iprgc_91 16378 16016160 849.8
## iprgc_92 15400 10312147 834.4
## iprgc_93 15058 8088854 736.8
## iprgc_94 14136 7103946 726.9
## iprgc_95 15275 7471948 757.4
## iprgc_96 16383 11278102 691.3
## iprgc_97 15520 12312129 908.0
## iprgc_98 15863 14054244 880.6
## iprgc_99 16831 17661585 933.6
## iprgc_100 15389 12256388 980.6
## iprgc_101 16561 12622526 767.6
## iprgc_102 15612 18809254 1247.5
## iprgc_104 15400 11375862 1009.0
## iprgc_105 15795 16846374 1028.5
## iprgc_106 15558 17994083 1067.9
## iprgc_107 14636 8231918 974.0
## iprgc_108 15633 18552934 1057.3
## iprgc_109 16728 24270138 1253.8
## iprgc_110 15568 13509357 1031.5
## iprgc_111 15706 19606417 1047.3
## iprgc_112 16153 20911855 1147.5
## iprgc_113 15965 12217700 1087.8
## iprgc_114 16314 19402528 1044.1
## iprgc_115 16441 19560965 1022.9
## iprgc_116 16929 20964695 1113.9
## iprgc_117 16262 18276778 953.3
## iprgc_118 16503 20885024 1105.3
## iprgc_119 16551 16774668 856.7
## iprgc_120 16564 24538069 1421.8
## iprgc_121 16368 19040231 1186.2
## iprgc_122 16794 21808522 1216.0
## iprgc_123 14501 7015938 1074.6
## iprgc_124 15405 8986063 1086.5
## iprgc_125 15940 19243970 1013.9
## iprgc_126 15122 18681748 1134.2
## iprgc_127 16250 20184962 1204.1
## iprgc_128 15505 18172993 1038.3
## iprgc_129 15339 17955093 1117.1
## hisat_observed_median_exprs
## iprgc_62 3
## iprgc_63 1
## iprgc_64 21
## iprgc_65 25
## iprgc_67 13
## iprgc_68 2
## iprgc_69 35
## iprgc_70 8
## iprgc_71 6
## iprgc_72 7
## iprgc_73 7
## iprgc_74 23
## iprgc_75 24
## iprgc_76 43
## iprgc_77 8
## iprgc_81 9
## iprgc_82 15
## iprgc_83 26
## iprgc_84 22
## iprgc_85 12
## iprgc_86 24
## iprgc_87 2
## iprgc_88 8
## iprgc_89 17
## iprgc_90 33
## iprgc_91 26
## iprgc_92 14
## iprgc_93 13
## iprgc_94 2
## iprgc_95 10
## iprgc_96 21
## iprgc_97 18
## iprgc_98 14
## iprgc_99 35
## iprgc_100 8
## iprgc_101 23
## iprgc_102 28
## iprgc_104 20
## iprgc_105 28
## iprgc_106 13
## iprgc_107 4
## iprgc_108 15
## iprgc_109 41
## iprgc_110 22
## iprgc_111 38
## iprgc_112 33
## iprgc_113 24
## iprgc_114 27
## iprgc_115 34
## iprgc_116 46
## iprgc_117 30
## iprgc_118 38
## iprgc_119 31
## iprgc_120 38
## iprgc_121 29
## iprgc_122 44
## iprgc_123 4
## iprgc_124 12
## iprgc_125 31
## iprgc_126 30
## iprgc_127 34
## iprgc_128 37
## iprgc_129 39
## hisat_count_table
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/40hisat_mm39_112/mm39_112_genome-paired_sreverse_gene_ID.count.xz
## umi_extract_r1_output
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/01umi_tools/r1_extracted.fastq.gz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/01umi_tools/r1_extracted.fastq.gz
## umi_extract_matches
## iprgc_62 21454044
## iprgc_63 15326358
## iprgc_64 18003256
## iprgc_65 20929623
## iprgc_67 15456881
## iprgc_68 19666257
## iprgc_69 16552894
## iprgc_70 13740575
## iprgc_71 15525499
## iprgc_72 17094474
## iprgc_73 15794837
## iprgc_74 15775224
## iprgc_75 16328183
## iprgc_76 24180028
## iprgc_77 17538867
## iprgc_81 16532098
## iprgc_82 15878311
## iprgc_83 16082507
## iprgc_84 15958920
## iprgc_85 23680086
## iprgc_86 20389649
## iprgc_87 27526146
## iprgc_88 29895877
## iprgc_89 23361256
## iprgc_90 31543306
## iprgc_91 15758794
## iprgc_92 22313362
## iprgc_93 20934807
## iprgc_94 28090789
## iprgc_95 23462476
## iprgc_96 14804663
## iprgc_97 22863130
## iprgc_98 19915507
## iprgc_99 14724866
## iprgc_100 32782165
## iprgc_101 15390215
## iprgc_102 28578632
## iprgc_104 30192805
## iprgc_105 24528770
## iprgc_106 30802656
## iprgc_107 42462593
## iprgc_108 28855525
## iprgc_109 21695810
## iprgc_110 22927463
## iprgc_111 18467297
## iprgc_112 19036600
## iprgc_113 27601259
## iprgc_114 18638104
## iprgc_115 17237384
## iprgc_116 17447459
## iprgc_117 17248353
## iprgc_118 21315056
## iprgc_119 14188002
## iprgc_120 31820880
## iprgc_121 27191863
## iprgc_122 19422096
## iprgc_123 40849547
## iprgc_124 36378912
## iprgc_125 16749234
## iprgc_126 22769305
## iprgc_127 23091824
## iprgc_128 18824955
## iprgc_129 22427478
## umi_dedup_output_bam
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/04umi_dedup/umi_tools_deduplicated.bam
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/04umi_dedup/umi_tools_deduplicated.bam
## umi_dedup_output_count
## iprgc_62 preprocessing/umd_sequenced/iprgc_62/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_63 preprocessing/umd_sequenced/iprgc_63/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_64 preprocessing/umd_sequenced/iprgc_64/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_65 preprocessing/umd_sequenced/iprgc_65/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_67 preprocessing/umd_sequenced/iprgc_67/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_68 preprocessing/umd_sequenced/iprgc_68/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_69 preprocessing/umd_sequenced/iprgc_69/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_70 preprocessing/umd_sequenced/iprgc_70/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_71 preprocessing/umd_sequenced/iprgc_71/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_72 preprocessing/umd_sequenced/iprgc_72/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_73 preprocessing/umd_sequenced/iprgc_73/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_74 preprocessing/umd_sequenced/iprgc_74/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_75 preprocessing/umd_sequenced/iprgc_75/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_76 preprocessing/umd_sequenced/iprgc_76/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_77 preprocessing/umd_sequenced/iprgc_77/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_81 preprocessing/umd_sequenced/iprgc_81/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_82 preprocessing/umd_sequenced/iprgc_82/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_83 preprocessing/umd_sequenced/iprgc_83/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_84 preprocessing/umd_sequenced/iprgc_84/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_85 preprocessing/umd_sequenced/iprgc_85/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_86 preprocessing/umd_sequenced/iprgc_86/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_87 preprocessing/umd_sequenced/iprgc_87/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_88 preprocessing/umd_sequenced/iprgc_88/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_89 preprocessing/umd_sequenced/iprgc_89/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_90 preprocessing/umd_sequenced/iprgc_90/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_91 preprocessing/umd_sequenced/iprgc_91/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_92 preprocessing/umd_sequenced/iprgc_92/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_93 preprocessing/umd_sequenced/iprgc_93/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_94 preprocessing/umd_sequenced/iprgc_94/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_95 preprocessing/umd_sequenced/iprgc_95/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_96 preprocessing/umd_sequenced/iprgc_96/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_97 preprocessing/umd_sequenced/iprgc_97/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_98 preprocessing/umd_sequenced/iprgc_98/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_99 preprocessing/umd_sequenced/iprgc_99/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_100 preprocessing/umd_sequenced/iprgc_100/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_101 preprocessing/umd_sequenced/iprgc_101/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_102 preprocessing/umd_sequenced/iprgc_102/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_104 preprocessing/umd_sequenced/iprgc_104/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_105 preprocessing/umd_sequenced/iprgc_105/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_106 preprocessing/umd_sequenced/iprgc_106/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_107 preprocessing/umd_sequenced/iprgc_107/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_108 preprocessing/umd_sequenced/iprgc_108/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_109 preprocessing/umd_sequenced/iprgc_109/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_110 preprocessing/umd_sequenced/iprgc_110/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_111 preprocessing/umd_sequenced/iprgc_111/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_112 preprocessing/umd_sequenced/iprgc_112/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_113 preprocessing/umd_sequenced/iprgc_113/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_114 preprocessing/umd_sequenced/iprgc_114/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_115 preprocessing/umd_sequenced/iprgc_115/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_116 preprocessing/umd_sequenced/iprgc_116/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_117 preprocessing/umd_sequenced/iprgc_117/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_118 preprocessing/umd_sequenced/iprgc_118/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_119 preprocessing/umd_sequenced/iprgc_119/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_120 preprocessing/umd_sequenced/iprgc_120/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_121 preprocessing/umd_sequenced/iprgc_121/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_122 preprocessing/umd_sequenced/iprgc_122/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_123 preprocessing/umd_sequenced/iprgc_123/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_124 preprocessing/umd_sequenced/iprgc_124/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_125 preprocessing/umd_sequenced/iprgc_125/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_126 preprocessing/umd_sequenced/iprgc_126/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_127 preprocessing/umd_sequenced/iprgc_127/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_128 preprocessing/umd_sequenced/iprgc_128/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## iprgc_129 preprocessing/umd_sequenced/iprgc_129/outputs/04umi_dedup/umi_tools_deduplicated_sreverse_gene_ID.count.xz
## umi_dedup_chimeric umi_dedup_num_reads_in umi_dedup_num_reads_out
## iprgc_62 1452937 18614709 8648625
## iprgc_63 709025 13297062 6169628
## iprgc_64 867058 17895655 12438709
## iprgc_65 825134 20305778 14650070
## iprgc_67 0 14911704 6244396
## iprgc_68 1246928 16877415 8001913
## iprgc_69 224846 18312482 13626331
## iprgc_70 0 11291811 4315031
## iprgc_71 0 14899869 6142703
## iprgc_72 0 17508994 8086389
## iprgc_73 0 15705128 7262328
## iprgc_74 444838 16982732 10297731
## iprgc_75 471376 17837101 11666895
## iprgc_76 548725 27435832 17919454
## iprgc_77 0 12622691 5641702
## iprgc_81 0 15696204 4325269
## iprgc_82 0 14420683 5989004
## iprgc_83 278840 20500536 6520845
## iprgc_84 382725 19718919 11961194
## iprgc_85 0 21919740 6297631
## iprgc_86 1662182 19427925 11226412
## iprgc_87 3002303 24375432 6943848
## iprgc_88 0 25825973 7683133
## iprgc_89 1288308 22230551 9988741
## iprgc_90 2506186 29561758 16430666
## iprgc_91 805952 15730918 11449267
## iprgc_92 0 20434181 6517215
## iprgc_93 0 18285032 4763200
## iprgc_94 3231382 23478066 7569580
## iprgc_95 0 20934229 6494203
## iprgc_96 716527 14087698 9401463
## iprgc_97 1230958 20902762 6972570
## iprgc_98 1347661 18716405 10937773
## iprgc_99 428519 15381926 12713186
## iprgc_100 3861642 29132038 10539058
## iprgc_101 946176 15478210 10762654
## iprgc_102 1500337 26398889 7615304
## iprgc_104 0 25659015 5958853
## iprgc_105 0 21634779 7136486
## iprgc_106 2296611 24666207 11852081
## iprgc_107 0 33732409 8324107
## iprgc_108 2555016 23911870 12383587
## iprgc_109 662661 21753591 15616405
## iprgc_110 0 22498630 5560116
## iprgc_111 0 17938608 4975228
## iprgc_112 0 19103684 8418311
## iprgc_113 0 26995038 7519015
## iprgc_114 594219 18353136 11277987
## iprgc_115 490334 17708595 12360074
## iprgc_116 351840 18170428 13514017
## iprgc_117 657983 17093633 12218494
## iprgc_118 1192045 20812806 13909310
## iprgc_119 354617 14328357 10869600
## iprgc_120 2098677 29739491 15991456
## iprgc_121 1844894 25274997 14082779
## iprgc_122 470276 20455761 13355865
## iprgc_123 3524658 35613060 8785532
## iprgc_124 3036013 31992425 7611305
## iprgc_125 367910 17181632 6236874
## iprgc_126 0 22064620 4360402
## iprgc_127 0 22219789 10051459
## iprgc_128 0 18785320 4216887
## iprgc_129 0 21729414 3911603
## umi_dedup_pct_reads umi_dedup_deduplicated_positions
## iprgc_62 0.465 1753865
## iprgc_63 0.464 1071878
## iprgc_64 0.695 4373152
## iprgc_65 0.721 5419451
## iprgc_67 0.419 1850476
## iprgc_68 0.474 1436590
## iprgc_69 0.744 6138409
## iprgc_70 0.382 1254243
## iprgc_71 0.412 1571654
## iprgc_72 0.462 2176556
## iprgc_73 0.462 1999845
## iprgc_74 0.606 3551834
## iprgc_75 0.654 4098587
## iprgc_76 0.653 6129190
## iprgc_77 0.447 1562142
## iprgc_81 0.276 1260702
## iprgc_82 0.415 2056722
## iprgc_83 0.318 2151073
## iprgc_84 0.607 3990543
## iprgc_85 0.287 1633097
## iprgc_86 0.578 4040213
## iprgc_87 0.285 992104
## iprgc_88 0.297 1747176
## iprgc_89 0.449 3055929
## iprgc_90 0.556 5517995
## iprgc_91 0.728 4836261
## iprgc_92 0.319 1974476
## iprgc_93 0.260 1639813
## iprgc_94 0.322 1407175
## iprgc_95 0.310 1633630
## iprgc_96 0.667 4043448
## iprgc_97 0.334 2174736
## iprgc_98 0.584 3780285
## iprgc_99 0.827 5930421
## iprgc_100 0.362 2736945
## iprgc_101 0.695 5000352
## iprgc_102 0.288 2468613
## iprgc_104 0.232 1748672
## iprgc_105 0.330 2684344
## iprgc_106 0.480 3598525
## iprgc_107 0.247 1445521
## iprgc_108 0.518 3874112
## iprgc_109 0.718 5888344
## iprgc_110 0.247 1935173
## iprgc_111 0.277 2273128
## iprgc_112 0.441 3260409
## iprgc_113 0.279 2452306
## iprgc_114 0.614 4137689
## iprgc_115 0.698 4863270
## iprgc_116 0.744 6018124
## iprgc_117 0.715 4461309
## iprgc_118 0.668 4602072
## iprgc_119 0.759 4348791
## iprgc_120 0.538 5227916
## iprgc_121 0.557 4731564
## iprgc_122 0.653 5194944
## iprgc_123 0.247 1224820
## iprgc_124 0.238 1735561
## iprgc_125 0.363 3711886
## iprgc_126 0.198 1740851
## iprgc_127 0.452 3552168
## iprgc_128 0.224 2102741
## iprgc_129 0.180 1607974
## umi_dedup_mean_umi_per_pos umi_dedup_max_umi_per_pos time_geno_loc
## iprgc_62 8.60 38924 p15_het_dlgn
## iprgc_63 9.86 39440 p15_het_dlgn
## iprgc_64 3.59 27371 p15_het_retina
## iprgc_65 3.34 27862 p15_het_retina
## iprgc_67 4.54 24406 p15_het_scn
## iprgc_68 9.68 45454 p15_ko_dlgn
## iprgc_69 2.38 15393 p15_ko_retina
## iprgc_70 4.62 20251 p15_ko_scn
## iprgc_71 6.12 36072 p15_wt_dlgn
## iprgc_72 5.93 37992 p15_wt_dlgn
## iprgc_73 5.75 40737 p15_wt_dlgn
## iprgc_74 3.63 25472 p15_wt_retina
## iprgc_75 3.53 28830 p15_wt_retina
## iprgc_76 3.62 38280 p15_wt_retina
## iprgc_77 5.18 23257 p15_wt_scn
## iprgc_81 4.63 22526 p08_wt_dlgn
## iprgc_82 3.71 21315 p08_wt_dlgn
## iprgc_83 3.96 53471 p08_wt_retina
## iprgc_84 3.92 57014 p08_wt_retina
## iprgc_85 5.78 45940 p08_ko_scn
## iprgc_86 3.89 46855 p08_ko_retina
## iprgc_87 16.31 56654 p08_ko_dlgn
## iprgc_88 7.90 55449 p08_het_dlgn
## iprgc_89 4.95 50112 p08_het_scn
## iprgc_90 4.15 55133 p08_het_retina
## iprgc_91 2.83 40894 p08_wt_retina
## iprgc_92 4.63 45886 p08_wt_dlgn
## iprgc_93 3.80 36240 p08_wt_scn
## iprgc_94 11.52 55625 p15_het_dlgn
## iprgc_95 6.17 47815 p15_het_scn
## iprgc_96 2.94 47871 p15_het_retina
## iprgc_97 4.38 43996 p15_wt_scn
## iprgc_98 3.95 49407 p15_wt_dlgn
## iprgc_99 2.33 24195 p15_wt_retina
## iprgc_100 7.43 58830 p15_ko_dlgn
## iprgc_101 2.66 37636 p15_ko_retina
## iprgc_102 4.06 48224 p15_ko_scn
## iprgc_104 4.96 44516 p08_het_dlgn
## iprgc_105 3.51 47370 p08_het_dlgn
## iprgc_106 4.79 54693 p15_het_dlgn
## iprgc_107 12.65 63093 p08_ko_dlgn
## iprgc_108 4.54 51083 p15_ko_dlgn
## iprgc_109 3.08 40990 p08_wt_retina
## iprgc_110 3.68 38373 p08_het_scn
## iprgc_111 2.50 29514 p08_het_scn
## iprgc_112 2.93 33820 p15_het_scn
## iprgc_113 4.20 48638 p08_ko_scn
## iprgc_114 3.13 40491 p15_ko_scn
## iprgc_115 2.93 37559 p08_wt_retina
## iprgc_116 2.46 30096 p15_wt_retina
## iprgc_117 3.22 44668 p08_het_retina
## iprgc_118 3.69 44346 p08_het_retina
## iprgc_119 2.80 32754 p15_het_retina
## iprgc_120 4.14 54664 p08_ko_retina
## iprgc_121 4.11 52455 p08_ko_retina
## iprgc_122 2.92 43252 p15_ko_retina
## iprgc_123 16.25 63117 p08_ko_dlgn
## iprgc_124 7.01 56208 p08_ko_scn
## iprgc_125 1.85 20841 p08_wt_dlgn
## iprgc_126 3.13 24582 p08_wt_dlgn
## iprgc_127 3.52 36871 p15_wt_dlgn
## iprgc_128 2.35 34708 p08_wt_scn
## iprgc_129 2.91 24561 p08_wt_scn
##
## $factors
## [1] "genotype_atb" "location_atb" "time_atb"
##
## $observed_nodes
## [1] "het" "het dlgn" "het dlgn p08" "het dlgn p15"
## [5] "het retina" "het retina p08" "het retina p15" "het scn"
## [9] "het scn p08" "het scn p15" "ko" "ko dlgn"
## [13] "ko dlgn p08" "ko dlgn p15" "ko retina" "ko retina p08"
## [17] "ko retina p15" "ko scn" "ko scn p08" "ko scn p15"
## [21] "wt" "wt dlgn" "wt dlgn p08" "wt dlgn p15"
## [25] "wt retina" "wt retina p08" "wt retina p15" "wt scn"
## [29] "wt scn p08" "wt scn p15"
##
## $ggplot
##
## attr(,"class")
## [1] "hpgltools::meta_sankey"Here is Theresa’s text, recall once again that I do not have some of these older samples (iprgc_62):
PC1 vs PC2 identifies retina vs axon is still the main component of variation. We do see though that in the PC2 direction, we see with the new samples added, we don’t see separation based on axonal targets (dLGN vs SCN). In the PC1 vs PC3 plot, we see that it’s PC3 where we start to see variation correlated with axonal compartment. Let’s look at PC1 vs PC2 colored by batch (when they were processed/sequenced) to see if that is what is contributing so much variation in PC2.
Side note: ipRGC 62 seems like an odd ball. This seems to me like it should have been a dLGN P08 sample. Is there any possibility this got mislabeled early on? I went back and double checked to see if all my processing is correct and it indeed was labeled an SCN P15 from the time I got the samples, and it is indeed.
13 DE
I now switched to Theresa’s document ‘WORKING_axonTRAP…’ and will start pulling sections from it. I am reasonably certain I have reasonably similar sample distributions, so I presume I can invoke similar/identical calls for DESeq and friends.
13.1 p8 retinas
In the block immediately before the DE analyses, Theresa created a subset expressionset of only p08 retinas. Thus this initial DE I assume will be used to subtract for the SCN/DLGN analyses that follow. (I guess I could read ahead and find out, but no! I want to be a blank slate)
Theresa’s primary workflow makes heavy use of DESeq2 (Love, Huber, and Anders (2014)) and sva (Leek et al. (2012)). In some(most?) of Theresa’s invocations of the all_pairwise() function, she excludes the other methods that it performs. In this workbook, I left those methods on, thus we can evaluate the relative performance DESeq2 vs. some (all? I may have disabled EBSeq/dream because they were taking too long) of the following:
- limma: (Ritchie et al. (2015)) (among other references) originally written for microarrays.
- EdgeR: (Robinson, McCarthy, and Smyth (2010)), which shares many assumptions with DESeq2.
- EBSeq: (Leng et al. (2013)), because I have a soft spot for any Bayesian method.
- Noiseq: (Tarazona et al. (2011)), which seeks to directly model variance in an RNASeq dataset and use that to improve the sensitivity of the result, much like:
- Dream: (Gabriel E. Hoffman and Roussos (2020)), written by the same authors (and uses very similar logic) as one of my favorite tools, variancePartition(Gabriel E. Hoffman and Schadt (2016)).
mm38_p8_retina <- subset_se(mm38_hisat_v3, subset = "time_atb=='p08' & location_atb=='retina'")
mm_normal_p8_ret_de <- all_pairwise(mm38_p8_retina, model_svs = "svaseq",
model_fstring = "~ 0 + condition", filter = TRUE)## het_retina ko_retina wt_retina
## 3 3 5## Removing 12001 low-count genes (13424 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 2593 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## conditions
## het_retina ko_retina wt_retina
## 3 3 5## conditions
## het_retina ko_retina wt_retina
## 3 3 5## conditions
## het_retina ko_retina wt_retina
## 3 3 5
## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 3 comparisons.There seems to be a discrepency with previous iterations of this. Let us simplify to just doing deseq and find what is causing it. In my previous iteration, I got 3632 genes in the unique(c()) or het+ko.
deseq_only <- deseq_pairwise(mm38_p8_retina, model_svs = "svaseq",
model_fstring = default_fstring, filter = TRUE)## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimatesdeseq_hetkeeper_genes <- deseq_only$all_tables$wt_retina_vs_het_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_kokeeper_genes <- deseq_only$all_tables$wt_retina_vs_ko_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_keepergenes <- unique(c(rownames(deseq_hetkeeper_genes),
rownames(deseq_kokeeper_genes)))
length(deseq_keepergenes)## [1] 3632deseq_pair_hetkeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_het_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_pair_kokeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_ko_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_pair_keepergenes <- unique(c(rownames(deseq_pair_hetkeeper_genes),
rownames(deseq_pair_kokeeper_genes)))
length(deseq_pair_keepergenes)## [1] 3632The following invocation performed by Theresa filters the wt/het comparison for only those genes which increased by at least 0.25 logFC with a significant adjusted p-value. I assume that this is to use the wt samples as a translational control for the ket/ko comparisons; I am therefore thinking that for my purposes, I will therefore separate the contrasts from all_pairwise do this in a stepwise fashion…
The block of code immediately following Theresa’s all_pairwise() invocation is a little confusing for me and warrants some explanation by me to me in the hopes that I do not misunderstand what is happening and the goals therein.
I think I can safely assume that the goal here is to pull out the IDs which increased in het with respect to wild type; even if by a small margin, as long as it is statistically significant vis a vis the adjusted p-value.
I am going to perform what I think is the same thing in a slightly different fashion so that I can share a copy of the results with whomever is interested. I will also repeat Theresa’s invocation and prove to myself that I understood and got the same answer.
wt_het_keeper <- list("het_vs_wt" = c("het_retina", "wt_retina"))
het_wt_table <- combine_de_tables(
mm_normal_p8_ret_de, keepers = wt_het_keeper, label_column = label_column,
excel = "03theresa_comparison_excel/het_retina_control.xlsx")## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.wanted_sig <- extract_significant_genes(
het_wt_table, lfc = 0.25, according_to = "deseq",
excel = "03theresa_comparison_excel/het_retina_control-sig.xlsx")
wanted_het_increased <- wanted_sig[["deseq"]][["ups"]][["het_vs_wt"]]
increased_het_genes <- rownames(wanted_het_increased)Here are Theresa’s next lines:
mm_de_normal_p8_ret <- mm_normal_p8_ret_de
hetkeeper_genes <- mm_de_normal_p8_ret$deseq$all_tables$wt_retina_vs_het_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
kokeeper_genes <- mm_de_normal_p8_ret$deseq$all_tables$wt_retina_vs_ko_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(hetkeeper_genes),
rownames(kokeeper_genes)))
## We know a priori that Opn4 is ENSMUSG00000021799
## I do not expect to see it in this set, it should be higher in wt
## retina vs ko retina by a significant margin.
"ENSMUSG00000021799" %in% keepergenes## [1] TRUEI think Rashmi made a compelling point which illustrates why we likely should expect the expression of Opn4 to significantly higher in the heterozygotes vs wild-type:
- Recall that the assay is using the immunopurification to extract the RNAs.
- The wt samples do not have the cre recombinase and therefore no HA and therefore everything we observe is due to non-specific binding.
- The set of genes observed due to non-specific binding is different than het/ko (presumably a larger number of relatively small values), therefore the divisor performed in the cpm is likely relativly large resulting in normalized values getting shifted down to some degree.
- On the other hand, the set of genes observed in het/ko are more likely to be only the specific binders and therefore smaller (I can test this) resulting in a smaller divisor and slight shifting up in the cpm values.
This makes me wonder if any normalization methods exist which do something like multiply the values by some value related to the proportion of observed genes; and/or if this is a good/bad/indifferent idea.
Also, just a note for me to remember: RPL22, not RPS22, for some reason I keep thinking the small subunit.
13.2 Prove I understood
hetkeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_het_retina %>%
filter(logFC <= -0.25 & adj.P.Val <= 0.05)
testthat::expect_true(nrow(hetkeeper_genes) == length(increased_het_genes))
taa_keepers <- sort(rownames(hetkeeper_genes))
atb_keepers <- sort(increased_het_genes)
testthat::expect_equal(taa_keepers, atb_keepers)Yay! I can read! Now let us repeat for the KO vs wt
wt_ko_keeper <- list("ko_vs_wt" = c("ko_retina", "wt_retina"))
ko_wt_table <- combine_de_tables(
mm_normal_p8_ret_de, keepers = wt_ko_keeper, label_column = label_column,
excel = "03theresa_comparison_excel/ko_retina_control.xlsx")## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.wanted_sig <- extract_significant_genes(
ko_wt_table, lfc = 0.25, according_to = "deseq",
excel = "03theresa_comparison_excel/ko_retina_control-sig.xlsx")
wanted_ko_increased <- wanted_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
increased_ko_genes <- rownames(wanted_ko_increased)The next thing performed in Theresa’s document is a unique(concatenation of these two gene groups), thus sucking up every gene which was significantly higher in either the knockout or heterzyous samples with respect to wild-type.
This was followed by a couple of merge operations of a little bit of the annotation data; I am not sure I understand the goal yet…
Here is her code. I copied the annotation ‘mgi_symbol’ column to ‘external_gene_name’ so that I need not change any of her code. I am assuming this is the appropriate column of interest, I do not know this for certain, but it seems quite likely.
While I am at it, here is the set_sig_limma() function from Theresa’s helpers.R
set_sig_limma <- function(limma_tbl, factors = NULL) {
if (is.null(factors)) {
#set significance for plotting colors
limma_tbl$Significance <- NA
limma_tbl[abs(limma_tbl$logFC) < 1 | limma_tbl$adj.P.Val > .05, "Significance"] <- "Not \nEnriched"
limma_tbl[limma_tbl$logFC >= 1 & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- "Disease \nUpregulated"
limma_tbl[limma_tbl$logFC <= -1 & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- "Disease \nDownregulated"
limma_tbl$Significance <- factor(limma_tbl$Significance, levels = c("Upregulated", "Downregulated", "Not \nEnriched"))
} else {
limma_tbl$Significance <- NA
limma_tbl[abs(limma_tbl$logFC) < 1 | limma_tbl$adj.P.Val > .05, "Significance"] <- "Not \nEnriched"
if(nrow(limma_tbl[limma_tbl$logFC >= 1 & limma_tbl$adj.P.Val <= .05, ]) != 0) {
limma_tbl[limma_tbl$logFC >= 1 & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- factors[1]
}
if (nrow(limma_tbl[limma_tbl$logFC <= -1 & limma_tbl$adj.P.Val <= .05, ]) != 0) {
limma_tbl[limma_tbl$logFC <= -1 & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- factors[2]
}
limma_tbl$Significance <- factor(limma_tbl$Significance, levels = c(factors, "Not \nEnriched"))
}
return(limma_tbl)
}13.2.1 Combining het/wt and ko/wt
mm_annot[["external_gene_name"]] <- mm_annot[["mgi_symbol"]]
keepergenes <- unique(c(rownames(hetkeeper_genes), rownames(kokeeper_genes)))
length(keepergenes)## [1] 3632annots_to_merge <- mm_annot %>%
select(ensembl_gene_id, external_gene_name) %>%
filter(ensembl_gene_id %in%
rownames(mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina)) %>%
distinct()
mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina <- merge(
mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina, annots_to_merge,
by.x = 0, by.y = "ensembl_gene_id", all.x = TRUE)
df <- mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina %>%
dplyr::mutate(logFC = -logFC) %>%
set_sig_limma(factors = c("Het Enriched", "KO Enriched"))My version of the above task makes use of the excludes option of combine_de_tabes. Given the set of unique gene IDs increased in the het/ko, I can ask to exlude anything not in that set. I could also have more parsimoniously directly excluded any gene ID increased in the wt samples. But, Theresa already provided the code to do the former, so it will be less typing/opportunity for silly mistakes to just do that.
both_increased_genes <- unique(c(increased_het_genes, increased_ko_genes))
## arbitrairly grab all genes from one of my data structures.
all_genes <- rownames(exprs(mm38_hisat_v3))
exclude_idx <- all_genes %in% both_increased_genes
summary(exclude_idx)## Mode FALSE TRUE
## logical 21793 3632My April 2025 version of this shows 21,793 and 3,632 genes in this set. Is that still true? (As of 20260311, it is!)
exclude_increased_genes <- all_genes[exclude_idx]
retina_keepers <- list(
"het_vs_wt" = c("het_retina", "wt_retina"),
"ko_vs_wt" = c("ko_retina", "wt_retina"),
"ko_vs_het" = c("ko_retina", "het_retina"))
## A reminder to myself: there is also a parameter 'wanted_genes'
## which does effectively the same thing as excludes in this context;
## excludes was originally written to allow flexible, keyword-based
## exclusion.
p8_retina_tables <- combine_de_tables(
mm_normal_p8_ret_de, keepers = retina_keepers,
wanted_genes = both_increased_genes, label_column = label_column,
excel = glue("03theresa_comparison_excel/p8_retina_kept_genes_increased_in_wt_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.p8_retina_sig <- extract_significant_genes(
p8_retina_tables, according_to = "deseq",
excel = glue("03theresa_comparison_excel/p8_retina_kept_genes_increased_in_wt_sig-v{ver}.xlsx"))
opposite_p8_retina_tables <- combine_de_tables(
mm_normal_p8_ret_de, keepers = retina_keepers,
excludes = both_increased_genes, label_column = label_column,
excel = glue("03theresa_comparison_excel/p8_retina_removed_genes_increased_in_wt_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.
## Looking for subscript invalid names, end of extract_keepers.14 Filtering out non-specific genes and examining the results
The following is a copy/paste from Theresa containing the remaining tasks she performed and will provide the template for implementation of the final tasks.
This picks up with the lines from her notebook immediately following the invocation of ‘set_sig_limma(factors = c(“Het Enriched” …’.
For all of the remaining blocks I will copy in her code, turn off its evaluation, run the blocks manually, compare them to her notebook output, then enable each block as I ensure I understand it.
I will likely therefore introduce some small formatting changes and add some additional GSEA/enrichment tasks once the non-specific filtering is complete.
df <- df %>%
filter(Row.names %in% keepergenes)
labels_ups <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
arrange(logFC) %>%
head(n = 9)
labels_downs <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
arrange(-logFC) %>%
head(n = 11)
labels <- rbind(labels_ups, labels_downs)
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
geom_point(aes(colour = Significance), size = 4) +
geom_vline(xintercept = c(-1, 1)) +
geom_hline(yintercept = -log10(0.05)) +
theme_classic(base_size = 20) +
xlab("log2(FC)") +
ylab("-log10(p-value)") +
theme(legend.position = "right") +
scale_color_manual(values = c("#F8766D", "#00BFC4", "Grey")) +
geom_label_repel(
data = filter(df,
## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
external_gene_name %in% labels$external_gene_name),
## nudge_x = -0.5,
nudge_y = 3, max.overlaps = 15) +
xlim(c(-3, 6))
pp(file = "03theresa_comparison_images/p08_retina_DE_1312024.pdf")## Warning in pp(file = "03theresa_comparison_images/p08_retina_DE_1312024.pdf"):
## The directory: 03theresa_comparison_images does not exist, will attempt to
## create it.## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 2 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).
## write_xlsx() wrote excel/retinahet_vs_retinako_WTfiltered.xlsx.
## The cursor is on sheet first, row: 3635 column: 13.14.1 How many ups/downs
## [1] 21## [1] 6914.2 category enrichment/GSEA
regulated_genes <- res_tbl %>%
filter(adj.P.Val <= 0.05) %>%
arrange(logFC) %>%
select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
filter(abs(logFC) >= 1)
## gsea_result_ko <- gost(query = ko_genes$external_gene_name,
## organism = "mmusculus",
## evcodes = TRUE,
## ordered_query = TRUE)
gsea_result_het <- gost(query = het_enriched$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE)
##gsea_result_alldysregulated <- gost(query = alldysregulated_genes$external_gene_name,
## organism = "mmusculus",
## evcodes = TRUE,
## ordered_query = TRUE)I have a function in my package which seeks to make gProfiler queries a bit more complete and easy. Let us see how similar the result is…
rownames(alldysregulated_genes) <- alldysregulated_genes[["Row.names"]]
alldysregulated_genes[["Row.names"]] <- NULL
het_gp <- simple_gprofiler(rownames(alldysregulated_genes),
species = "mmusculus",
excel = glue("excel/het_gprofiler-v{ver}.xlsx"))
het_gp
enrichplot::dotplot(het_gp[["BP_enrich"]])
gp_pair <- enrichplot::pairwise_termsim(het_gp[["BP_enrich"]])
enrichplot::emapplot(gp_pair)
enrichplot::ssplot(gp_pair)
enrichplot::treeplot(gp_pair)
upsetplot(het_gp[["BP_enrich"]])
enrichplot::dotplot(het_gp[["REAC_enrich"]])
gp_pair <- enrichplot::pairwise_termsim(het_gp[["REAC_enrich"]])
enrichplot::emapplot(gp_pair)
enrichplot::ssplot(gp_pair)
enrichplot::treeplot(gp_pair)
upsetplot(het_gp[["REAC_enrich"]])I make a somewhat arbitrary distinction between the concepts of over-enrichment analyses and GSEA: the former (as performed by gprofiler) (Raudvere et al. (2019)) seeks to find groups of genes overrepresented in GO/reactome/etc. These groups of genes are taken exclusively from the top-n/bottom-n genes with respect to fold-change between conditions of interest; in this case most different than wt in the p08 retina ko or het samples.
With that in mind, I can invoke a similar function using the full table of DE results to get what I call the GSEA result using clusterProfiler (Yu (n.d.)). In the following block I will use the ‘all_cprofiler’ function on the data structures named ‘p8_retina_tables’ and ‘opposite_p8_retina_tables’ in order to get these GSEA results for each contrast performed (het/wt, ko/wt, het/ko). I will follow that up with ‘all_gprofiler’ which does the same, but uses gProfiler’s enrichment analyses (it will therefore include what we just looked at).
p08_retina_all_cp <- all_cprofiler(
p8_retina_sig, p8_retina_tables, orgdb = "org.Mm.eg.db", orgdb_from = orgdb_from,
excel = "03theresa_comparison_excel/cprofiler_p08_retina.xlsx")## Error : unable to find an inherited method for function 'metadata' for signature 'x = "NULL"'
## Error : unable to find an inherited method for function 'metadata' for signature 'x = "NULL"'## Error in `simple_cl[["kegg_universe"]]`:
## ! subscript out of bounds## Error in `h()`:
## ! error in evaluating the argument 'object' in selecting a method for function 'dotplot': object 'p08_retina_all_cp' not found## Error in `h()`:
## ! error in evaluating the argument 'gse' in selecting a method for function 'plot_topn_gsea': object 'p08_retina_all_cp' not foundpp(file = "03theresa_comparison_images/gsea_p08_retina_ko_vs_het_top_hit.pdf")
p08_topn_gsea[["GO_ko_vs_het_up"]][[1]]## Error:
## ! object 'p08_topn_gsea' not found## Error:
## ! object 'p08_topn_gsea' not found## Error:
## ! object 'p08_topn_gsea' not found## Error:
## ! object 'p08_topn_gsea' not found## Error:
## ! object 'p08_topn_gsea' not found## Error:
## ! object 'p08_topn_gsea' not foundpp(file = "03theresa_comparison_images/gsea_p08_retina_het_vs_wt_top_hit.pdf")
p08_topn_gsea[["GO_het_vs_wt_up"]][[1]]## Error:
## ! object 'p08_topn_gsea' not found#gsea_ko <- gsea_result_ko[["result"]] %>%
# select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
# arrange(desc(recall)) %>%
# head(n = 10)
# gsea_plots_ko <- ggplot(gsea_ko, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
# geom_bar(stat = "identity")+
# scale_fill_continuous(low = "blue", high = "red") +
# theme_bw()+
# ylab("") +
# xlab("GSEA Score")
gsea_het <- gsea_result_het[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 10)
gsea_plots_het <- ggplot(gsea_het, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over Representation Score")
pp(file = "03theresa_comparison_images/GSEA_p08_axontrap_retinahet_upregulated_vs_retinako.pdf")
gsea_plots_het
plotted <- dev.off()
gsea_plots_het
gsea_all <- gsea_result_alldysregulated[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 10)
gsea_plots_all <- ggplot(gsea_all, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over Representation Score")
pp(file = "images/GSEA_p08_retina_axontrap_alldysregulatedgenes.pdf")
gsea_plots_all
plotted <- dev.off()15 SCN Het vs KO
It is only now that I realized we are splitting the data by location for each set of comparisons. I think that, left to my own devices, I would prefer to keep the input data structure intact, perform the somewhat larger number of contrasts, and then split up the results. Ideally this will slightly improve the fidelity of the results returned by DESeq2 and friends. But, I will run the state of Theresa’s notebook with as few changes as possible first, then add this.
15.1 PCA
I am going to skip this PCA plot for a couple of reasons: I already did a superset of it, and the subset Theresa performed is not valid given the set of samples included in my sample sheet, and figuring out the actually corresponding subset will take me forever… In addition, I want to use my mm38_hisat_v3 for everything…
mm38_subset <- subset_se(
mm38_hisat,
subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08' & location == 'scn' | sampleid == 'iprgc_03'")
mm38_norm <- normalize(mm38_subset, filter = TRUE, convert = "cpm",
transform = "log2", batch = "svaseq")
mm38_norm <- set_batches(mm38_norm, fact = "location")
mm38_norm <- set_conditions(mm38_norm, fact = "genotype")
pca_norm <- plot_pca(mm38_norm, max_overlaps = 70)
pca_norm$plotInstead I will simplify the subset and see what happens…
scn_samples <- subset_se(mm38_hisat_v3,
subset = "location_atb == 'scn'") %>%
set_batches(fact = "location_atb") %>%
set_conditions(fact = "genotype_atb", colors = color_choices[["genotype"]])## The number of samples by batch are:##
## scn
## 17## The numbers of samples by condition are:##
## het ko wt
## 6 6 5scn_norm <- normalize(scn_samples, filter = TRUE, convert = "cpm",
transform = "log2", batch = "svaseq")## Removing 11109 low-count genes (14316 remaining).## transform_counts: Found 919 values less than 0.## transform_counts: Found 919 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by het, ko, wt
## Shapes are defined by scn.
16 Library sizes post-deduplication
Theresa’s next operation was to perform libsize/nonzero plots. I already did the pre/post deduplication nonzero, here is the analagous libsize.
v2 is pre-deduplication and v3 is post.
## Library sizes of 65 samples,
## ranging from 3,717,242 to 24,538,069.
post_filter_nonzero <- plot_libsize(mm38_hisat_v3, text = FALSE)
pp(file = "01diagnostic_images/post_all_filteres_nonzero.pdf")
post_filter_nonzero[["plot"]]
plotted <- dev.off()
post_filter_nonzero## Library sizes of 63 samples,
## ranging from 1,264,475 to 10,979,038.
I am a bit concerned about some of these library sizes post-deduplication.
Let us look at the relationship between reads and duplication, which I assume will be relatively linear.
test <- colData(mm38_hisat_v3)[, c("hisat_genome_single_all", "umi_dedup_pct_reads")]
test_plot <- plot_linear_scatter(test, loess = TRUE)
test_plot[["scatter"]]## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: label.
## i This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## i Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: label.
## i This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## i Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: label.
## i This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## i Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## png
## 2Theresa also produced a density/sample plot, that might prove quite useful for these due to their significantly larger variance across samples (due to deduplication).
pp(file = "01diagnostic_images/sample_density.pdf")
mm38_density <- plot_density(loc_geno_nt)
mm38_density[["plot"]] +
theme(legend.position = "none")
plot_boxplot(loc_geno_nt)## iprgc_62 iprgc_63 iprgc_64 iprgc_65 iprgc_66 iprgc_67 iprgc_68
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.689 1.527 2.480 2.558 1.894 2.186 1.469
## median 3.713 3.521 4.413 4.445 3.909 4.021 3.539
## mean 3.711 3.556 4.250 4.279 3.916 3.995 3.573
## q3 5.460 5.314 5.914 5.904 5.705 5.638 5.333
## max 15.929 16.020 14.188 14.054 14.741 15.920 16.168
## iqr 3.771 3.788 3.434 3.345 3.811 3.452 3.864
## iqr_high 11.115 10.995 11.064 10.922 11.423 10.817 11.129
## iqr_low -5.656 -5.681 -5.151 -5.018 -5.717 -5.178 -5.796
## sd 2.426 2.434 2.309 2.285 2.439 2.309 2.439
## var 5.885 5.926 5.330 5.219 5.948 5.333 5.948
## stdvar 1.586 1.667 1.254 1.220 1.519 1.335 1.665
## iprgc_69 iprgc_70 iprgc_71 iprgc_72 iprgc_73 iprgc_74 iprgc_75
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.543 2.182 1.968 1.990 1.996 2.404 2.419
## median 4.466 4.015 3.869 3.922 3.907 4.268 4.318
## mean 4.299 3.977 3.865 3.889 3.901 4.169 4.210
## q3 5.929 5.624 5.556 5.584 5.599 5.848 5.883
## max 12.381 15.767 16.057 15.939 15.386 13.760 13.110
## iqr 3.387 3.442 3.587 3.594 3.602 3.444 3.465
## iqr_high 11.009 10.786 10.937 10.975 11.002 11.014 11.080
## iqr_low -5.080 -5.162 -5.381 -5.391 -5.403 -5.166 -5.197
## sd 2.304 2.325 2.377 2.377 2.384 2.334 2.328
## var 5.308 5.404 5.651 5.648 5.684 5.448 5.421
## stdvar 1.235 1.359 1.462 1.452 1.457 1.307 1.288
## iprgc_76 iprgc_77 iprgc_81 iprgc_82 iprgc_83 iprgc_84 iprgc_85
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.518 2.264 2.084 2.178 2.503 2.531 2.246
## median 4.379 4.066 3.814 3.984 4.354 4.457 3.959
## mean 4.264 4.025 3.848 3.996 4.221 4.288 3.938
## q3 5.908 5.654 5.476 5.617 5.852 5.961 5.531
## max 13.649 15.864 16.157 14.893 13.932 13.337 16.483
## iqr 3.389 3.390 3.392 3.439 3.349 3.430 3.285
## iqr_high 10.992 10.739 10.565 10.776 10.875 11.106 10.459
## iqr_low -5.084 -5.085 -5.088 -5.159 -5.023 -5.145 -4.928
## sd 2.299 2.295 2.320 2.320 2.298 2.317 2.234
## var 5.287 5.266 5.382 5.384 5.283 5.368 4.989
## stdvar 1.240 1.308 1.399 1.348 1.252 1.252 1.267
## iprgc_86 iprgc_87 iprgc_88 iprgc_89 iprgc_90 iprgc_91 iprgc_92
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.470 1.526 1.858 2.199 2.303 2.420 2.044
## median 4.345 3.317 3.742 4.035 4.389 4.450 3.823
## mean 4.232 3.389 3.773 4.031 4.202 4.259 3.874
## q3 5.941 4.998 5.463 5.664 5.973 5.967 5.531
## max 14.490 16.910 16.308 15.157 14.315 13.393 15.881
## iqr 3.471 3.472 3.604 3.465 3.669 3.547 3.487
## iqr_high 11.148 10.206 10.869 10.862 11.477 11.288 10.761
## iqr_low -5.207 -5.208 -5.406 -5.198 -5.504 -5.321 -5.230
## sd 2.339 2.276 2.347 2.320 2.403 2.360 2.325
## var 5.471 5.179 5.510 5.381 5.773 5.571 5.404
## stdvar 1.293 1.528 1.460 1.335 1.374 1.308 1.395
## iprgc_93 iprgc_94 iprgc_95 iprgc_96 iprgc_97 iprgc_98 iprgc_99
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.194 1.455 2.093 2.550 2.101 1.934 2.604
## median 3.971 3.413 3.819 4.423 3.892 3.878 4.541
## mean 3.924 3.486 3.853 4.270 3.934 3.908 4.335
## q3 5.516 5.208 5.455 5.926 5.576 5.660 5.976
## max 16.492 16.585 16.084 13.933 15.433 14.700 12.844
## iqr 3.322 3.753 3.362 3.377 3.475 3.726 3.371
## iqr_high 10.498 10.837 10.498 10.991 10.789 11.249 11.033
## iqr_low -4.982 -5.629 -5.043 -5.065 -5.213 -5.589 -5.057
## sd 2.253 2.401 2.276 2.305 2.332 2.424 2.303
## var 5.075 5.766 5.181 5.314 5.440 5.876 5.302
## stdvar 1.293 1.654 1.344 1.245 1.383 1.504 1.223
## iprgc_100 iprgc_101 iprgc_102 iprgc_104 iprgc_105 iprgc_106 iprgc_107
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.824 2.562 2.159 2.260 2.261 1.787 1.599
## median 3.846 4.490 4.065 4.050 4.187 3.930 3.435
## mean 3.838 4.307 4.032 4.011 4.119 3.888 3.505
## q3 5.593 5.958 5.751 5.639 5.825 5.706 5.147
## max 15.708 13.878 14.894 15.914 14.621 15.187 17.169
## iqr 3.770 3.396 3.592 3.380 3.564 3.919 3.548
## iqr_high 11.248 11.053 11.138 10.709 11.171 11.585 10.469
## iqr_low -5.654 -5.095 -5.388 -5.069 -5.346 -5.879 -5.322
## sd 2.419 2.294 2.368 2.270 2.351 2.479 2.303
## var 5.852 5.263 5.610 5.155 5.526 6.147 5.305
## stdvar 1.525 1.222 1.391 1.285 1.342 1.581 1.514
## iprgc_108 iprgc_109 iprgc_110 iprgc_111 iprgc_112 iprgc_113 iprgc_114
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.838 2.491 2.183 2.361 2.193 2.358 2.163
## median 4.026 4.483 3.966 4.274 4.066 4.150 4.078
## mean 3.945 4.302 4.002 4.184 4.080 4.108 4.065
## q3 5.784 6.029 5.657 5.855 5.808 5.711 5.801
## max 14.921 12.859 15.079 14.025 13.023 15.794 13.261
## iqr 3.946 3.538 3.473 3.493 3.614 3.353 3.638
## iqr_high 11.703 11.335 10.867 11.095 11.229 10.741 11.257
## iqr_low -5.919 -5.306 -5.210 -5.240 -5.422 -5.030 -5.457
## sd 2.486 2.354 2.336 2.335 2.386 2.267 2.400
## var 6.179 5.543 5.457 5.450 5.695 5.139 5.760
## stdvar 1.566 1.289 1.364 1.303 1.396 1.251 1.417
## iprgc_115 iprgc_116 iprgc_117 iprgc_118 iprgc_119 iprgc_120 iprgc_121
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.511 2.620 2.396 2.491 2.525 2.346 2.352
## median 4.495 4.525 4.367 4.396 4.404 4.347 4.365
## mean 4.303 4.332 4.223 4.256 4.248 4.214 4.214
## q3 6.020 5.957 5.980 5.985 5.919 5.941 5.942
## max 12.739 12.644 13.541 13.650 13.796 14.752 14.560
## iqr 3.508 3.337 3.584 3.494 3.394 3.596 3.590
## iqr_high 11.282 10.963 11.356 11.225 11.010 11.335 11.327
## iqr_low -5.262 -5.006 -5.376 -5.241 -5.091 -5.393 -5.385
## sd 2.354 2.289 2.377 2.347 2.310 2.368 2.366
## var 5.543 5.242 5.649 5.509 5.335 5.609 5.596
## stdvar 1.288 1.210 1.338 1.294 1.256 1.331 1.328
## iprgc_122 iprgc_123 iprgc_124 iprgc_125 iprgc_126 iprgc_127 iprgc_128
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.567 1.556 2.038 2.211 2.215 2.323 2.352
## median 4.453 3.322 3.745 4.085 4.082 4.172 4.198
## mean 4.282 3.409 3.761 4.045 4.037 4.138 4.128
## q3 5.915 5.004 5.325 5.709 5.733 5.838 5.782
## max 13.284 17.222 17.196 13.801 15.040 14.076 15.026
## iqr 3.348 3.447 3.287 3.498 3.518 3.515 3.430
## iqr_high 10.937 10.174 10.255 10.956 11.011 11.110 10.926
## iqr_low -5.022 -5.171 -4.930 -5.247 -5.278 -5.272 -5.145
## sd 2.300 2.242 2.196 2.354 2.363 2.348 2.316
## var 5.291 5.026 4.821 5.543 5.583 5.511 5.364
## stdvar 1.236 1.474 1.282 1.370 1.383 1.332 1.300
## iprgc_129 iprgc_130
## min 0.000 0.000
## q1 2.462 2.286
## median 4.266 4.221
## mean 4.175 4.132
## q3 5.817 5.836
## max 15.200 13.939
## iqr 3.354 3.550
## iqr_high 10.848 11.162
## iqr_low -5.031 -5.325
## sd 2.293 2.373
## var 5.258 5.632
## stdvar 1.259 1.363## Plot describing the gene distribution from a dataset.## png
## 2
## iprgc_62 iprgc_63 iprgc_64 iprgc_65 iprgc_66 iprgc_67 iprgc_68
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.689 1.527 2.480 2.558 1.894 2.186 1.469
## median 3.713 3.521 4.413 4.445 3.909 4.021 3.539
## mean 3.711 3.556 4.250 4.279 3.916 3.995 3.573
## q3 5.460 5.314 5.914 5.904 5.705 5.638 5.333
## max 15.929 16.020 14.188 14.054 14.741 15.920 16.168
## iqr 3.771 3.788 3.434 3.345 3.811 3.452 3.864
## iqr_high 11.115 10.995 11.064 10.922 11.423 10.817 11.129
## iqr_low -5.656 -5.681 -5.151 -5.018 -5.717 -5.178 -5.796
## sd 2.426 2.434 2.309 2.285 2.439 2.309 2.439
## var 5.885 5.926 5.330 5.219 5.948 5.333 5.948
## stdvar 1.586 1.667 1.254 1.220 1.519 1.335 1.665
## iprgc_69 iprgc_70 iprgc_71 iprgc_72 iprgc_73 iprgc_74 iprgc_75
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.543 2.182 1.968 1.990 1.996 2.404 2.419
## median 4.466 4.015 3.869 3.922 3.907 4.268 4.318
## mean 4.299 3.977 3.865 3.889 3.901 4.169 4.210
## q3 5.929 5.624 5.556 5.584 5.599 5.848 5.883
## max 12.381 15.767 16.057 15.939 15.386 13.760 13.110
## iqr 3.387 3.442 3.587 3.594 3.602 3.444 3.465
## iqr_high 11.009 10.786 10.937 10.975 11.002 11.014 11.080
## iqr_low -5.080 -5.162 -5.381 -5.391 -5.403 -5.166 -5.197
## sd 2.304 2.325 2.377 2.377 2.384 2.334 2.328
## var 5.308 5.404 5.651 5.648 5.684 5.448 5.421
## stdvar 1.235 1.359 1.462 1.452 1.457 1.307 1.288
## iprgc_76 iprgc_77 iprgc_81 iprgc_82 iprgc_83 iprgc_84 iprgc_85
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.518 2.264 2.084 2.178 2.503 2.531 2.246
## median 4.379 4.066 3.814 3.984 4.354 4.457 3.959
## mean 4.264 4.025 3.848 3.996 4.221 4.288 3.938
## q3 5.908 5.654 5.476 5.617 5.852 5.961 5.531
## max 13.649 15.864 16.157 14.893 13.932 13.337 16.483
## iqr 3.389 3.390 3.392 3.439 3.349 3.430 3.285
## iqr_high 10.992 10.739 10.565 10.776 10.875 11.106 10.459
## iqr_low -5.084 -5.085 -5.088 -5.159 -5.023 -5.145 -4.928
## sd 2.299 2.295 2.320 2.320 2.298 2.317 2.234
## var 5.287 5.266 5.382 5.384 5.283 5.368 4.989
## stdvar 1.240 1.308 1.399 1.348 1.252 1.252 1.267
## iprgc_86 iprgc_87 iprgc_88 iprgc_89 iprgc_90 iprgc_91 iprgc_92
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.470 1.526 1.858 2.199 2.303 2.420 2.044
## median 4.345 3.317 3.742 4.035 4.389 4.450 3.823
## mean 4.232 3.389 3.773 4.031 4.202 4.259 3.874
## q3 5.941 4.998 5.463 5.664 5.973 5.967 5.531
## max 14.490 16.910 16.308 15.157 14.315 13.393 15.881
## iqr 3.471 3.472 3.604 3.465 3.669 3.547 3.487
## iqr_high 11.148 10.206 10.869 10.862 11.477 11.288 10.761
## iqr_low -5.207 -5.208 -5.406 -5.198 -5.504 -5.321 -5.230
## sd 2.339 2.276 2.347 2.320 2.403 2.360 2.325
## var 5.471 5.179 5.510 5.381 5.773 5.571 5.404
## stdvar 1.293 1.528 1.460 1.335 1.374 1.308 1.395
## iprgc_93 iprgc_94 iprgc_95 iprgc_96 iprgc_97 iprgc_98 iprgc_99
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.194 1.455 2.093 2.550 2.101 1.934 2.604
## median 3.971 3.413 3.819 4.423 3.892 3.878 4.541
## mean 3.924 3.486 3.853 4.270 3.934 3.908 4.335
## q3 5.516 5.208 5.455 5.926 5.576 5.660 5.976
## max 16.492 16.585 16.084 13.933 15.433 14.700 12.844
## iqr 3.322 3.753 3.362 3.377 3.475 3.726 3.371
## iqr_high 10.498 10.837 10.498 10.991 10.789 11.249 11.033
## iqr_low -4.982 -5.629 -5.043 -5.065 -5.213 -5.589 -5.057
## sd 2.253 2.401 2.276 2.305 2.332 2.424 2.303
## var 5.075 5.766 5.181 5.314 5.440 5.876 5.302
## stdvar 1.293 1.654 1.344 1.245 1.383 1.504 1.223
## iprgc_100 iprgc_101 iprgc_102 iprgc_104 iprgc_105 iprgc_106 iprgc_107
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.824 2.562 2.159 2.260 2.261 1.787 1.599
## median 3.846 4.490 4.065 4.050 4.187 3.930 3.435
## mean 3.838 4.307 4.032 4.011 4.119 3.888 3.505
## q3 5.593 5.958 5.751 5.639 5.825 5.706 5.147
## max 15.708 13.878 14.894 15.914 14.621 15.187 17.169
## iqr 3.770 3.396 3.592 3.380 3.564 3.919 3.548
## iqr_high 11.248 11.053 11.138 10.709 11.171 11.585 10.469
## iqr_low -5.654 -5.095 -5.388 -5.069 -5.346 -5.879 -5.322
## sd 2.419 2.294 2.368 2.270 2.351 2.479 2.303
## var 5.852 5.263 5.610 5.155 5.526 6.147 5.305
## stdvar 1.525 1.222 1.391 1.285 1.342 1.581 1.514
## iprgc_108 iprgc_109 iprgc_110 iprgc_111 iprgc_112 iprgc_113 iprgc_114
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 1.838 2.491 2.183 2.361 2.193 2.358 2.163
## median 4.026 4.483 3.966 4.274 4.066 4.150 4.078
## mean 3.945 4.302 4.002 4.184 4.080 4.108 4.065
## q3 5.784 6.029 5.657 5.855 5.808 5.711 5.801
## max 14.921 12.859 15.079 14.025 13.023 15.794 13.261
## iqr 3.946 3.538 3.473 3.493 3.614 3.353 3.638
## iqr_high 11.703 11.335 10.867 11.095 11.229 10.741 11.257
## iqr_low -5.919 -5.306 -5.210 -5.240 -5.422 -5.030 -5.457
## sd 2.486 2.354 2.336 2.335 2.386 2.267 2.400
## var 6.179 5.543 5.457 5.450 5.695 5.139 5.760
## stdvar 1.566 1.289 1.364 1.303 1.396 1.251 1.417
## iprgc_115 iprgc_116 iprgc_117 iprgc_118 iprgc_119 iprgc_120 iprgc_121
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.511 2.620 2.396 2.491 2.525 2.346 2.352
## median 4.495 4.525 4.367 4.396 4.404 4.347 4.365
## mean 4.303 4.332 4.223 4.256 4.248 4.214 4.214
## q3 6.020 5.957 5.980 5.985 5.919 5.941 5.942
## max 12.739 12.644 13.541 13.650 13.796 14.752 14.560
## iqr 3.508 3.337 3.584 3.494 3.394 3.596 3.590
## iqr_high 11.282 10.963 11.356 11.225 11.010 11.335 11.327
## iqr_low -5.262 -5.006 -5.376 -5.241 -5.091 -5.393 -5.385
## sd 2.354 2.289 2.377 2.347 2.310 2.368 2.366
## var 5.543 5.242 5.649 5.509 5.335 5.609 5.596
## stdvar 1.288 1.210 1.338 1.294 1.256 1.331 1.328
## iprgc_122 iprgc_123 iprgc_124 iprgc_125 iprgc_126 iprgc_127 iprgc_128
## min 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## q1 2.567 1.556 2.038 2.211 2.215 2.323 2.352
## median 4.453 3.322 3.745 4.085 4.082 4.172 4.198
## mean 4.282 3.409 3.761 4.045 4.037 4.138 4.128
## q3 5.915 5.004 5.325 5.709 5.733 5.838 5.782
## max 13.284 17.222 17.196 13.801 15.040 14.076 15.026
## iqr 3.348 3.447 3.287 3.498 3.518 3.515 3.430
## iqr_high 10.937 10.174 10.255 10.956 11.011 11.110 10.926
## iqr_low -5.022 -5.171 -4.930 -5.247 -5.278 -5.272 -5.145
## sd 2.300 2.242 2.196 2.354 2.363 2.348 2.316
## var 5.291 5.026 4.821 5.543 5.583 5.511 5.364
## stdvar 1.236 1.474 1.282 1.370 1.383 1.332 1.300
## iprgc_129 iprgc_130
## min 0.000 0.000
## q1 2.462 2.286
## median 4.266 4.221
## mean 4.175 4.132
## q3 5.817 5.836
## max 15.200 13.939
## iqr 3.354 3.550
## iqr_high 10.848 11.162
## iqr_low -5.031 -5.325
## sd 2.293 2.373
## var 5.258 5.632
## stdvar 1.259 1.363## Plot describing the gene distribution from a dataset.## png
## 2## Plot describing the gene distribution from a dataset.
There is some difference across sample densities, but it is not too crazytown.
17 Diverging a little
At this point in the document I read ahead a bit and came to the conclusion that it repeats the above logic of taking the union of wt comparisons to remove genes from the appropriate het/ko or p15/p08 or location comparisons. This seems quite reasonable to me, but I would prefer to not separate all the data, so I will attempt to duplicate and slightly streamline this logic on the full dataset. Thus I am going to skip down to the end and attempt to implement this.
17.1 DE
Note: The following few blocks are all copy/pasted directly from Theresa’s notebook and are not evaluated because they are performed almost identically later but with slightly different logic/orders.
mm_de_normal_p8_scn <- all_pairwise(mm38_subset, model_batch = "svaseq",
parallel = FALSE, do_ebseq = FALSE, do_basic = FALSE,
do_dream = FALSE, do_noiseq = FALSE, do_edger = FALSE,
filter = TRUE)
annots_to_merge <- mm_annot %>%
select(ensembl_gene_id, external_gene_name) %>%
filter(ensembl_gene_id %in% rownames(mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn)) %>%
distinct()
mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn <- merge(
mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn,
annots_to_merge, by.x = 0, by.y = "ensembl_gene_id", all.x = TRUE)hetkeeper_genes <- mm_de_normal_p8_scn$deseq$all_tables$wt_scn_vs_het_scn %>%
filter(logFC <= -0.1 & adj.P.Val <= 0.05)
kokeeper_genes <- mm_de_normal_p8_scn$deseq$all_tables$wt_scn_vs_ko_scn %>%
filter(logFC <= -0.1 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(hetkeeper_genes), rownames(kokeeper_genes)))
df <- mm_de_normal_p8_scn$deseq$all_tables$koscn_vs_hetscn %>%
dplyr::mutate(logFC = -logFC) %>%
set_sig_limma(factors = c("Het Enriched",
"KO Enriched"))
df <- df %>%
filter(Row.names %in% keepergenes)
labels_ups <- df %>%
filter(abs(logFC) > 1) %>%
arrange(logFC) %>%
head(n = 1)
labels_downs <- df %>%
filter(abs(logFC) > 1) %>%
arrange(-logFC) %>%
head(n = 1)
labels <- rbind(labels_ups, labels_downs)
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
geom_point(aes(colour = Significance), size = 4) +
geom_vline(xintercept = c(-1, 1)) +
geom_hline(yintercept = -log10(0.05)) +
theme_classic(base_size = 20) +
xlab("log2(FC)") +
ylab("-log10(p-value)") +
## ggtitle(title, subtitle = subtitle) +
theme(legend.position="right") +
scale_color_manual(values=c("Het Enriched" = "#F8766D",
"KO Enriched" = "#00BFC4",
"Not\n Enriched" = "Grey")) +
geom_label_repel(data=filter(df,
## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
external_gene_name %in% labels$external_gene_name),
## nudge_x = -0.5,
nudge_y = 3, max.overlaps = 15) +
ggtitle("SCN Het vs KO Translatome")
pp(file = "images/p08_scn_DE_1312024.pdf")
DEplot
plotted <- dev.off()
write_xlsx(df, excel = "excel/scnhet_vs_scnko_WTfiltered.xlsx")17.2 How many ups/downs
17.3 GSEA
ko_genes <- res_tbl %>%
filter(adj.P.Val <= 0.05) %>%
arrange(-abs(logFC)) %>%
select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
filter(logFC <= -1)
het_genes <- res_tbl %>%
filter(adj.P.Val <= 0.05) %>%
arrange(-abs(logFC)) %>%
select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
filter(logFC >= 1)
alldysregulated_genes <- res_tbl %>%
filter(adj.P.Val <= 0.05) %>%
arrange(logFC) %>%
select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
filter(abs(logFC) >= 1)
gsea_result_ko <- gost(query = ko_genes$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE)
gsea_result_het <- gost(query = het_genes$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE)
gsea_result_alldysregulated <- gost(query = alldysregulated_genes$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE)gsea_ko <- gsea_result_ko[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 10)
gsea_plots_ko <- ggplot(gsea_ko, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over enrichment Score")
gsea_het <- gsea_result_het[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 10)
gsea_plots_het <- ggplot(gsea_het, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over enrichment Score")
gsea_all <- gsea_result_alldysregulated[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 10)
gsea_plots_all <- ggplot(gsea_all, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over enrichment Score")
pp(file = "images/GSEA_p08_retina_axontrap_alldysregulatedgenes.pdf")
gsea_plots_all
plotted <- dev.off()18 Het Retina vs Het SCN
mm38_subset2 <- subset_se(
mm38_hisat,
subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08' & genotype != 'ko' & location != 'dlgn' | sampleid == 'iprgc_03'")
mm38_subset2 <- subset_se(mm38_subset2, subset = "sampleid != 'iprgc_89'")
mm38_subset2$design %>%
select(genotype, location) %>%
table()
mm38_norm2 <- normalize(mm38_subset2, filter=TRUE,
convert="cpm",
transform="log2", batch = "svaseq")18.1 PCA
18.2 DE
mm_de_subset2 <- all_pairwise(mm38_subset2,
model_batch="svaseq",
parallel=FALSE, do_ebseq=FALSE,
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_edger = FALSE,
filter = TRUE)retinakeeper_genes <- mm_de_subset2$deseq$all_tables$wt_retina_vs_het_retina %>%
filter(logFC <= -0.1 & adj.P.Val <= 0.05)
scnkeeper_genes <- mm_de_subset2$deseq$all_tables$wt_scn_vs_het_scn %>%
filter(logFC <= -0.1 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(retinakeeper_genes), rownames(scnkeeper_genes)))
annots_to_merge <- mm_annot %>%
select(ensembl_gene_id, external_gene_name) %>%
filter(ensembl_gene_id %in% rownames(mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina)) %>%
distinct()
mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina <- merge(
mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina,
annots_to_merge, by.x = 0,
by.y = "ensembl_gene_id", all.x = TRUE)
df <- mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina %>%
mutate(Significance = case_when(logFC <= -1.0 ~ "Retina Enriched",
logFC >= 1.0 ~ "SCN Enriched",
logFC > -1.0 & logFC < 1.0 ~ "Not\n Enriched"))
df <- df %>%
filter(Row.names %in% keepergenes)
scn_enriched <- df %>%
filter(adj.P.Val <= 0.05 & logFC >= 1.0) %>%
arrange(-logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
mutate(Significance = "SCN Enriched") %>%
filter(Row.names %in% rownames(scnkeeper_genes))
retina_enriched <- df %>%
filter(adj.P.Val <= 0.05 & logFC <= -1.0) %>%
arrange(logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
mutate(Significance = "Retina Enriched") %>%
filter(Row.names %in% rownames(retinakeeper_genes))
notenriched <- df %>%
select(Row.names, external_gene_name, logFC, adj.P.Val, Significance) %>%
filter(Row.names %in% c(rownames(retinakeeper_genes),
rownames(scnkeeper_genes))[duplicated(c(rownames(retinakeeper_genes),
rownames(scnkeeper_genes)))]) %>%
filter(Significance == "Not\n Enriched")
df <- rbind(scn_enriched, retina_enriched, notenriched)
df <- df %>%
distinct()
## writexl::write_xlsx(df, path = "axonTRAP_DE_results_20240202/retinahet_vs_scn_het_WTfiltered.xlsx")labels_ups <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1.0) %>%
arrange(logFC) %>%
head(n = 10)
labels_downs <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1.0) %>%
arrange(-logFC) %>%
head(n = 10)
labels <- rbind(labels_ups, labels_downs)
labels_requested <- c("Cdh10","Cdh12","Cdh13","Cdh18",
"Cdh7","Cdh8","Cdh9","Cntn3",
"Cntn4","Cntn5","Cntn6","Kirrel3",
"Nrxn1","Nrxn3","Sema3c","Sema6d",
"Tenm1","Tenm2","Tenm4")
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
geom_point(aes(colour = Significance), size = 4) +
geom_vline(xintercept = c(-1, 1)) +
geom_hline(yintercept = -log10(0.05)) +
theme_classic(base_size = 20) +
xlab("log2(FC)") +
ylab("-log10(p-value)") +
## ggtitle(title, subtitle = subtitle) +
theme(legend.position="right") +
scale_color_manual(values=c("Grey", "#F8766D", "#00BFC4")) +
geom_label_repel(data=filter(df,
external_gene_name %in% labels_requested),
## c(labels$external_gene_name, "Opn4")), #c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
## nudge_x = -0.5,
nudge_y = 15, max.overlaps = 25)
#pp(file = "axonTRAP_Volcanoplots_20240202/p08_retinavsscnhet_DE_requested_genelabels_02052024.pdf")
DEplot
#dev.off()18.3 How many ups/downs
scn_enriched <- df %>%
filter(adj.P.Val <= 0.05 & logFC >= 1.0) %>%
arrange(-logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val, Significance)
retina_enriched <- df %>%
filter(adj.P.Val <= 0.05 & logFC <= -1.0) %>%
arrange(logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val, Significance)
scn_enriched
retina_enriched
df %>%
filter(Significance == "Not\n Enriched")18.4 GSEA
gsea_result_scn <- gost(query = scn_enriched$external_gene_name,
organism = "mmusculus", evcodes = TRUE,
ordered_query = TRUE, source = c("GO"))
gsea_result_ret <- gost(query = retina_enriched$external_gene_name,
organism = "mmusculus", evcodes = TRUE,
ordered_query = TRUE, source = c("GO"))gsea_scn <- gsea_result_scn[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 20)
gsea_plots_scn <- ggplot(gsea_scn, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over enrichment Score")
pp(file = "images/GSEA_SCNhet_vs_retina_enriched_P08.pdf")
gsea_plots_scn
plotted <- dev.off()
gsea_ret <- gsea_result_ret[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 20)
gsea_plots_ret <- ggplot(gsea_ret, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("Over enrichment Score")
pp(file = "images/GSEA_Retinahet_vs_SCN_enriched_P08.pdf")
gsea_plots_ret
plotted <- dev.off()19 KO Retina vs KO SCN
mm38_subset3 <- subset_se(
mm38_hisat,
subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08' & genotype != 'het' & location != 'dlgn' | sampleid == 'iprgc_03'")
mm38_subset3 <- subset_se(mm38_subset3, subset = "sampleid != 'iprgc_86'")
mm38_subset3$design %>%
select(genotype, location) %>%
table()
mm38_norm3 <- normalize(mm38_subset3, filter=TRUE,
convert="cpm", transform="log2", batch = "svaseq")19.1 PCA
19.2 DE
mm_de_subset3 <- all_pairwise(mm38_subset3,
model_batch="svaseq",
parallel=FALSE, do_ebseq=FALSE,
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_edger = FALSE,
filter = TRUE)
retinakeeper_genes <- mm_de_subset3$deseq$all_tables$wtretina_vs_koretina %>%
filter(logFC <= -1.0 & adj.P.Val <= 0.05)
scnkeeper_genes <- mm_de_subset3$deseq$all_tables$wtscn_vs_koscn %>%
filter(logFC <= -1.0 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(retinakeeper_genes), rownames(scnkeeper_genes)))
annots_to_merge <- mm_annot %>%
select(ensembl_gene_id, external_gene_name) %>%
filter(ensembl_gene_id %in% rownames(mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina)) %>%
distinct()
mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina <- merge(
mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina,
annots_to_merge, by.x = 0,
by.y = "ensembl_gene_id", all.x = TRUE)
df <- mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina %>%
mutate(Significance = case_when(logFC <= -1 ~ "Retina Enriched",
logFC >= 1 ~ "SCN Enriched",
logFC > -1 & logFC < 1 ~ "Not\n Enriched"))
df <- df %>%
filter(Row.names %in% keepergenes)
scn_enriched <- df %>%
filter(adj.P.Val <= 0.05 & logFC >= 1) %>%
arrange(-logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
mutate(Significance = "SCN Enriched") %>%
filter(Row.names %in% rownames(scnkeeper_genes))
df %>%
filter(adj.P.Val <= 0.05 & logFC <= -1) %>%
arrange(logFC) %>%
select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
mutate(Significance = "Retina Enriched") %>%
filter(Row.names %in% rownames(retinakeeper_genes)) -> retina_enriched
notenriched <- df %>%
select(Row.names, external_gene_name, logFC, adj.P.Val, Significance) %>%
filter(Row.names %in% c(rownames(retinakeeper_genes),
rownames(scnkeeper_genes))[duplicated(c(rownames(retinakeeper_genes),
rownames(scnkeeper_genes)))])
df <- rbind(scn_enriched, retina_enriched, notenriched)labels_ups <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
arrange(logFC) %>%
head(n = 10)
labels_downs <- df %>%
filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
arrange(-logFC) %>%
head(n = 10)
labels <- rbind(labels_ups, labels_downs)
## wanted_column <- "Significance"
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
geom_point(aes(colour = Significance), size = 4) +
## geom_point(aes(colour = !!sym(wanted_column)), size = 4) +
geom_vline(xintercept = c(-1, 1)) +
geom_hline(yintercept = -log10(0.05)) +
theme_classic(base_size = 20) +
xlab("log2(FC)") +
ylab("-log10(p-value)") +
## ggtitle(title, subtitle = subtitle) +
theme(legend.position = "right") +
scale_color_manual(values = c("Grey", "#F8766D", "#00BFC4")) +
geom_label_repel(data = filter(
df, external_gene_name %in% c(labels$external_gene_name, "Opn4")),
## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
## nudge_x = -0.5,
nudge_y = 10, max.overlaps = 25)
pp(file = "images/p08_retinavsscnko_DE_1312024.pdf")
DEplot
plotted <- dev.off()19.3 How many ups/downs
19.4 GSEA
gsea_result_scn <- gost(query = scn_enriched$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE,
source = c("GO"))
gsea_result_ret <- gost(query = retina_enriched$external_gene_name,
organism = "mmusculus",
evcodes = TRUE,
ordered_query = TRUE,
source = c("GO"))gsea_scn <- gsea_result_scn[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 20)
gsea_plots_scn <- ggplot(gsea_scn, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("GSEA Score")
pp(file = "images/GSEA_SCNko_enriched_vs_retina_P08.pdf")
gsea_plots_scn
plotted <- dev.off()
gsea_ret <- gsea_result_ret[["result"]] %>%
select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
arrange(desc(recall)) %>%
head(n = 20)
gsea_plots_ret <- ggplot(gsea_ret, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
geom_bar(stat = "identity") +
scale_fill_continuous(low = "blue", high = "red") +
theme_bw() +
ylab("") +
xlab("GSEA Score")
pp(file = "images/GSEA_Retinako_enriched_vs_SCN_P08.pdf")
gsea_plots_ret
plotted <- dev.off()20 My version of the global analysis
I want to have an invocation of all_pairwise() which uses all samples, in the following block I will set that up using a set of ‘keepers’ which will be named by time, location, then 2 letters for the numerator/denominator: w for WT, h for het, d for delta; thus “p08_retina_hw” is comparing the het/wt for the p08 retina samples.
If they are of interest, I will have a separate set which follows the same convention with names like “p08_ko_sr” to compare p08 deltas with SCN as the numerator and retina as the denominator.
20.1 Set up the exclusion dataset
The most peculiar aspect of this analysis resides in the choices around choosing which genes to consider when comparing the genotypes/locations/times. The general idea is pretty clear: find the genes which are non-specifically being pulled down in the WT samples and either exclude or discount them. The various potential methods for performing this are confusing:
- Which set of comparisons of wt/ko wt/het do we use to
exclude/discount genes?
- Should it be a combination of all samples wt vs. x?
- Should it be only the ‘relevant’ comparison, e.g. if we are comparing p08_dlgn_het vs. p08_scn_het; do we remove genes observed in (p08_dlgn_het/wt && p08_scn_het/wt)
- Do we instead attempt to use this x/wt information to normalize the expression values in the other conditions and keep those genes?
Theresa’s current worksheet implements a version of 1b in which she separated the various input gene sets to define the exclusion genes. I am going to repeat this, but leave the starting data structure intact.
In this first iteration, I will do that by creating a simplified model of the data which combines the time/genotype/location and using sva. In my next iteration I will use a full statistical model containing each of those factors (and probably also using sva).
Note: my color choices are kind of garbage.
In addition, the exclusion dataset is the same as the analysis dataset, it is really only the contrasts which will be different.
v3_pairwise_input <- set_conditions(mm38_hisat_v3, fact = "time_geno_loc",
colors = color_choices[["all"]])## The numbers of samples by condition are:##
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Warning in set_se_colors(new_se, colors = colors): Colors for the following
## categories are not being used: p60_wt_dlgn, p60_wt_retina, p60_wt_scn.20.2 A heatmap of specific genes across all conditions
all_cond_gene_heatmap_start <- normalize(v3_pairwise_input, filter = "simple",
length_column = "cds_length",
convert = "rpkm", transform = "log2")## Removing 5517 low-count genes (19908 remaining).## There appear to be 6387 genes without a length.## transform_counts: Found 592846 values equal to 0, adding 1 to the matrix.## The factor p08_het_dlgn has 3 rows.## The factor p08_het_retina has 3 rows.## The factor p08_het_scn has 3 rows.## The factor p08_ko_dlgn has 3 rows.## The factor p08_ko_retina has 3 rows.## The factor p08_ko_scn has 3 rows.## The factor p08_wt_dlgn has 5 rows.## The factor p08_wt_retina has 5 rows.## The factor p08_wt_scn has 3 rows.## The factor p15_het_dlgn has 4 rows.## The factor p15_het_retina has 4 rows.## The factor p15_het_scn has 3 rows.## The factor p15_ko_dlgn has 3 rows.## The factor p15_ko_retina has 3 rows.## The factor p15_ko_scn has 3 rows.## The factor p15_wt_dlgn has 5 rows.## The factor p15_wt_retina has 5 rows.## The factor p15_wt_scn has 2 rows.all_cond_mtrx <- all_cond_gene_heatmap_input[["medians"]]
color_order <- colnames(all_cond_mtrx)
na_idx <- is.na(all_cond_mtrx)
all_cond_mtrx[na_idx] <- 0
variances <- matrixStats::rowVars(as.matrix(all_cond_mtrx))
variant_genes <- variances > 6.2
input_mtrx <- all_cond_mtrx[variant_genes, ]
cond_colors <- get_colors_by_condition(v3_pairwise_input, levels = color_order)
dim(input_mtrx)## [1] 104 18## Warning in pp(file =
## "04inclusion_comparisons/top_104_variant_rpkm_genes_heatmap.pdf"): The
## directory: 04inclusion_comparisons does not exist, will attempt to create it.gplots::heatmap.2(as.matrix(input_mtrx), scale = "none", trace = "none",
ColSideColors = cond_colors)
dev.off()## png
## 221 TODO: Change the above to subtract wt
Rashmi suggested we should do the above plot after subtracting the wt counts. This is a good idea, but it will have to wait until we finish the current set.
21.1 Set up the contrasts
In the following few blocks I will set up the various comparisons of interest. Starting with the set of genes to exclude because they were observed to bind non-specifically in the wt samples.
21.1.1 Inclusion contrasts
In each exclusion I will have the contrast first followed by the pair of contrasts which will be used to define the gene set to exclude.
- p15_het_dlgn/p08_het_dlgn: p15_wt_dlgn/p15_het_dlgn, p08_wt_dlgn/p08_het_dlgn; remove the genes increased in wt.
- p15_ko_scn/p08_ko_scn: p15_wt_scn/p15_ko_scn, p08_wt_scn/p15_ko_scn
- p15_het_retina/p08_het_retina: I think you get it, wt/het for both p15 retinas and p08 retinas…
Put slightly differently, for every term of interest I will create a contrast with the wt as numerator and the desired term as denominator, then pull out the genes increased in wt.
inclusions <- list(
## I like alphabetizing things, start with dlgn
"p15_het_dlgn" = c("p15_het_dlgn", "p15_wt_dlgn"),
"p08_het_dlgn" = c("p08_het_dlgn", "p08_wt_dlgn"),
"p15_ko_dlgn" = c("p15_ko_dlgn", "p15_wt_dlgn"),
"p08_ko_dlgn" = c("p08_ko_dlgn", "p08_wt_dlgn"),
## Then retinas
"p15_het_retina" = c("p15_het_retina", "p15_wt_retina"),
"p08_het_retina" = c("p08_het_retina", "p08_wt_retina"),
"p15_ko_retina" = c("p15_ko_retina", "p15_wt_retina"),
"p08_ko_retina" = c("p08_ko_retina", "p08_wt_retina"),
## Then scn
"p15_het_scn" = c("p15_het_scn", "p15_wt_scn"),
"p08_het_scn" = c("p08_het_scn", "p08_wt_scn"),
"p15_ko_scn" = c("p15_ko_scn", "p15_wt_scn"),
"p08_ko_scn" = c("p08_ko_scn", "p08_wt_scn"))21.1.2 Time contrasts
For each location/genotype of interest, let us compare p15/p08
time_keepers <- list(
## DLGN
"t_het_dlgn" = c("p15_het_dlgn", "p08_het_dlgn"),
"t_ko_dlgn" = c("p15_ko_dlgn", "p08_ko_dlgn"),
## Retina
"t_het_retina" = c("p15_het_retina", "p08_het_retina"),
"t_ko_retina" = c("p15_ko_retina", "p08_ko_retina"),
## SCN
"t_het_scn" = c("p15_het_scn", "p08_het_scn"),
"t_ko_scn" = c("p15_ko_scn", "p08_ko_scn"))21.1.3 Location contrasts
Compare locations and keep time/genotype consistent. I will use the location initials to define numerator/denominator.
location_keepers <- list(
## dlgn/retina
"dr_p08_het" = c("p08_het_dlgn", "p08_het_retina"),
"dr_p15_het" = c("p15_het_dlgn", "p15_het_retina"),
"dr_p08_ko" = c("p08_ko_dlgn", "p08_ko_retina"),
"dr_p15_ko" = c("p15_ko_dlgn", "p15_ko_retina"),
## scn/retina
"sr_p08_het" = c("p08_het_scn", "p08_het_retina"),
"sr_p15_het" = c("p15_het_scn", "p15_het_retina"),
"sr_p08_ko" = c("p08_ko_scn", "p08_ko_retina"),
"sr_p15_ko" = c("p15_ko_scn", "p15_ko_retina"),
## dlgn/scn
"ds_p08_het" = c("p08_het_dlgn", "p08_het_scn"),
"ds_p15_het" = c("p15_het_dlgn", "p15_het_scn"),
"ds_p08_ko" = c("p08_ko_dlgn", "p08_ko_scn"),
"ds_p15_ko" = c("p15_ko_dlgn", "p15_ko_scn"))21.1.4 Genotype contrasts
Compare ko/het while keeping time/location constant. Similarly, use the initials to denote numerator/denominator, which will always be kh.
genotype_keepers <- list(
## DLGN
"kh_p08_dlgn" = c("p08_ko_dlgn", "p08_het_dlgn"),
"kh_p15_dlgn" = c("p15_ko_dlgn", "p15_het_dlgn"),
## Retina
"kh_p08_retina" = c("p08_ko_retina", "p08_het_retina"),
"kh_p15_retina" = c("p15_ko_retina", "p15_het_retina"),
## SCN
"kh_p08_scn" = c("p08_ko_scn", "p08_het_scn"),
"kh_p15_scn" = c("p15_ko_scn", "p15_het_scn"))21.2 Perform the exclusion comparison
My all_pairwise() function now has a parameter which allows me to choose which contrasts to perform instead of literally doing every possible comparison. That is well suited for these operations:
In a container, the following appears to fail with:
“error code 1 from Lapack routine ‘dgesdd’”
Running it manually outside the container results in it working without error. I assume therefore that the problem lies in the compilation flags of LAPACK in the container.
Note: This problem was fixed by removing some parallelization.
inclusion_de <- all_pairwise(
v3_pairwise_input, filter = "simple", model_fstring = default_fstring,
keepers = inclusions, model_svs = "svaseq")## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 5517 low-count genes (19908 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 394442 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2
## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 12 comparisons.inclusion_tables <- combine_de_tables(
inclusion_de, keepers = inclusions, label_column = label_column,
excel = glue("04inclusion_comparisons/inclusion_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p15_het_dlgn_vs_p15_wt_dlgn 536 971 668
## 2 p08_het_dlgn_vs_p08_wt_dlgn 15 79 57
## 3 p15_ko_dlgn_vs_p15_wt_dlgn 903 1333 1029
## 4 p08_ko_dlgn_vs_p08_wt_dlgn 117 282 133
## 5 p15_het_retina_vs_p15_wt_retina 151 58 193
## 6 p08_het_retina_vs_p08_wt_retina 432 110 464
## 7 p15_ko_retina_vs_p15_wt_retina 107 34 178
## 8 p08_ko_retina_vs_p08_wt_retina 533 155 551
## 9 p15_het_scn_vs_p15_wt_scn 11 8 39
## 10 p08_het_scn_vs_p08_wt_scn 48 8 57
## 11 p15_ko_scn_vs_p15_wt_scn 7 5 28
## 12 p08_ko_scn_vs_p08_wt_scn 31 26 83
## edger_sigdown limma_sigup limma_sigdown
## 1 1013 582 833
## 2 107 40 46
## 3 1346 837 1045
## 4 297 186 356
## 5 114 119 57
## 6 162 315 88
## 7 62 39 25
## 8 224 404 144
## 9 28 19 34
## 10 45 42 20
## 11 30 39 39
## 12 54 65 55## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## i Please use tidy evaluation idioms with `aes()`.
## i See also `vignette("ggplot2-in-packages")` for more information.
## i The deprecated feature was likely used in the UpSetR package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## i Please use the `linewidth` argument instead.
## i The deprecated feature was likely used in the UpSetR package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Plot describing unique/shared genes in a differential expression table.
## 202603: I successfully recapitulated previous non-container result.
inclusion_sig <- extract_significant_genes(
inclusion_tables, lfc = lfc_cutoff, p = adjp_cutoff, according_to = "deseq",
excel = glue("04inclusion_comparisons/inclusion_sig-v{ver}.xlsx"))
inclusion_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 0.1 adj P cutoff: 0.1## deseq_up deseq_down
## p15_het_dlgn 2067 2381
## p08_het_dlgn 180 349
## p15_ko_dlgn 2544 2633
## p08_ko_dlgn 397 572
## p15_het_retina 402 223
## p08_het_retina 976 380
## p15_ko_retina 347 93
## p08_ko_retina 1551 859
## p15_het_scn 15 9
## p08_het_scn 225 55
## p15_ko_scn 8 6
## p08_ko_scn 112 116
202505: A strange thing happened here in this iteration: the plot of the significant genes is the exact same as the previous iteration; but the table of numbers of genes looks different.
For example, the previous table showed: p15_het_dlgn with 2067 up and 2381 down. The plot shows exactly that; but the new table shows 607 up and 1229 down. Let us check the actual data structure and see what is up?
I think I get it: when we do the extract_significant_genes above, we explicitly set a non-standard p-value and logFC because we are explicitly attempting to use a very loose definition of the set of genes which are in greater abundance than their most similar wild-type. However, when I create the barplot of significant genes; those values are explicitly set to 0,1,2 logFCs and p-value 0.05. Therefore, what I need to do, in order to check consistency, is to repeat this call but with the default FC/p values and see what the numbers look like.
test_inclusion <- extract_significant_genes(
inclusion_tables, according_to = "deseq", excel = "excel/default_inclusion_sig.xlsx")
test_inclusion## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## p15_het_dlgn 536 971
## p08_het_dlgn 15 79
## p15_ko_dlgn 903 1333
## p08_ko_dlgn 117 282
## p15_het_retina 151 58
## p08_het_retina 432 110
## p15_ko_retina 107 34
## p08_ko_retina 533 155
## p15_het_scn 11 8
## p08_het_scn 48 8
## p15_ko_scn 7 5
## p08_ko_scn 31 26
Yeah, I think this makes sense; what I need to do: change the significant bar plot so that it uses the lfc cutoff argument as the second of its 3 cutoffs. That should ensure that these numbers are consistent across analyses and parameters provided.
## [1] 2067 75test_all_up <- inclusion_tables$data$p15_het_dlgn[["deseq_logfc"]] > 0.1 &
inclusion_tables$data$p15_het_dlgn[["deseq_adjp"]] <= 0.1
summary(test_all_up)## Mode FALSE TRUE
## logical 17841 2067Ohh, I get it, when I was testing this out manually, I set the logFC to 1.0 instead of the very minimal 0.1 we have been using for this!
22 MA/Volcano plots of x vs wt
Rashmi asked to see the comparisons against wt; I will name each file xw to show that it is x vs wt. for whatever other parameters are being examined. It is likely that some colors will be wrong because this is my first time creating these plots and we are doing them manually.
22.1 p15_het_dlgn
allc <- color_choices[["all"]]
table_name <- "p15_het_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_dlgn"
denom <- "p15_wt_dlgn"
hw_p15_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p15_dlgn_volcano.pdf", width = 9, height = 9)## Warning in pp(file = "05inclusion_volcano_ma/hw_p15_dlgn_volcano.pdf", width =
## 9, : The directory: 05inclusion_volcano_ma does not exist, will attempt to
## create it.
hw_p15_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p15_dlgn_ma.pdf", width = 9, height = 9)
hw_p15_dlgn_ma[["plot"]]
plotted <- dev.off()
hw_p15_dlgn_ma[["plot"]]
22.2 p08_het_dlgn
table_name <- "p08_het_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_dlgn"
denom <- "p08_wt_dlgn"
hw_p08_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_dlgn_volcano.pdf", width = 9, height = 9)
hw_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
hw_p08_dlgn_volcano[["plot"]]
hw_p08_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_dlgn_ma.pdf", width = 9, height = 9)
hw_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
hw_p08_dlgn_ma[["plot"]]
22.3 p15_ko_dlgn
table_name <- "p15_ko_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_dlgn"
denom <- "p15_wt_dlgn"
kw_p15_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_dlgn_volcano.pdf", width = 9, height = 9)
kw_p15_dlgn_volcano[["plot"]]
plotted <- dev.off()
kw_p15_dlgn_volcano[["plot"]]
kw_p15_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_dlgn_ma.pdf", width = 9, height = 9)
kw_p15_dlgn_ma[["plot"]]
plotted <- dev.off()
kw_p15_dlgn_ma[["plot"]]
22.4 p08_ko_dlgn
table_name <- "p08_ko_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_dlgn"
denom <- "p08_wt_dlgn"
kw_p08_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_dlgn_volcano.pdf", width = 9, height = 9)
kw_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
kw_p08_dlgn_volcano[["plot"]]
kw_p08_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/kw_p08_dlgn_ma.pdf", width = 9, height = 9)
kw_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
kw_p08_dlgn_ma[["plot"]]
22.5 p15_het_retina
table_name <- "p15_het_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_retina"
denom <- "p15_wt_retina"
hw_p15_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p15_retina_volcano.pdf", width = 9, height = 9)
hw_p15_retina_volcano[["plot"]]
plotted <- dev.off()
hw_p15_retina_volcano[["plot"]]
hw_p15_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p15_retina_ma.pdf", width = 9, height = 9)
hw_p15_retina_ma[["plot"]]
plotted <- dev.off()
hw_p15_retina_ma[["plot"]]
22.6 p08_het_retina
table_name <- "p08_het_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_retina"
denom <- "p08_wt_retina"
hw_p08_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_retina_volcano.pdf", width = 9, height = 9)
hw_p08_retina_volcano[["plot"]]
plotted <- dev.off()
hw_p08_retina_volcano[["plot"]]
hw_p08_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_retina_ma.pdf", width = 9, height = 9)
hw_p08_retina_ma[["plot"]]
plotted <- dev.off()
hw_p08_retina_ma[["plot"]]
22.7 p15_ko_retina
table_name <- "p15_ko_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_retina"
denom <- "p15_wt_retina"
kw_p15_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_retina_volcano.pdf", width = 9, height = 9)
kw_p15_retina_volcano[["plot"]]
plotted <- dev.off()
kw_p15_retina_volcano[["plot"]]
kw_p15_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_retina_ma.pdf", width = 9, height = 9)
kw_p15_retina_ma[["plot"]]
plotted <- dev.off()
kw_p15_retina_ma[["plot"]]
22.8 p08_ko_retina
table_name <- "p08_ko_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_retina"
denom <- "p08_wt_retina"
kw_p08_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_retina_volcano.pdf", width = 9, height = 9)
kw_p08_retina_volcano[["plot"]]
plotted <- dev.off()
kw_p08_retina_volcano[["plot"]]
kw_p08_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma_p08_retina_ma.pdf", width = 9, height = 9)
kw_p08_retina_ma[["plot"]]
plotted <- dev.off()
kw_p08_retina_ma[["plot"]]
22.9 p15_het_scn
table_name <- "p15_het_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_scn"
denom <- "p15_wt_scn"
hw_p15_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p15_scn_volcano.pdf", width = 9, height = 9)
hw_p15_scn_volcano[["plot"]]
plotted <- dev.off()
hw_p15_scn_volcano[["plot"]]
hw_p15_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p15_scn_ma.pdf", width = 9, height = 9)
hw_p15_scn_ma[["plot"]]
plotted <- dev.off()
hw_p15_scn_ma[["plot"]]
22.10 p08_het_scn
table_name <- "p08_het_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_scn"
denom <- "p08_wt_scn"
hw_p08_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_scn_volcano.pdf", width = 9, height = 9)
hw_p08_scn_volcano[["plot"]]
plotted <- dev.off()
hw_p08_scn_volcano[["plot"]]
hw_p08_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_scn_ma.pdf", width = 9, height = 9)
hw_p08_scn_ma[["plot"]]
plotted <- dev.off()
hw_p08_scn_ma[["plot"]]
22.11 p15_ko_scn
table_name <- "p15_ko_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_scn"
denom <- "p15_wt_scn"
kw_p15_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_scn_volcano.pdf", width = 9, height = 9)
kw_p15_scn_volcano[["plot"]]
plotted <- dev.off()
kw_p15_scn_volcano[["plot"]]
kw_p15_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_scn_ma.pdf", width = 9, height = 9)
kw_p15_scn_ma[["plot"]]
plotted <- dev.off()
kw_p15_scn_ma[["plot"]]
22.12 p08_ko_scn
table_name <- "p08_ko_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_scn"
denom <- "p08_wt_scn"
kw_p08_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = allc[[denom]], color_high = allc[[num]],
label_column = "mgi_symbol", label = 10, alpha = 1.0,
size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_scn_volcano.pdf", width = 9, height = 9)
kw_p08_scn_volcano[["plot"]]
plotted <- dev.off()
kw_p08_scn_volcano[["plot"]]
kw_p08_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = allc[[denom]], color_high = allc[[num]],
p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
label = 10)
pp(file = "05inclusion_volcano_ma/kw_p08_scn_ma.pdf", width = 9, height = 9)
kw_p08_scn_ma[["plot"]]
plotted <- dev.off()
kw_p08_scn_ma[["plot"]]
See the shared/unique genes in these sets.
inclusion_upsets <- upsetr_sig(inclusion_sig)
inclusion_intersects <- write_upset_groups(
inclusion_upsets, excel = "04inclusion_comparison/inclusion_gene_groups.xlsx")22.12.1 Extract genes included for each set of contrasts
Now, using that function, pull out the gene IDs of genes we do not trust because they were too high in wt for every contrast we are likely to perform.
The following was a modified version of the inclusion function which is somewhat more restrictive.
extract_inclusions_strict <- function(inclusion_sig, inclusion_tables, inclusions, keepers,
all_genes, according_to = "deseq", which = "ups") {
retlist <- list()
table_names <- names(inclusion_sig[[according_to]][[which]])
for (c_num in seq_along(keepers)) {
contrast <- names(keepers)[c_num]
numerator_name <- keepers[[c_num]][1]
denominator_name <- keepers[[c_num]][2]
## In my new branch I cleaned up the sanitizer function for contrasts so this is not needed.
## The following two lines are no longer needed because of the cleanups I performed.
##numerator_name <- gsub(x = numerator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
##denominator_name <- gsub(x = denominator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
numerator_table <- inclusion_sig[[according_to]][[which]][[numerator_name]]
numerator_genes <- rownames(numerator_table)
denominator_table <- inclusion_sig[[according_to]][[which]][[denominator_name]]
denominator_genes <- rownames(denominator_table)
df_columns <- paste0("deseq_", c("logfc", "adjp", "den"))
included_num <- inclusion_tables[["data"]][[numerator_name]][, df_columns]
colnames(included_num) <- c("numerator_vs_wt_logfc", "numerator_vs_wt_adjp", "num_wt_mean_exprs")
included_den <- inclusion_tables[["data"]][[denominator_name]][, df_columns]
colnames(included_den) <- c("denominator_vs_wt_logfc", "denominator_vs_wt_adjp", "den_wt_mean_exprs")
## I think this is where things went wrong,
## compare this modified line to the original to prove it.
included_df <- merge(included_num, included_den, by = "row.names")
## Previously, I did not specify the merge action, all = FALSE by default.
## This then will result in a difference in the rows observed
## included_df <- merge(included_num, included_den, by = "row.names", all = FALSE)
rownames(included_df) <- included_df[["Row.names"]]
included_df[["Row.names"]] <- NULL
concatenated_genes <- c(numerator_genes, denominator_genes)
both_gene_idx <- duplicated(concatenated_genes)
genes_in_both <- concatenated_genes[both_gene_idx]
message("The set of unique genes higher in ", numerator_name,
" vs. wt is ", length(numerator_genes), ".")
message("The set of unique genes higher in ", denominator_name,
" vs. wt is ", length(denominator_genes), ".")
message("The intersection of them is ", length(genes_in_both), " genes.")
include_name <- paste0("inc_", contrast)
include_idx <- all_genes %in% genes_in_both
include_genes <- all_genes[include_idx]
df_name <- paste0("df_", contrast)
retlist[[df_name]] <- included_df
written_inclusion <- write_xlsx(
data = included_df,
excel = glue("07included_strict_genes_excel/{include_name}-v{ver}.xlsx"))
retlist[[include_name]] <- include_genes
retlist[[contrast]] <- include_genes
}
return(retlist)
}This is the pre-202505 version of this function.
extract_inclusions <- function(inclusion_sig, inclusion_tables, inclusions, keepers, all_genes,
according_to = "deseq", which = "ups") {
retlist <- list()
table_names <- names(inclusion_sig[[according_to]][[which]])
for (c_num in seq_along(keepers)) {
contrast <- names(keepers)[c_num]
numerator_name <- keepers[[c_num]][1]
denominator_name <- keepers[[c_num]][2]
## In my new branch I cleaned up the sanitizer function for contrasts so this is not needed.
## The following two lines are no longer needed because of the cleanups I performed.
##numerator_name <- gsub(x = numerator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
##denominator_name <- gsub(x = denominator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
numerator_table <- inclusion_sig[[according_to]][[which]][[numerator_name]]
numerator_genes <- rownames(numerator_table)
denominator_table <- inclusion_sig[[according_to]][[which]][[denominator_name]]
denominator_genes <- rownames(denominator_table)
df_columns <- paste0("deseq_", c("logfc", "adjp", "den"))
included_num <- inclusion_tables[["data"]][[numerator_name]][, df_columns]
colnames(included_num) <- c("numerator_vs_wt_logfc", "numerator_vs_wt_adjp", "num_wt_mean_exprs")
included_den <- inclusion_tables[["data"]][[denominator_name]][, df_columns]
colnames(included_den) <- c("denominator_vs_wt_logfc", "denominator_vs_wt_adjp", "den_wt_mean_exprs")
included_df <- merge(included_num, included_den, by = "row.names")
rownames(included_df) <- included_df[["Row.names"]]
included_df[["Row.names"]] <- NULL
include_genes <- unique(c(numerator_genes, denominator_genes))
message("The set of unique genes higher in ", numerator_name,
" vs. wt is ", length(numerator_genes), ".")
message("The set of unique genes higher in ", denominator_name,
" vs. wt is ", length(denominator_genes), ".")
message("The unique union of them is ", length(include_genes), " genes.")
include_name <- paste0("inc_", contrast)
include_idx <- all_genes %in% include_genes
include_genes <- all_genes[include_idx]
df_name <- paste0("df_", contrast)
retlist[[df_name]] <- included_df
written_inclusion <- write_xlsx(data = included_df,
excel = glue("included_genes/{include_name}-v{ver}.xlsx"))
retlist[[include_name]] <- include_genes
retlist[[contrast]] <- include_genes
}
return(retlist)
}23 ‘Normal’ Inclusion extraction
Here is the full set of gene IDs
In the following blocks I am including the union of genes observed higher than wt in either of the numerator or denominator for each contrast.
23.1 Time
time_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
time_keepers, all_genes)## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The unique union of them is 2113 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The unique union of them is 2716 genes.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1086 genes.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1664 genes.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 238 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The unique union of them is 120 genes.23.2 Location
location_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
location_keepers, all_genes)## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1134 genes.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 2361 genes.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1883 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The unique union of them is 2843 genes.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1188 genes.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 417 genes.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1624 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The unique union of them is 355 genes.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 402 genes.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The unique union of them is 2080 genes.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The unique union of them is 493 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The unique union of them is 2550 genes.23.3 Genotype
genotype_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
genotype_keepers, all_genes)## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The unique union of them is 501 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The unique union of them is 2773 genes.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1760 genes.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 571 genes.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 312 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The unique union of them is 18 genes.24 The strict ‘inclusion’ set
24.1 Time
time_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
time_keepers, all_genes)## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The intersection of them is 134 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The intersection of them is 225 genes.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The intersection of them is 292 genes.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The intersection of them is 234 genes.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The intersection of them is 2 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The intersection of them is 0 genes.24.2 Location
location_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
location_keepers, all_genes)## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The intersection of them is 22 genes.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The intersection of them is 108 genes.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The intersection of them is 65 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The intersection of them is 48 genes.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The intersection of them is 13 genes.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The intersection of them is 0 genes.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The intersection of them is 39 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The intersection of them is 0 genes.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The intersection of them is 3 genes.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The intersection of them is 2 genes.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The intersection of them is 16 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The intersection of them is 2 genes.24.3 Genotype
genotype_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
genotype_keepers, all_genes)## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The intersection of them is 76 genes.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The intersection of them is 1838 genes.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The intersection of them is 767 genes.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The intersection of them is 178 genes.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The intersection of them is 25 genes.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The intersection of them is 5 genes.24.3.1 Check vs Theresa’s filter
Up above Theresa performed a 0.25 log2FC and 0.05 adjp filter which provided a set of 2,640 genes observed higher in the p08 het retinas vs. wt retinas. I should see that in this inclusion_sig data structure.
There is an important caveat though: in Theresa’s filter above, she did a DE of only the retina samples but I did all samples. I expected that this would result in basically the same result (I actually assumed I would get a few more genes), but instead it appears to have retrieved a significantly smaller number of genes (about 1/2, happily they pretty much all appear in the previous filter). As a result, I am going to try relaxing my constraints slightly to see if I can recapitulate her filter (which would match Theresa’s later filter, though I guess that in turn will lead to a smaller set of genes compared to her later, relaxed 0.1 filter).
comparison <- inclusion_sig[["deseq"]][["ups"]][["p08_het_retina"]]
comp <- list(
"taa" = taa_keepers,
"new" = rownames(comparison))
test_comparison <- Vennerable::Venn(comp)
Vennerable::plot(test_comparison)
I want to have a little function which, given a contrast of interest, will extract the gene sets which should be included/excluded given the above.
write_all_cp <- function(all_cp, prefix = "12", suffix = "") {
all_written <- list()
for (g in seq_len(length(all_cp))) {
name <- names(all_cp)[g]
datum <- all_cp[[name]]
filename <- glue("{prefix}enrichment_excel/{name}_cprofiler{suffix}-v{ver}.xlsx")
written <- sm(write_cp_data(datum, excel = filename))
all_written[[g]] <- written
}
return(all_written)
}
write_all_gp <- function(all_gp, prefix = "13", suffix = "") {
all_written <- list()
for (g in seq_len(length(all_gp))) {
name <- names(all_gp)[g]
datum <- all_gp[[name]]
filename <- glue("{prefix}enrichment_excel/{name}_gprofiler{suffix}-v{ver}.xlsx")
written <- sm(write_gprofiler_data(datum, excel = filename))
all_written[[g]] <- written
}
return(all_written)
}
write_all_en <- function(all_en, prefix = "14", suffix = "") {
all_written <- list()
for (e in seq_len(length(all_en))) {
name <- names(all_en)[e]
datum <- all_en[[name]]
filename <- glue("{prefix}enrichment_excel/{name}_enricher{suffix}-v{ver}.xlsx")
written <- sm(write_enricher_data(datum, excel = filename))
all_written[[e]] <- written
}
return(all_written)
}24.3.2 Extract genes included for each set of contrasts
Now, using that function, pull out the gene IDs of genes we do not trust because they were too high in wt for every contrast we are likely to perform.
all_genes <- rownames(assay(v3_pairwise_input))
time_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
time_keepers, all_genes)## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The unique union of them is 2113 genes.## Deleting the file included_genes/inc_t_het_dlgn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The unique union of them is 2716 genes.## Deleting the file included_genes/inc_t_ko_dlgn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1086 genes.## Deleting the file included_genes/inc_t_het_retina-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1664 genes.## Deleting the file included_genes/inc_t_ko_retina-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 238 genes.## Deleting the file included_genes/inc_t_het_scn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The unique union of them is 120 genes.## Deleting the file included_genes/inc_t_ko_scn-v20260314.xlsx before writing the tables.location_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
location_keepers, all_genes)## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1134 genes.## Deleting the file included_genes/inc_dr_p08_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 2361 genes.## Deleting the file included_genes/inc_dr_p15_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1883 genes.## Deleting the file included_genes/inc_dr_p08_ko-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The unique union of them is 2843 genes.## Deleting the file included_genes/inc_dr_p15_ko-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1188 genes.## Deleting the file included_genes/inc_sr_p08_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 417 genes.## Deleting the file included_genes/inc_sr_p15_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The unique union of them is 1624 genes.## Deleting the file included_genes/inc_sr_p08_ko-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The unique union of them is 355 genes.## Deleting the file included_genes/inc_sr_p15_ko-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 402 genes.## Deleting the file included_genes/inc_ds_p08_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The unique union of them is 2080 genes.## Deleting the file included_genes/inc_ds_p15_het-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The unique union of them is 493 genes.## Deleting the file included_genes/inc_ds_p08_ko-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The unique union of them is 2550 genes.## Deleting the file included_genes/inc_ds_p15_ko-v20260314.xlsx before writing the tables.genotype_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
genotype_keepers, all_genes)## The set of unique genes higher in p08_ko_dlgn vs. wt is 397.## The set of unique genes higher in p08_het_dlgn vs. wt is 180.## The unique union of them is 501 genes.## Deleting the file included_genes/inc_kh_p08_dlgn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_dlgn vs. wt is 2544.## The set of unique genes higher in p15_het_dlgn vs. wt is 2067.## The unique union of them is 2773 genes.## Deleting the file included_genes/inc_kh_p15_dlgn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_ko_retina vs. wt is 1551.## The set of unique genes higher in p08_het_retina vs. wt is 976.## The unique union of them is 1760 genes.## Deleting the file included_genes/inc_kh_p08_retina-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_retina vs. wt is 347.## The set of unique genes higher in p15_het_retina vs. wt is 402.## The unique union of them is 571 genes.## Deleting the file included_genes/inc_kh_p15_retina-v20260314.xlsx before writing the tables.## The set of unique genes higher in p08_ko_scn vs. wt is 112.## The set of unique genes higher in p08_het_scn vs. wt is 225.## The unique union of them is 312 genes.## Deleting the file included_genes/inc_kh_p08_scn-v20260314.xlsx before writing the tables.## The set of unique genes higher in p15_ko_scn vs. wt is 8.## The set of unique genes higher in p15_het_scn vs. wt is 15.## The unique union of them is 18 genes.## Deleting the file included_genes/inc_kh_p15_scn-v20260314.xlsx before writing the tables.24.4 Perform the DE analyses and exclude the target genes
24.4.1 Genotype
genotype_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
keepers = genotype_keepers, model_svs = "svaseq")## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 109425 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2
## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 6 comparisons.24.4.2 Location
location_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
keepers = location_keepers, model_svs = "svaseq")## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 109425 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2
## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 12 comparisons.24.4.3 Time
time_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
keepers = time_keepers, model_svs = "svaseq")## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 109425 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2
## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 6 comparisons.It is near here when the computer sometimes fails with no more tempfiles. In another window I am messing with tempfile() in R to try to understand where it is going off the rails…
24.5 Extract the relevant tables and include genes lower in wt
24.5.1 Genotype contrasts
I will start with the tables and no inclusions so I can check my work.
In this first block I will explain a little more thoroughly what is going on:
- Dump the full table of the contrasts I defined above comparing the 3 genotypes across time/location.
- Iterate over each of those contrasts and do the following:
- Extract the name of the contrast, ‘kh_p08_dlgn’ for example
- Yank out that specific entry from the keeper list and its name
- Yank out the corresponding set of genes to include from the inclusions data structure.
- Create a filename given the name in (a) above and the logFC cutoff chosen for the inclusions (I am assuming we may change this)
- Given (b), (c), and (d), extract the corresponding table from the differential expression analysis and include the appropriate genes.
genotype_tables_full <- combine_de_tables(
genotype_de, keepers = genotype_keepers, label_column = label_column,
fancy = TRUE,
excel = glue("08full_contrasts_excel/genotype_full_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p08_ko_dlgn_vs_p08_het_dlgn 24 2 42
## 2 p15_ko_dlgn_vs_p15_het_dlgn 50 2 81
## 3 p08_ko_retina_vs_p08_het_retina 6 2 9
## 4 p15_ko_retina_vs_p15_het_retina 9 5 6
## 5 p08_ko_scn_vs_p08_het_scn 54 135 80
## 6 p15_ko_scn_vs_p15_het_scn 0 16 3
## edger_sigdown limma_sigup limma_sigdown
## 1 1 41 3
## 2 3 0 0
## 3 2 3 1
## 4 4 0 3
## 5 139 32 28
## 6 29 0 1## Plot describing unique/shared genes in a differential expression table.
genotype_sig_full <- extract_significant_genes(
genotype_tables_full, according_to = "deseq",
excel = glue("08full_contrasts_excel/genotype_full_sig-v{ver}.xlsx"))
genotype_sig_full## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## kh_p08_dlgn 24 2
## kh_p15_dlgn 50 2
## kh_p08_retina 6 2
## kh_p15_retina 9 5
## kh_p08_scn 54 135
## kh_p15_scn 0 16
24.5.2 Search Enrichment of these sets
In this run, we will search the full set of genes, next we will only do the inclusions.
genotype_full_gp <- all_gprofiler(genotype_sig_full, species = "mmusculus",
excel = "09full_contrasts_enrich/genotype_full_gprofiler.xlsx")## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.
## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.
## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.genotype_full_cp <- all_cprofiler(genotype_sig_full, genotype_tables_full,
orgdb = "org.Mm.eg.db", go_level = go_level, organism = "mouse",
orgdb_from = "ENSEMBL", max_groupsize = max_groupsize,
excel = "09full_contrasts_enrich/genotype_full_cprofiler.xlsx")## Error : unable to find an inherited method for function 'metadata' for signature 'x = "NULL"'
## Error : unable to find an inherited method for function 'metadata' for signature 'x = "NULL"'## Error in `simple_cl[["kegg_universe"]]`:
## ! subscript out of boundsgenotype_full_upset <- upsetr_sig(genotype_sig_full)
genotype_full_intersects <- write_upset_groups(genotype_full_upset,
excel = "09full_contrasts_intersections/genotype_full_gene_groups.xlsx")genotype_tables <- list()
genotype_sig <- list()
genotype_gp <- list()
genotype_cp <- list()
for (k in seq_along(genotype_keepers)) {
name <- names(genotype_keepers)[k]
message("Examining ", name)
keeper <- genotype_keepers[name]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- genotype_inclusions[[include_df_name]]
includes <- genotype_inclusions[[include_name]]
summary(rownames(genotype_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
include_filename <- glue("genotype_contrasts/genotype_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
include_sig_filename <- glue("genotype_contrasts/genotype_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
genotype_tables[[name]] <- combine_de_tables(
genotype_de, extra_annot = include_df,
keepers = keeper, label_column = label_column,
excel = include_filename, wanted_genes = includes)
print(genotype_tables[[name]])
genotype_sig[[name]] <- extract_significant_genes(
genotype_tables[[name]], according_to = "deseq",
excel = include_sig_filename)
print(genotype_sig[[name]])
num_rows <- nrow(genotype_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(genotype_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
if (num_rows >= 10) {
message("Performing gprofiler/clusterProfiler.")
genotype_gp[[name]] <- all_gprofiler(genotype_sig[[name]], species = "mmusculus")
gp_written <- write_all_gp(genotype_gp[[name]])
genotype_cp[[name]] <- all_cprofiler(genotype_sig[[name]], genotype_tables[[name]],
orgdb = "org.Mm.eg.db", kegg_organism = "mmu")
cp_written <- write_all_cp(genotype_cp[[name]])
} else {
warning("There are less than 10 genes up and down in the ", name, " comparison.")
message("There are less than 10 genes up and down in the ", name, " comparison.")
}
}## Examining kh_p08_dlgn## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p08_ko_dlgn_vs_p08_het_dlgn 23 1 30
## edger_sigdown limma_sigup limma_sigdown
## 1 0 26 0## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## kh_p08_dlgn 23 1
## There are 24 significant up and down genes.## Performing gprofiler/clusterProfiler.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Warning in simple_clusterprofiler(sig_genes = structure(list(ensembl_gene_id =
## c("ENSMUSG00000041144", : No genes were found between the significant genes and
## the universe.## Error in testForValidKeytype(x, keytype) :
## 'keytype' must be a a single string## Error in `simple_cl[["kegg_universe"]]`:
## ! subscript out of boundsA few specific plots of interest: Colenso asked to label a few genes for the knockout/het p08_retinas, p08_scn, and p08_dlgn: either the top-15 or all significant. I am pretty sure if I tell it 15 and there are not that many, it will just do the significant? Let us find out!
24.5.2.1 ko/het for p08 retinas
For some crazy reason, this plot is double-labelling!
table_name <- "kh_p08_retina"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting <- c("Opn4", "Gm9008", "Lrr1", "Cnbd1")
kh_p08_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
label_column = "mgi_symbol", label = interesting, alpha = 1.0,
size = 4)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Warning in pp(file = "images/kh_p08_retina_volcano.pdf", width = 9, height =
## 9): The directory: images does not exist, will attempt to create it.## Error:
## ! object 'kh_p08_retina_volcano' not found## Error:
## ! object 'kh_p08_retina_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p08_retina_ma' not found## Error:
## ! object 'kh_p08_retina_ma' not found24.5.2.2 ko/het p08 SCN
Holy crappers, this plot did not double label; oooh I have a check in my plotter to see if there are too few/too many labels and I foolishly allowed it to concatenate the labels! What in the crap was I thinking?
I am going to make an executive decision for this plot, 15 is too many and makes it crazy cluttered.
24.5.3 Repeat this with two sets of genes
table_name <- "kh_p08_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Fign", "Nrn1", "Dpysl2", "Actb", "Fgf9", "Otx2", "Sec23",
"Ncam1", "Map4", "Sec22b", "Nlgn3", "Marcks", "Cd47",
"Dpysl3", "Lin7c", "Cadm1", "Snx12", "Rhoa", "Inpp5f",
"Atg12", "Set", "Gsk3b", "Pdcd4", "Gabra2", "Tmco1", "Anapc16")
kh_p08_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0,
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]])## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'kh_p08_scn_volcano' not found## Error:
## ! object 'kh_p08_scn_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p08_scn_ma' not found## Error:
## ! object 'kh_p08_scn_ma' not found24.5.3.1 Same plot but a different set of labeled genes
table_name <- "kh_p08_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c(
"Anapc16", "Gabra2", "Tmco1", "Sod2", "Fgf9", "Pdcd4", "Rhoa", "Gsk3b", "Foxp1",
"Ncam1", "Marcks", "Fign", "Dpysl3", "Inpp5f", "Cadm1", "Map4", "Ugcg", "Elovl4",
"Elavl1", "Cfl2", "Tnnt1", "Gnb1", "Impact", "Nrn1", "Nlgn3", "Actb", "Cd47",
"Sec22b", "Slc17a7", "Vglut1", "Actb", "B4galt5", "Foxp1", "Otx2", "Lin7c",
"Snx12", "Atg12", "Set")
kh_p08_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'kh_p08_scn_volcano' not found## Error:
## ! object 'kh_p08_scn_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p08_scn_ma' not found## Error:
## ! object 'kh_p08_scn_ma' not found24.5.3.2 ko/het p08 dLGN
table_name <- "kh_p08_dlgn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
kh_p08_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
label_column = "mgi_symbol", label = 10, size = 4, alpha = 1.0)
pp(file = "images/kh_p08_dlgn_volcano.pdf", width = 9, height = 9)
kh_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
kh_p08_dlgn_volcano[["plot"]]
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = 10)
pp(file = "images/kh_p08_dlgn_ma.pdf", width = 9, height = 9)
kh_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
kh_p08_dlgn_ma[["plot"]]
24.5.3.3 ko/het for p15 retinas
For some crazy reason, this plot is double-labelling!
table_name <- "kh_p15_retina"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting <- c("Opn4", "Gm9008", "Lrr1", "Cnbd1")
kh_p15_retina_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
label_column = "mgi_symbol", label = interesting, alpha = 1.0,
size = 4)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.pp(file = "images/kh_p15_retina_volcano.pdf", width = 9, height = 9)
kh_p15_retina_volcano[["plot"]]## Error:
## ! object 'kh_p15_retina_volcano' not found## Error:
## ! object 'kh_p15_retina_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_retina_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p15_retina_ma' not found## Error:
## ! object 'kh_p15_retina_ma' not found24.5.3.4 ko/het p15 SCN
Holy crappers, this plot did not double label; oooh I have a check in my plotter to see if there are too few/too many labels and I foolishly allowed it to concatenate the labels! What in the crap was I thinking?
I am going to make an executive decision for this plot, 15 is too many and makes it crazy cluttered.
24.5.4 Repeat this with two sets of genes
table_name <- "kh_p15_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Fign", "Nrn1", "Dpysl2", "Actb", "Fgf9", "Otx2", "Sec23",
"Ncam1", "Map4", "Sec22b", "Nlgn3", "Marcks", "Cd47",
"Dpysl3", "Lin7c", "Cadm1", "Snx12", "Rhoa", "Inpp5f",
"Atg12", "Set", "Gsk3b", "Pdcd4", "Gabra2", "Tmco1", "Anapc16")
kh_p15_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
label_column = "mgi_symbol", size = 4, alpha = 1.0,
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]])## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'kh_p15_scn_volcano' not found## Error:
## ! object 'kh_p15_scn_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p15_scn_ma' not found## Error:
## ! object 'kh_p15_scn_ma' not foundtable_name <- "kh_p15_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c(
"Anapc16", "Gabra2", "Tmco1", "Sod2", "Fgf9", "Pdcd4", "Rhoa", "Gsk3b", "Foxp1",
"Ncam1", "Marcks", "Fign", "Dpysl3", "Inpp5f", "Cadm1", "Map4", "Ugcg", "Elovl4",
"Elavl1", "Cfl2", "Tnnt1", "Gnb1", "Impact", "Nrn1", "Nlgn3", "Actb", "Cd47",
"Sec22b", "Slc17a7", "Vglut1", "Actb", "B4galt5", "Foxp1", "Otx2", "Lin7c",
"Snx12", "Atg12", "Set")
kh_p15_scn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'kh_p15_scn_volcano' not found## Error:
## ! object 'kh_p15_scn_volcano' not found## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_scn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p15_scn_ma' not found## Error:
## ! object 'kh_p15_scn_ma' not found24.5.4.1 ko/het p15 dLGN
table_name <- "kh_p15_dlgn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
kh_p15_dlgn_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
label_column = "mgi_symbol", label = 10, size = 4, alpha = 1.0)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'kh_p15_dlgn_volcano' not found## Error:
## ! object 'kh_p15_dlgn_volcano' not found## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_dlgn_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = 10)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'kh_p15_dlgn_ma' not found## Error:
## ! object 'kh_p15_dlgn_ma' not found24.5.5 Location contrasts with genes removed/kept
Repeat the same block with a find/replace of genotype/location.
location_tables_full <- combine_de_tables(
location_de, keepers = location_keepers, label_column = label_column,
excel = glue("full_contrasts/location_full_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p08_het_dlgn_vs_p08_het_retina 2165 1562 2212
## 2 p15_het_dlgn_vs_p15_het_retina 2437 3369 2587
## 3 p08_ko_dlgn_vs_p08_ko_retina 2180 1868 2144
## 4 p15_ko_dlgn_vs_p15_ko_retina 2715 3942 2934
## 5 p08_het_scn_vs_p08_het_retina 2634 1707 2586
## 6 p15_het_scn_vs_p15_het_retina 2841 2395 2716
## 7 p08_ko_scn_vs_p08_ko_retina 2728 1705 2644
## 8 p15_ko_scn_vs_p15_ko_retina 2613 3005 2612
## 9 p08_het_dlgn_vs_p08_het_scn 648 788 751
## 10 p15_het_dlgn_vs_p15_het_scn 1708 2796 1984
## 11 p08_ko_dlgn_vs_p08_ko_scn 1002 1342 1115
## 12 p15_ko_dlgn_vs_p15_ko_scn 1829 2529 2158
## edger_sigdown limma_sigup limma_sigdown
## 1 1632 1886 1660
## 2 3339 2748 2639
## 3 2077 2104 1963
## 4 3847 3236 2962
## 5 1882 2226 1889
## 6 2616 2730 2366
## 7 1922 2386 2160
## 8 3143 2812 2623
## 9 780 647 766
## 10 2623 1979 2034
## 11 1439 1169 1317
## 12 2396 1878 2000## Plot describing unique/shared genes in a differential expression table.
location_sig_full <- extract_significant_genes(
location_tables_full, according_to = "deseq",
excel = glue("full_contrasts/location_full_sig-v{ver}.xlsx"))
location_sig_full## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## dr_p08_het 2165 1562
## dr_p15_het 2437 3369
## dr_p08_ko 2180 1868
## dr_p15_ko 2715 3942
## sr_p08_het 2634 1707
## sr_p15_het 2841 2395
## sr_p08_ko 2728 1705
## sr_p15_ko 2613 3005
## ds_p08_het 648 788
## ds_p15_het 1708 2796
## ds_p08_ko 1002 1342
## ds_p15_ko 1829 2529
location_full_upset <- upsetr_sig(location_sig_full)
##location_full_intersects <- write_upset_groups(
## location_full_upset,
## excel = "excel/location_full_gene_groups.xlsx")
location_tables <- list()
location_sig <- list()
location_gp <- list()
location_cp <- list()
for (k in seq_along(location_keepers)) {
name <- names(location_keepers)[k]
message("Examining ", name)
keeper <- location_keepers[name]
includes <- location_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- location_inclusions[[include_df_name]]
includes <- location_inclusions[[include_name]]
summary(rownames(location_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
include_filename <- glue("location_contrasts/location_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
include_sig_filename <- glue("location_contrasts/location_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
location_tables[[name]] <- combine_de_tables(
location_de, extra_annot = include_df,
keepers = keeper, label_column = label_column,
excel = include_filename, wanted_genes = includes)
print(location_tables[[name]])
location_sig[[name]] <- extract_significant_genes(
location_tables[[name]], according_to = "deseq",
excel = include_sig_filename)
print(location_sig[[name]])
num_rows <- nrow(location_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(location_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
if (num_rows > 10) {
location_gp[[name]] <- all_gprofiler(location_sig[[name]], species = "mmusculus")
gp_written <- write_all_gp(genotype_gp[[name]])
location_cp[[name]] <- all_cprofiler(location_sig[[name]], location_tables[[name]],
orgdb = "org.Mm.eg.db", organism = "mouse")
cp_written <- write_all_cp(genotype_cp[[name]])
}
}## Examining dr_p08_het## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p08_het_dlgn_vs_p08_het_retina 259 81 259
## edger_sigdown limma_sigup limma_sigdown
## 1 85 240 81## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## dr_p08_het 259 81
## There are 340 significant up and down genes.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Warning in simple_clusterprofiler(sig_genes = structure(list(ensembl_gene_id =
## c("ENSMUSG00000021685", : No genes were found between the significant genes and
## the universe.## Error in testForValidKeytype(x, keytype) :
## 'keytype' must be a a single string## Error in `simple_cl[["kegg_universe"]]`:
## ! subscript out of boundsColenso sent a specific query of interest, comparing SCN vs. Retinas at p08 in the heterozygotes including a set of genes of particular interest. Perhaps I can use some of these as markers to quality control my work in the future?
Here are the genes:
Opn4, Eomes, Trpc7, Oprm1, Nr4a3, Tbx20, Irx6, AW551984, Pcdh19, Adcyap1, Baiap3, Chl1, Grin3a, Igf1, Gria1, Grin2d, Grin3a, Chrna6, Chrna3, Htr5a, Htr2a, Htr7, Irx4, PlxnC1, Sema6d, Sema4f, Sema4a, Sema6b, Lrrc4b, Lrrc58, Lrrc3b, Wnt4, Wnt9b, Ctxn3, Tenm1, Gna14, Rgs4, Rgs6, Rgs5
table_input <- location_tables[["sr_p08_het"]]
table_name <- "sr_p08_het"
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Opn4", "Eomes", "Trpc7", "Oprm1", "Nr4a3", "Tbx20",
"Irx6", "AW551984", "Pcdh19", "Adcyap1r1", "Baiap3",
"Chl1", "Grin3a", "Igf1", "Gria1", "Grin2d", "Grin3a",
"Chrna6", "Chrna3", "Htr5a", "Htr2a", "Htr7", "Irx4",
"PlxnC1", "Sema6d", "Sema4f", "Sema4a", "Sema6b", "Lrrc4b",
"Lrrc58", "Lrrc3b", "Wnt4", "Wnt9b", "Ctxn3", "Tenm1", "Gna14",
"Rgs4", "Rgs6", "Rgs5", "Pou4f2", "Chrnb3", "Bcan")
sr_p08_het_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
size = 4)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'sr_p08_het_volcano' not found## Error:
## ! object 'sr_p08_het_volcano' not foundsr_p08_het_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'sr_p08_het_ma' not found## Error:
## ! object 'sr_p08_het_ma' not found24.5.6 Also the ko
table_input <- location_tables[["sr_p08_ko"]]
table_name <- "sr_p08_ko"
table <- table_input[["data"]][[table_name]]
sr_p08_ko_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
size = 4)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'sr_p08_ko_volcano' not found## Error:
## ! object 'sr_p08_ko_volcano' not foundsr_p08_ko_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'sr_p08_ko_ma' not found## Error:
## ! object 'sr_p08_ko_ma' not foundtable_input <- location_tables[["sr_p15_het"]]
table_name <- "sr_p15_het"
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Opn4", "Eomes", "Trpc7", "Oprm1", "Nr4a3", "Tbx20",
"Irx6", "AW551984", "Pcdh19", "Adcyap1r1", "Baiap3",
"Chl1", "Grin3a", "Igf1", "Gria1", "Grin2d", "Grin3a",
"Chrna6", "Chrna3", "Htr5a", "Htr2a", "Htr7", "Irx4",
"PlxnC1", "Sema6d", "Sema4f", "Sema4a", "Sema6b", "Lrrc4b",
"Lrrc58", "Lrrc3b", "Wnt4", "Wnt9b", "Ctxn3", "Tenm1", "Gna14",
"Rgs4", "Rgs6", "Rgs5", "Pou4f2", "Chrnb3", "Bcan")
sr_p15_het_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
size = 4)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'sr_p15_het_volcano' not found## Error:
## ! object 'sr_p15_het_volcano' not foundsr_p15_het_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["het_retina"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'sr_p15_het_ma' not found## Error:
## ! object 'sr_p15_het_ma' not found24.5.7 Also the ko
table_input <- location_tables[["sr_p15_ko"]]
table_name <- "sr_p15_ko"
table <- table_input[["data"]][[table_name]]
sr_p15_ko_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["ko_retina"]], color_high = colors[["ko_scn"]],
label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
size = 4, min.segment.length = 0, point.padding = 0.2)## Error in `plot_volcano_condition_de()`:
## ! Column: deseq_logfc is not in the table.## Error:
## ! object 'sr_p15_ko_volcano' not found## Error:
## ! object 'sr_p15_ko_volcano' not foundsr_p15_ko_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)## The column: mgi_symbol is not in the data, using rownames.## Warning in max(newdf[["avg"]]): no non-missing arguments to max; returning -Inf## Warning in plot_ma_condition_de(table, table_name, expr_col = "deseq_basemean",
## : NAs introduced by coercion## Error in `[[<-.data.frame`:
## ! replacement has 1 row, data has 0## Error:
## ! object 'sr_p15_ko_ma' not found## Error:
## ! object 'sr_p15_ko_ma' not found24.5.7.1 Test a specific location query for duplicated IDs
Let us see if any Ensembl gene IDs and/or MGI IDs are shared in the worksheet location_sr_p08_ko_including_wt_0.1_decreased_sig up/down.
test_table_up <- location_sig[["sr_p08_ko"]][["deseq"]][["ups"]][[1]]
test_table_down <- location_sig[["sr_p08_ko"]][["deseq"]][["downs"]][[1]]
query <- list("up" = rownames(test_table_up),
"down" = rownames(test_table_down))
query_upset <- UpSetR::fromList(query)
UpSetR::upset(query_upset)## Error in `start_col:end_col`:
## ! argument of length 0query <- list("up" = test_table_up[["mgi_symbol"]],
"down" = test_table_down[["mgi_symbol"]])
query_upset <- UpSetR::fromList(query)
UpSetR::upset(query_upset)## Error in `start_col:end_col`:
## ! argument of length 024.5.8 And time
time_tables_full <- combine_de_tables(
time_de, keepers = time_keepers,
label_column = label_column,
excel = glue("full_contrasts/time_full_tables-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.time_sig_full <- extract_significant_genes(
time_tables_full, according_to = "deseq",
excel = glue("full_contrasts/time_full_sig-v{ver}.xlsx"))
time_tables <- list()
time_sig <- list()
time_gp <- list()
time_cp <- list()
for (k in seq_along(time_keepers)) {
name <- names(time_keepers)[k]
message("Examining ", name)
keeper <- time_keepers[name]
includes <- time_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- time_inclusions[[include_df_name]]
includes <- time_inclusions[[include_name]]
summary(rownames(time_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
include_filename <- glue("time_contrasts/time_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
include_sig_filename <- glue("time_contrasts/time_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
time_tables[[name]] <- combine_de_tables(
time_de, extra_annot = include_df,
keepers = keeper, label_column = label_column,
excel = include_filename, wanted_genes = includes)
print(time_tables[[name]])
time_sig[[name]] <- extract_significant_genes(
time_tables[[name]], according_to = "deseq",
excel = include_filename)
print(time_sig[[name]])
num_rows <- nrow(time_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(time_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
if (num_rows > 10) {
time_gp[[name]] <- all_gprofiler(time_sig[[name]], species = "mmusculus")
gp_written <- write_all_gp(time_gp[[name]])
time_cp[[name]] <- all_cprofiler(time_sig[[name]], time_tables[[name]],
orgdb = "org.Mm.eg.db")
cp_written <- write_all_cp(time_cp[[name]])
}
}## Examining t_het_dlgn## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 p15_het_dlgn_vs_p08_het_dlgn 397 14 431
## edger_sigdown limma_sigup limma_sigdown
## 1 14 359 13## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
## Deleting the file time_contrasts/time_t_het_dlgn_including_wt_0.1_decreased_table-v20260314.xlsx before writing the tables.## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## t_het_dlgn 397 14
## There are 411 significant up and down genes.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Error : Request to g:Profiler failed (HTTP 400). Please check your input or organism argument.
## API message: No legal source provided. Saw ['MIRNA']
## If the issue persists, please contact biit.support@ut.ee with a reproducible example.## Warning in simple_clusterprofiler(sig_genes = structure(list(ensembl_gene_id =
## c("ENSMUSG00000021948", : No genes were found between the significant genes and
## the universe.## Error in testForValidKeytype(x, keytype) :
## 'keytype' must be a a single string## Error in `simple_cl[["kegg_universe"]]`:
## ! subscript out of bounds25 Translatome queries
In conversation with Colenso, he spoke about a series of contrasts which would be interesting to attempt in order to query the changes across both locations and genotypes and/or both locations and time, thus:
(p08_het_scn / p08_het_retina) / (p08_ko_scn / p08_ko_retina)
as an example. We can definitely do these, but they do not work for all methods employed (I think they work best with limma and edgeR).
Lets find out!
25.1 Two scn/retina comparisons
- (p08_het_scn / p08_het_retina) / (p08_ko_scn / p08_ko_retina)
- (p15_het_scn / p15_het_retina) / (p15_ko_scn / p15_ko_retina)
scn_extra <- glue("\\
p08het = (conditionp08_het_scn - conditionp08_het_retina), \\
p08ko = (conditionp08_ko_scn - conditionp08_ko_retina), \\
p08het_vs_p08ko = (conditionp08_het_scn - conditionp08_het_retina) - (conditionp08_ko_scn - conditionp08_ko_retina), \\
p15het = (conditionp15_het_scn - conditionp15_het_retina), \\
p15ko = (conditionp15_ko_scn - conditionp15_ko_retina), \\
p15het_vs_p15ko = (conditionp15_het_scn - conditionp15_het_retina) - (conditionp15_ko_scn - conditionp15_ko_retina)")
scn_translatome_de_keepers <- list(
"p08het" = c("p08_het_scn", "p08_het_retina"),
"p08ko" = c("p08_ko_scn", "p08_ko_retina"),
"p15het" = c("p15_het_scn", "p15_het_retina"),
"p15ko" = c("p15_ko_scn", "p15_ko_retina"))
scn_translatome_keepers <- list(
"p08het" = c("p08_het_scn", "p08_het_retina"),
"p08ko" = c("p08_ko_scn", "p08_ko_retina"),
"p08_scn_translatome" = c("p08het", "p08ko"),
"p15het" = c("p15_het_scn", "p15_het_retina"),
"p15ko" = c("p15_ko_scn", "p15_ko_retina"),
"p15_scn_translatome" = c("p15het", "p15ko"))
filt <- normalize(v3_pairwise_input, filter = TRUE)## Removing 10162 low-count genes (15263 remaining).limma_test <- limma_pairwise(filt,
keepers = scn_translatome_de_keepers,
model_fstring = "~ 0 + condition",
model_svs = FALSE, extra_contrastrs = scn_extra)## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2edger_test <- edger_pairwise(filt,
keepers = scn_translatome_de_keepers,
model_fstring = "~ 0 + condition",
model_svs = FALSE, extra_contrasts = scn_extra)## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2scn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = scn_translatome_de_keepers,
model_svs = FALSE,
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = scn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## scn_translatome_de_keepers, : This will likely fail because of how the keepers
## and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## The contrast p08het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p08ko is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p08het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15ko is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15het is not in the results.## If this is not an extra contrast, then this is an error.## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2scn_combined_test <- combine_de_tables(
scn_translatome_de, keepers = scn_translatome_keepers,
excel = glue("translatome/test_scn_translatome_unfiltered_nosva-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08ko or conditionp08het.## coefficient limma did not find p08ko or p08het.## coefficient edger did not find conditionp15ko or conditionp15het.## coefficient limma did not find p15ko or p15het.## Looking for subscript invalid names, end of extract_keepers.scn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = scn_translatome_de_keepers,
model_svs = "svaseq",
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = scn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## scn_translatome_de_keepers, : This will likely fail because of how the keepers
## and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Error in `adjuster_svs()`:
## ! unused arguments (do_basic = FALSE, do_dream = FALSE, do_noiseq = FALSE, do_ebseq = FALSE)scn_combined_test_sva <- combine_de_tables(
scn_translatome_de_sva, keepers = scn_translatome_keepers,
excel = glue("translatome/test_scn_translatome_unfiltered_sva-v{ver}.xlsx"))## Error:
## ! object 'scn_translatome_de_sva' not found25.1.1 Subtracting DESeq2 results: p08 scn het vs ko
p08_scn_combined_deseq <- subtract_deseq_results(
first_table = scn_combined_test[["data"]][["p08het"]],
second_table = scn_combined_test[["data"]][["p08ko"]],
first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
first_p = "deseq_adjp", second_p = "deseq_adjp",
first_name = "het", second_name = "ko",
excel = glue("translatome/translatome_p08_scn_combined_deseq-v{ver}.xlsx"))## Error in `subtract_deseq_results()`:
## ! could not find function "subtract_deseq_results"25.1.2 Subtracting DESeq2 results: p15 scn het vs ko
p15_scn_combined_deseq <- subtract_deseq_results(
first_table = scn_combined_test[["data"]][["p15het"]],
second_table = scn_combined_test[["data"]][["p15ko"]],
first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
first_p = "deseq_adjp", second_p = "deseq_adjp",
first_name = "het", second_name = "ko",
excel = glue("translatome/translatome_p15_scn_combined_deseq-v{ver}.xlsx"))## Error in `subtract_deseq_results()`:
## ! could not find function "subtract_deseq_results"25.2 One dlgn/retina comparison
- (p08_het_dlgn / p08_het_retina) / (p08_ko_dlgn / p08_ko_retina)
p08_dlgn_extra <- "p08het_vs_p08ko = (conditionp08_het_dlgn - conditionp08_het_retina) - (conditionp08_ko_dlgn - conditionp08_ko_retina)"
p08_dlgn_translatome_de_keepers <- list(
"p08het" = c("p08_het_dlgn", "p08_het_retina"),
"p08ko" = c("p08_ko_dlgn", "p08_ko_retina"))
p08_dlgn_translatome_keepers <- list(
"p08_het_dlgn_vs_retina" = c("p08_het_dlgn", "p08_het_retina"),
"p08_ko_dlgn_vs_retina" = c("p08_ko_dlgn", "p08_ko_retina"),
"p08_dlgn_translatome" = c("p08het", "p08ko"))
p08_dlgn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = p08_dlgn_translatome_de_keepers,
model_svs = FALSE,
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = p08_dlgn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## p08_dlgn_translatome_de_keepers, : This will likely fail because of how the
## keepers and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## The contrast p08het is not in the results.## If this is not an extra contrast, then this is an error.## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2p08_dlgn_combined_test <- combine_de_tables(
p08_dlgn_translatome_de, keepers = p08_dlgn_translatome_keepers,
label_column = label_column,
excel = glue("translatome/test_p08_dlgn_translatome_unfiltered_nosva-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08ko or conditionp08het.## coefficient limma did not find p08ko or p08het.## Looking for subscript invalid names, end of extract_keepers.p08_dlgn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = p08_dlgn_translatome_de_keepers,
model_svs = "svaseq",
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = p08_dlgn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## p08_dlgn_translatome_de_keepers, : This will likely fail because of how the
## keepers and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Error in `adjuster_svs()`:
## ! unused arguments (do_basic = FALSE, do_dream = FALSE, do_noiseq = FALSE, do_ebseq = FALSE)p08_dlgn_combined_test_sva <- combine_de_tables(
p08_dlgn_translatome_de_sva, keepers = p08_dlgn_translatome_keepers,
label_column = label_column,
excel = glue("translatome/test_p08_dlgn_translatome_unfiltered_sva-v{ver}.xlsx"))## Error:
## ! object 'p08_dlgn_translatome_de_sva' not found25.2.1 Subtracting the DESeq2 results
25.3 Two scn/retina comparisons (p15/p08 across het/ko)
- (p15_het_scn / p15_het_retina) / (p08_het_scn / p08_het_retina)
- (p15_ko_scn / p15_ko_retina) / (p08_ko_scn / p08_ko_retina)
time_scn_extra <- glue("\\
p15het = (conditionp15_het_scn - conditionp15_het_retina), \\
p08het = (conditionp08_het_scn - conditionp08_het_retina), \\
p15het_vs_p08het = (conditionp15_het_scn - conditionp15_het_retina) - (conditionp08_het_scn - conditionp08_het_retina),
p15ko = (conditionp15_ko_scn - conditionp15_ko_retina), \\
p08ko = (conditionp08_ko_scn - conditionp08_ko_retina), \\
p15ko_vs_p08ko = (conditionp15_ko_scn - conditionp15_ko_retina) - (conditionp08_ko_scn - conditionp08_ko_retina)")
time_scn_translatome_de_keepers <- list(
"p15het" = c("p15_het_scn", "p15_het_retina"),
"p08het" = c("p08_het_scn", "p08_het_retina"),
"p15ko" = c("p15_ko_scn", "p15_ko_retina"),
"p08ko" = c("p08_ko_scn", "p08_ko_retina"))
time_scn_translatome_keepers <- list(
"p15het" = c("p15_het_scn", "p15_het_retina"),
"p08het" = c("p08_het_scn", "p08_het_retina"),
"p15ko" = c("p15_ko_scn", "p15_ko_retina"),
"p08ko" = c("p08_ko_scn", "p08_ko_retina"),
"p15_het_sc_vs_retina" = c("p15_het_scn", "p15_het_retina"),
"p08_het_sc_vs_retina" = c("p08_het_scn", "p08_het_retina"),
"scn_het_translatome" = c("p15het", "p08het"),
"scn_ko_translatome" = c("p15ko", "p08ko"))
time_scn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = time_scn_translatome_de_keepers,
model_svs = FALSE,
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = time_scn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## time_scn_translatome_de_keepers, : This will likely fail because of how the
## keepers and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## The contrast p15het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p08het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15het is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15ko is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p08ko is not in the results.## If this is not an extra contrast, then this is an error.## The contrast p15ko is not in the results.## If this is not an extra contrast, then this is an error.## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## conditions
## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2time_scn_translatome_test <- combine_de_tables(
time_scn_translatome_de,
keepers = time_scn_translatome_keepers,
label_column = label_column,
excel = glue("translatome/test_time_scn_translatome_unfiltered_nosva-v{ver}.xlsx"))## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08het or conditionp15het.## coefficient limma did not find p08het or p15het.## coefficient edger did not find conditionp08ko or conditionp15ko.## coefficient limma did not find p08ko or p15ko.## Looking for subscript invalid names, end of extract_keepers.time_scn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
keepers = time_scn_translatome_de_keepers,
model_svs = "svaseq",
model_fstring = "~ 0 + condition",
do_basic = FALSE, do_dream = FALSE,
do_noiseq = FALSE, do_ebseq = FALSE,
extra_contrasts = time_scn_extra)## Warning in all_pairwise(v3_pairwise_input, filter = TRUE, keepers =
## time_scn_translatome_de_keepers, : This will likely fail because of how the
## keepers and extra contrasts are evaluated.## p08_het_dlgn p08_het_retina p08_het_scn p08_ko_dlgn p08_ko_retina
## 3 3 3 3 3
## p08_ko_scn p08_wt_dlgn p08_wt_retina p08_wt_scn p15_het_dlgn
## 3 5 5 3 4
## p15_het_retina p15_het_scn p15_ko_dlgn p15_ko_retina p15_ko_scn
## 4 3 3 3 3
## p15_wt_dlgn p15_wt_retina p15_wt_scn
## 5 5 2## Removing 10162 low-count genes (15263 remaining).## Error in `adjuster_svs()`:
## ! unused arguments (do_basic = FALSE, do_dream = FALSE, do_noiseq = FALSE, do_ebseq = FALSE)time_scn_translatome_test_sva <- combine_de_tables(
time_scn_translatome_de_sva,
keepers = time_scn_translatome_keepers,
label_column = label_column,
excel = glue("translatome/test_time_scn_translatome_unfiltered_sva-v{ver}.xlsx"))## Error:
## ! object 'time_scn_translatome_de_sva' not foundNext step: Perform the retina filter; need to think about the proper union/intersection of the retina/x expression values
In the previous block, we are making 2 global comparisons, here is one of them:
(p15hetscn/p15hetret)/(p08hetscn/p08hetret)
I therefore want to extract the most logical set of genes higher in some/all of these conditions with respect to the corresponding wt conditions. Previously, in section ‘Extract genes included for each set of contrasts’, I attempted to perform this operation for 2 specific wt conditions. When this was performed, it took the unique(union) of the two sets. Thus it stands to reason that I want to take the unique(union) of all 4 in this instance? e.g.:
(p15hetscn > p15wtscn) | (p15hetret > p15wtret) | (p08hetscn > p08wtscn) | (p08hetret > p08wtret)
I kind of think it should be:
((p15hetscn > p15wtscn) | (p15hetret > p15wtret)) & ((p08hetscn > p08wtscn) | (p08hetret > p08wtret))
gross, perhaps I should just do this manually, given that there are only a few putative translatomes to query?
26 Quick and dirty DESeq2 contrast of contrasts
In a fashion similar to how Hector handled the effect of phagocytosis with Laura and Najib a long time ago, I propose to do a simple subtraction of the results of our two contrasts which comprise the translatome query (I was thinking about this last week, thus the inclusion of them in the de tables above). Similarly to the phagocytosis effect, I will simply take the worst posible adjusted p-value. I will repeat this with limma/EdgeR and see how similar the final results are to what those methods provide in the (a/b)/(c/d) comparisons. I am reasonably certain that DESeq2’s results() function has the ability to perform these odd contrasts, but I have never figured out how; perhaps I will use this as a chance to revisit that…
Let us test this idea with the p08 dlgn query, which seeks to compare:
(p08_het_dlgn / p08_het_retina) / (p08_ko_dlgn / p08_ko_retina)
These are maintained in the de_table with the names ‘p08_het_dlgn_vs_retina’ and ‘p08_ko_dlgn_vs_retina’
26.1 p08 dlgn het vs ko
p08_dlgn_combined_deseq <- subtract_deseq_results(
first_table = p08_dlgn_combined_test[["data"]][["p08_het_dlgn_vs_retina"]],
second_table = p08_dlgn_combined_test[["data"]][["p08_ko_dlgn_vs_retina"]],
first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
first_p = "deseq_adjp", second_p = "deseq_adjp",
first_name = "het", second_name = "ko",
excel = glue("translatome/translatome_p08_dlgn_combined_deseq-v{ver}.xlsx"))## Error in `subtract_deseq_results()`:
## ! could not find function "subtract_deseq_results"See how similar these results are to those obtained from limma/edger.
test_columns <- c("edger_logfc", "limma_logfc", "edger_adjp", "limma_adjp")
test_df <- p08_dlgn_combined_test[["data"]][["p08_dlgn_translatome"]][, test_columns]
test_df <- merge(test_df, p08_dlgn_combined_deseq, by = "row.names")## Error in `h()`:
## ! error in evaluating the argument 'y' in selecting a method for function 'merge': object 'p08_dlgn_combined_deseq' not foundrownames(test_df) <- test_df[["Row.names"]]
test_df[["Row.names"]] <- NULL
cor.test(test_df[["limma_logfc"]], test_df[["het_vs_ko_logfc"]])## Error in `cor.test.default()`:
## ! 'y' must be a numeric vector## Error in `cor.test.default()`:
## ! 'y' must be a numeric vector## Error in `h()`:
## ! error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': subscript contains invalid names## NULL## Error in `h()`:
## ! error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': subscript contains invalid names## NULL## So, using the maximum p-value is a complete failure; but the extreme similarities
## between this and edgeR suggest to me that it is likely possible to use the results
## from edgeR without concern (or limma for that matter, it was also extremely similar)
## Or I can spend a little time and collect the numbers on each side of the division
## and calculate a t statistic myself.27 Non-Specific filtering of the translatome data
I have on hand
- p08het_vs_p08ko : (p08_het_scn - p08_het_retina) - (p08_ko_scn - p08_ko_retina)
- p15het_vs_p15ko : (p15_het_scn - p15_het_retina) - (p15_ko_scn - p15_ko_retina)
- p08het_vs_p08ko : (p08_het_dlgn - p08_het_retina) - (p08_ko_dlgn - p08_ko_retina)
- p15het_vs_p08het : (p15_het_scn - p15_het_retina) - (p08_het_scn - p08_het_retina)
- p15ko_vs_p08ko : (p15_ko_scn - p15_ko_retina) - (p08_ko_scn - p08_ko_retina)
I have gene sets up above which define the genes suitable for each of these pieces. There are only 5 comparisons, let us step through them.
27.1 SCN translatome het/ko at p08
The data for this contrast resides in scn_combined_test\(data\)p08_scn_translatome or the same slot of scn_combined_test_sva
- p08_het_scn - p08_het_retina) - (p08_ko_scn - p08_ko_retina)
Thus, the inclusion_sig portions to extract are found in: inclusion_sig[[“deseq”]][[“ups”]], and are named exactly as written above!
p08_het_vs_ko_translatome_unfilt <- scn_combined_test[["data"]][["p08_scn_translatome"]]
num_union <- unique(c(rownames(inclusion_sig[["deseq"]][["ups"]][["p08_het_scn"]]),
rownames(inclusion_sig[["deseq"]][["ups"]][["p08_het_retina"]])))
length(num_union)## [1] 1188den_union <- unique(c(rownames(inclusion_sig[["deseq"]][["ups"]][["p08_ko_scn"]]),
rownames(inclusion_sig[["deseq"]][["ups"]][["p08_ko_retina"]])))
length(den_union)## [1] 1624## [1] 2005both_inter_idx <- num_union %in% den_union
both_inter <- num_union[both_inter_idx]
length(both_inter)## [1] 807keeper <- list("p08_scn_translatome" = c("p08het", "p08ko"))
p08_scn_translatome_union_filtered <- combine_de_tables(
scn_translatome_de, keepers = keeper,
label_column = label_column,
excel = glue("translatome/p08_scn_translatome_union_filtered_nosva-v{ver}.xlsx"),
wanted_genes = both_union)## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08ko or conditionp08het.## coefficient limma did not find p08ko or p08het.## Looking for subscript invalid names, end of extract_keepers.p08_scn_translatome_inter_filtered <- combine_de_tables(
scn_translatome_de, keepers = keeper,
label_column = label_column,
excel = glue("translatome/p08_scn_translatome_intersect_filtered_nosva-v{ver}.xlsx"),
wanted_genes = both_inter)## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08ko or conditionp08het.## coefficient limma did not find p08ko or p08het.## Looking for subscript invalid names, end of extract_keepers.p08_scn_translatome_union_filtered_sva <- combine_de_tables(
scn_translatome_de_sva, keepers = keeper,
label_column = label_column,
excel = glue("translatome/p08_scn_translatome_union_filtered_sva-v{ver}.xlsx"),
wanted_genes = both_union)## Error:
## ! object 'scn_translatome_de_sva' not foundp08_scn_translatome_union_filtered <- combine_de_tables(
scn_translatome_de, keepers = keeper,
label_column = label_column,
excel = glue("translatome/p08_scn_translatome_intersect_filtered_sva-v{ver}.xlsx"),
wanted_genes = both_inter)## Looking for subscript invalid names, start of extract_keepers.## coefficient edger did not find conditionp08ko or conditionp08het.## coefficient limma did not find p08ko or p08het.## Looking for subscript invalid names, end of extract_keepers.28 Venn/UpSet of Retina, SCN, and dLGN DE Genes
Here is a snippet from Rashmi which expresses nicely the DE-result comparisons she is most interested:
Since, I want to know the number of DEG expressed in Retina, SCN and dLGN with respect to genotype, Location and time. I prepared the venn diagram for these comparison:
- Genotype: P8 Ret Het vs KO, P15 Ret Het vs KO, P8 SCN Het vs KO, P15 SCN Het vs KO, P8 dLGN Het vs KO, P15 dLGN Het vs KO
- Location: P8_het Ret vs SCN, P8_KO Ret vs SCN, P15_het Ret vs SCN, P15_KO Ret vs SCN, P8_het Ret vs dLGN, P8_KO Ret vs dLGN, P15_het Ret vs dLGN, P15_KO Ret vs dLGN, P8_het SCN vs dLGN, P8_KO SCN vs dLGN, P15_het SCN vs dLGN, P15_KO SCN vs dLGN.
Since I was interested in understanding the change in local translatome according to Location for different developmental time points for Het and KO. Hence, I tried to generate a venn diagram for Location (Ret and SCN) at developmental time points P8 and P15 for genotype het and KO. So the venn diagram / upset plot will be for location where some genes will be shared/unique for P8_Ret_het, P8_SCN_Het, P15_Ret_HET, P15_SCN_HET. We can prepare an upset plot for P8_Ret_KO, P8_SCN_KO, P15_Ret_KO and P15_SCN_KO also. Or can generate an upset plot by combining both P8_Ret_het, P8_SCN_Het, P15_Ret_HET and P15_SCN_HET and P8_Ret_KO, P8_SCN_KO, P15_Ret_KO and P15_SCN_KO.
Ok, let us see if I can implement this, starting with the genotype query
- Genotype: P8 Ret Het vs KO, P15 Ret Het vs KO, P8 SCN Het vs KO, P15 SCN Het vs KO, P8 dLGN Het vs KO, P15 dLGN Het vs KO
28.1 ko vs het; all locations and times
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("kh_p08_retina", "kh_p15_retina", "kh_p08_scn",
"kh_p15_scn", "kh_p08_dlgn", "kh_p15_dlgn")
table_names %in% names(genotype_sig)## [1] FALSE FALSE FALSE FALSE TRUE FALSEnewsig <- genotype_sig[[1]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- genotype_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- genotype_sig[[name]][["deseq"]][["downs"]][[name]]
}
genotype_upsetr <- upsetr_sig(newsig)## Error in `1:ncol(data)`:
## ! argument of length 0genotype_upset_written <- write_upset_groups(genotype_upsetr, excel = "excel/genotype_upset_groups.xlsx")## Error:
## ! object 'genotype_upsetr' not found## Error:
## ! object 'genotype_upsetr' not found## Error:
## ! object 'genotype_upsetr' not foundNow let us try the location-specific comparisons
28.2 scn vs retina, p08
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("sr_p08_het", "sr_p08_ko")
table_names %in% names(location_sig)## [1] FALSE FALSElocation_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `xtfrm.data.frame()`:
## ! cannot xtfrm data frameslocation_upset_written <- write_upset_groups(location_upsetr, excel = "excel/sr_p08_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not foundI am reasonably certain that Rashmi would like a table of the genes shared among increased scn ko and het in the above plot along with the increased retina (e.g. the 269 and 103 gene sets).
28.3 scn vs retina, p15
## [1] FALSE FALSElocation_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `xtfrm.data.frame()`:
## ! cannot xtfrm data frameslocation_upset_written <- write_upset_groups(location_upsetr, excel = "excel/sr_p15_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'scn_retina_p15_upset_result' not found## Error:
## ! object 'location_upsetr' not found28.4 dlgn vs retina, p08
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("dr_p08_het", "dr_p08_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `1:ncol(data)`:
## ! argument of length 0location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/dr_p08_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found28.5 dlgn vs retina, p15
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("dr_p15_het", "dr_p15_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `xtfrm.data.frame()`:
## ! cannot xtfrm data frameslocation_upset_written <- write_upset_groups(location_upsetr, excel = "excel/dr_p15_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found28.6 dlgn vs scn, p08
table_names <- c("ds_p08_het", "ds_p08_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `xtfrm.data.frame()`:
## ! cannot xtfrm data frameslocation_upset_written <- write_upset_groups(location_upsetr, excel = "excel/ds_p08_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found28.7 dlgn vs scn, p15
table_names <- c("ds_p15_het", "ds_p15_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
name <- table_names[sig]
newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)## Error in `xtfrm.data.frame()`:
## ! cannot xtfrm data frameslocation_upset_written <- write_upset_groups(location_upsetr, excel = "excel/ds_p15_hetko_upset_groups.xlsx")## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found## Error:
## ! object 'location_upsetr' not found29 Shared and unique gene sets across x/wt
In this block I want to find the unique and shared genes between:
- scn p8 het/wt and retina p8 het/wt: hwp08scninc, hwp08retinc, hwp08scndec, hwp08retdec
- scn p15 het/wt and retina p15 het/wt: hwp15scninc, hwp15retinc, hwp15scndec, hwp15retdec
- #1 and #2 together: 8 catgories above
- scn p8 ko/wt and retina p8 ko/wt
- scn p15 ko/wt and retina p15 ko/wt
- #4 and #5 together
The comparisons of het/wt are found in the ‘inclusion_sig’ dataset; because they are providing our cutoffs for nonspecific binding.
29.1 Number 1 above: p08_het vs wt for scn and retina.
table_names <- c("p08_het_scn", "p08_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
29.2 Number 2 above: p15_het vs wt for scn and retina.
table_names <- c("p15_het_scn", "p15_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p15_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
29.3 Number 3 above: combination of #1 and #2
table_names <- c("p08_het_scn", "p08_het_retina", "p15_het_scn", "p15_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08p15_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
29.4 Number 4 above p08_ko vs wt for scn and retina.
table_names <- c("p08_ko_scn", "p08_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
29.5 Number 5 above p15_ko vs wt for scn and retina.
table_names <- c("p15_ko_scn", "p15_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## i Do you need to adjust the group aesthetic?inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p15_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
29.6 Number 6 above: Combining #4 and #5
table_names <- c("p08_ko_scn", "p08_ko_retina", "p15_ko_scn", "p15_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08p15_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
30 GSVA
msigdb <- "reference/msigdb_v2024.1.Mm.db"
if (file.exists(msigdb)) {
v3_h_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "mh",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_h_gsva
v3_h_gsva_sig <- get_sig_gsva_categories(
v3_h_gsva, excel = "excel/gsva_sig_hallmark_categories.xlsx")
v3_h_gsva_sig
v3_m1_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m1",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_m1_gsva
v3_m1_gsva_sig <- get_sig_gsva_categories(
v3_m1_gsva, excel = "excel/gsva_sig_positional_categories.xlsx")
v3_m1_gsva_sig
v3_m2_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m2",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_m2_gsva
v3_m2_gsva_sig <- get_sig_gsva_categories(
v3_m2_gsva, excel = "excel/gsva_sig_curated_categories.xlsx")
v3_m2_gsva_sig
v3_m3_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m3",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_m3_gsva
v3_m3_gsva_sig <- get_sig_gsva_categories(
v3_m3_gsva, excel = "excel/gsva_sig_regulatory_categories.xlsx")
v3_m3_gsva_sig
v3_m5_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m5",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_m5_gsva
v3_m5_gsva_sig <- get_sig_gsva_categories(
v3_m5_gsva, excel = "excel/gsva_sig_ontology_categories.xlsx")
v3_m5_gsva_sig
v3_m8_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m8",
signatures = msigdb, id_source = "fdata",
required_id = "mgi_symbol")
v3_m8_gsva
v3_m8_gsva_sig <- get_sig_gsva_categories(
v3_m8_gsva, excel = "excel/gsva_sig_celltype_categories.xlsx")
v3_m8_gsva_sig
}## Error:
## ! no such table: gene_set31 GSEA images
Up above I created a fairly large set of enrichment/GSEA analyses. Let us pull some of the most interesting results here and look at them.
Here are the specific queries from Rashmi:
- Genotype (het vs ko):
- P8 het and ko for Ret
- SCN (P8 het vs KO SCN
- P8 het vs KO Ret)
- P15 het and ko for Ret
- Location (somal vs axonal):
- SR_P08_KO
- SR_P08_Het
- SR_P15_KO
- SR_P15_Het
- Time(p8vs p15):
- t_het_Ret_ po8-p15
- t_KO_Ret_C po8-p15
- t_het_SCN_po8-p15
- t_KO_SCN_po8-p15
31.1 Genotype
Let us take a moment and see for which contrasts I acquired results:
I need to make a little summary for clusterprofiler too so that I can easily see how many hits there are for each contrast.
## Length Class Mode
## kh_p08_dlgn_up 21 gprofiler_result list
## kh_p15_dlgn_up 28 gprofiler_result list
## kh_p08_scn_up 21 gprofiler_result list
## kh_p08_scn_down 25 gprofiler_result list
## kh_p15_scn_down 26 gprofiler_result list## [1] "kh_p08_dlgn_up"
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 0 0 0 0 0 0 0 0 0 0
## [1] "kh_p15_dlgn_up"
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 63 32 0 118 1 0 21 6 3 0
## [1] "kh_p08_scn_up"
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 0 0 0 0 0 0 0 0 0 0
## [1] "kh_p08_scn_down"
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 58 21 0 0 0 0 3 0 113 0
## [1] "kh_p15_scn_down"
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 49 9 0 0 1 0 4 0 1 0## Error:
## ! object 'genotype_full_cp' not found## Error:
## ! object 'genotype_full_cp' not found31.1.1 p8 het/ko for retina:
This contrast, even before filtering away the high-wt genes, only has 8 genes in the set of up and down genes combined. As a result, my function which performs gProfiler/clusterProfiler skips it, and also skips the p15 het/ko for retina samples.
31.1.2 p8 het/ko for scn:
This has a bunch more genes: 51 up and 128 down. Unfortunately, gProfiler sees no significant over-representation in the up category of genes. The down category has
The up/down sets from clusterProfiler have enrich_go, gse_go, and enrich_objects to look at.
## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 0 0 0 0 0 0 0 0 0 0## BP CC CORUM HP KEGG MIRNA MF REAC TF WP
## 58 21 0 0 0 0 3 0 113 0## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## x Problematic argument:
## * by = "Count"
## i Did you misspell an argument name?
Perhaps I should just ask the question: for which categories did I get results back?
## Length Class Mode
## kh_p08_dlgn_up 21 gprofiler_result list
## kh_p15_dlgn_up 28 gprofiler_result list
## kh_p08_scn_up 21 gprofiler_result list
## kh_p08_scn_down 25 gprofiler_result list
## kh_p15_scn_down 26 gprofiler_result listkh_p08_dlgn_up: No significant gProfiler results. kh_p15_dlgn_up: Significant BP, HP, KEGG, MF, REAC, TF kh_p08_scn_up: No significant gProfiler results. kh_p08_scn_down: Significant BP, MiRNA, MF, TF kh_p15_scn_down: Significant BP, MF
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## x Problematic argument:
## * by = "Count"
## i Did you misspell an argument name?
31.2 Location
31.2.1 Scn vs retina ko, p08
plots <- plot_enrichresult(location_gp[["sr_p08_ko"]][["sr_p08_ko_up"]][["BP_enrich"]])
plots[["dot"]]## NULLplots <- plot_enrichresult(location_gp[["sr_p08_ko"]][["sr_p08_ko_down"]][["BP_enrich"]])
plots[["dot"]]## NULL31.2.2 scn vs retina, het, p08
Enriched groups: BP, KEGG, MF, TF, CC
## Length Class Mode
## 0 NULL NULLplots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_up"]][["BP_enrich"]])
plots[["dot"]]## NULLplots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_up"]][["CC_enrich"]])
plots[["dot"]]## NULLplots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_down"]][["BP_enrich"]])
plots[["dot"]]## NULL## Error in `if (nrow(gse) < topn) ...`:
## ! argument is of length zero31.3 Time
31.3.1 het retina
Ups: significant results for BP, MF, TF Downs: BP, MF, REAC, TF, WP
plots <- plot_enrichresult(time_gp[["t_het_retina"]][["t_het_retina_up"]][["BP_enrich"]])
plots[["dot"]]## NULLplots <- plot_enrichresult(time_gp[["t_het_retina"]][["t_het_retina_down"]][["BP_enrich"]])
plots[["dot"]]## NULL31.3.2 ko retina
Up: BP, MiRNA, MF Down: BP, MF, REAC, TF
plots <- plot_enrichresult(time_gp[["t_ko_retina"]][["t_ko_retina_up"]][["BP_enrich"]])
plots[["dot"]]## NULLplots <- plot_enrichresult(time_gp[["t_ko_retina"]][["t_ko_retina_down"]][["BP_enrich"]])
plots[["dot"]]## NULL31.3.3 het scn
Neither of the SCN gProfiler queries provided any results.
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message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename = savefile))
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Huijuan Xiao, et al. 2022. “A Male Germ-Cell-Specific Ribosome
Controls Male Fertility.” Nature 612 (7941): 725–31. https://doi.org/10.1038/s41586-022-05508-0.
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014.
“Moderated Estimation of Fold Change and Dispersion for
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---
title: "Analyses of the IPRGC re-processed samples."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
bibliography: atb.bib
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: zenburn
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
      rmdformats::readthedown:
        code_download: true
        code_folding: show
        df_print: paged
        fig_caption: true
        fig_height: 7
        fig_width: 7
        highlight: zenburn
        width: 300
        keep_md: false
        mode: selfcontained
        toc_float: true
        BiocStyle::html_document:
          code_download: true
          code_folding: show
          fig_caption: true
          fig_height: 7
          fig_width: 7
          highlight: zenburn
          keep_md: false
          mode: selfcontained
          toc_float: true
---

<style type="text/css">
body, td {
font-size: 16px;
}
code.r{
font-size: 16px;
}
pre {
font-size: 16px
}
body .main-container {
max-width: 1600px;
}
</style>

```{r options, include=FALSE}
library(hpgltools)
library(dplyr)
library(enrichplot)
library(ggplot2)
library(ggrepel)
library(gprofiler2)
tt <- try(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(
  progress = TRUE, verbose = TRUE, width = 90, echo = TRUE)
knitr::opts_chunk$set(
  error = TRUE, fig.width = 8, fig.height = 8, fig.retina = 2,
  out.width = "100%", dev = "png",
  dev.args = list(png = list(type = "cairo-png")))
old_options <- options(digits = 4, stringsAsFactors = FALSE, knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202408"
previous_file <- ""
ver <- format(Sys.Date(), "%Y%m%d")

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "iprgc_analyses_202408.Rmd"
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
## No one will ever read this I suspect, but there will _never_ be a word produced
## by an LLM in a document written by me.
```

# Set some parameters which will be used later

```{r}
## LogFC cutoff when working on the inclusion sets.
lfc_cutoff <- 0.1
## Adjusted p-value cutoff when working on the inclusion sets.
adjp_cutoff <- 0.1
## Increase the maximum allowed group size when working with clusterProfiler
## This should bring out some of the more general groups like 'cellbody'
max_groupsize <- 2000
## Allow groups higher up in the tree for clusterProfiler results.
go_level <- 2
## Allow 10 GO categories to be displayed when plotting.
go_categories <- 14
## MA plot point outlines
outline <- FALSE
## Speed up clusterProfiler by choosing the correct keytypes
orgdb_from <- "ENSEMBL"
default_fstring <- "~ 0 + condition"
```

# TODOs

* Check this for correctness.
* Reorganize it
* GSVA (@hanzelmannGSVAGeneSet2013a)
* Consider embedding some/many/all of the Excel outputs into the html
  output via xfun::embed_dir('excel/')

# Meeting with Theresa

Previous papers did not do an explicit subtraction, instead just
compared to WT and kept the genes which are > in delta/het vs. wt.
There are multiple ways to deal with this and that query has not yet
been defined.  Later, Theresa came to the conclusion that the
subtraction method is not appropriate.

# Introduction

In this document I hope to explore the freshly processed samples and
perform some comparisons to see that we have the expected similarities
and differences from the prior analysis performed by Theresa.

There is one way in which I expect any/all of these analyses to be
explicitly different: this should include the changes produced by
April's renaming of some samples.

My intention is to produce a sample sheet which includes one column
with non-umi-deduplicated results and one with deduplicated results.
With the exception of the previous point, I hope that the first will
be identical (or at least very close to identical) to Theresa's result
while the second I expect will be subtly different -- but I am hoping
subtly enough that it will not significantly change the interpretation
but be a little more precise.

Lets see!  I need therefore to make a change to my metadata gathering
function to include the umi deduplicated result.  I am thinking
therefore to create a separate specification for umi-barcoded samples
because looking through the logs for umi stuff when they are not used
will be too much of a pain...

## Small random reminder

I have a couple pictures of RPL22 to help me remember the experimental
design:

* The human ribosome with RPL22 in red: ![human_rpl22_red](human_ribosome_L22_red.png)
* The mouse ribosome with RPL22 in green at the center:
![mouse_rpl22](mmusculus_RPL22_green_center.png)

That second picture came from: (@liMaleGermcellspecificRibosome2022)

# A note about implementation

I would like to improve this document by comparing/contrasting the
methodologies performed by other groups and those performed by me in
it.  I never fully appreciated the suite of computational methods
applied by previous groups when examining TRAP data; I instead simply
followed Theresa's notebook without considering other possibilities.

I therefore spent a little time stepping through her thesis and
pulling out the relevant papers in the hopes of learning these various
methods.  I should therefore be able soon to compare/contrast the
various methods employed by other labs in addition to copying
Theresa's logic.

## The following block cannot work in the container

The following block assumes the full tree of preprocessed data with
the logs from the trimmer, mapping, umi deduplication, counting, etc.
As a result it cannot work in the container which has only the various
count tables.

As a result, I am including a copy of this sheet after running the
following block in my working tree.  I suppose for the moment you will
have to trust that it worked.  (for right now, when testing out this
container, I am just sending the R working directory to my tree for
this block, then moving it back.

I will need to manually edit one column though, the symlink column
from Theresa has a series of paths which do not work in the container.


```{r, eval=FALSE}
umi_spec <- make_rnaseq_spec(umi = TRUE)
iprgc_2022_meta <- gather_preprocessing_metadata("sample_sheets/20240606_only_umd_sequenced.xlsx",
                                                 spec = umi_spec, species = "mm39_112", verbose = FALSE,
                                                 basedir = "preprocessing/umd_sequenced")
colnames(iprgc_2022_meta[["new_meta"]])
head(iprgc_2022_meta[["new_meta"]])
```

```{r}
sample_sheet <- "sample_sheets/20240606_only_umd_sequenced_modified.xlsx"
msigdb <- "reference/msigdb_v2024.1.Mm.db"
msig_data <- NULL
make_transparent <- function() {
  ggplot2::theme(
    panel.background = element_rect(fill = 'transparent'),
    plot.background = element_rect(fill = 'transparent', color = NA),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    legend.background = element_rect(fill = 'transparent'),
    legend.box.background = element_rect(fill = 'transparent'))
}
```

I will figure out if I can leave mSigDB M2 in this image; if not, then
any analyses depending on those gene sets will fail a priori.

```{r}
m2_gsc <- try(load_gmt_signatures(signatures = msigdb,
                                  signature_category = "M2"), silent = TRUE)
## I do not think I have permission to load the msigdb in the container
## So, if this fails, just load it from GSVAData, oh wait no, GSVAdata is human.
if ("try-error" %in% class(m2_gsc)) {
  warning("Unable to load the M2 MsigDB data.")
}
```

From this point on, I am hoping/intending to pull liberally from
Theresa's notebook with a diversion to compare the three datasets:

* Pre-April renaming: E.g. Theresa's current dataset
* Post renaming: Unless I am mistaken, this should be very similar to
  the above.
* Post deduplication: Given what I saw from the extracted logs in the
  sample sheet, I expect this to be similar but not identical to the
  previous two.

Lets find out!   But first, annotations!

# Annotation data

I am pulling this from Theresa's anxontrapR_pipeline.Rmd, primarily
because it looks similar to the other documents, but was modified more
recently.  I will change it slightly, primarily because I grabbed a
new mmusculus assembly and therefore I will pull the mmusculus
annotations from a specific biomart
(@smedleyBioMartBiologicalQueries2009) archive that should match it.

A note from the future: multiple ensembl archive servers have been
taken offline since last I ran this.  Let us see if Feb. 2023 still
works.

## An important note!

In the recent past, ensembl queries have become inconsistent, failing
much more often than ever in the past.  I do not think this is the
fault of ensembl; but I think I need a fallback mechanism for
collecting annotation information.

In the case of ensembl, it should be trivial (but less fun) to use a
combination of the locally installed orgdb and txdb databases.

This does open a risk that the set of genes with annotations will be
different depending on when the container is run due to differences
between the orgdb/txdb instance and the Feb 2023 biomart.  I am not
sure there is much I can do about that except to bundle the set of
annotations I downloaded in the container -- since
load_biomart_annotations() does save a rda copy of its download.

ok, I did both.  If you, dear reader, wish to download your own
annotations, and ensembl is having troubles, the following should work
without a problem; in addition the rda annotations are in /data of the
container and should get loaded.

```{r}
tx_gene_map <- data.frame()
mm_annot <- try(load_biomart_annotations(species = "mmusculus", year = "2023", month = "02"))
if ("try-error" %in% class(mm_annot)) {
  fields <- c("ACCNUM", "ENSEMBL", "ENSEMBLTRANS", "ENTEZID", "GENENAME", "SYMBOL")
  orgdb_annot <- load_orgdb_annotations("org.Mm.eg.db", fields = fields)
  gene_info <- orgdb_annot[["genes"]]
  ## Note, there are a bunch of variants of the txdb package one might use.
  ## I do not think it matters a lot for our purposes, but I suspect that if we used
  ## a mismatched BSgenome and tried to pull CDS sequences, that might end badly.
  pkg <- "TxDb.Mmusculus.UCSC.mm10.knownGene"
  tx_annot <- load_txdb_annotations(pkg)
  transcripts <- tx_annot[["TX"]]
  transcripts[["tx"]] <- gsub(x = transcripts[["TXNAME"]],
                              pattern = "\\.\\d+$", replacement = "")
  mm_annot <- merge(gene_info, transcripts, by.x = "ensembltrans", by.y = "tx")
  rownames(mm_annot) <- make.names(mm_annot[["ensembl"]], unique = TRUE)
} else {
  mm_annot <- mm_annot[["annotation"]]
  mm_annot[["txid"]] <- paste0(mm_annot[["ensembl_transcript_id"]], ".", mm_annot[["version"]])
  rownames(mm_annot) <- make.names(mm_annot[["ensembl_gene_id"]], unique=TRUE)
  tx_gene_map <- mm_annot[, c("txid", "ensembl_gene_id")]
}
```

# Hisat2 summarizedExperiments

The primary difference between my block and Theresa's are:

1.  I am pulling the metadata directly from the
    gather_preprocessing_metadata() above.
2.  I am using the column 'symlink' which is just a copy of the
    existing 'file' column with a small change so I can load it from
    my directory without having to copy everything.
3.  I am using the ensembl genome release 39, version 112 and so
    pulled a somewhat newer copy of the annotation data.
4.  The original is named 'v1', followed by 'v2' and 'v3' for the
    other two treatments I performed.

## Color choices and reused parameters

Given that we are excluding a bunch of the older samples, the set of
colors I expect to find is different; so I will make explicit here the
various colors used to denote location/genotype/time/etc.

April turned me onto this website 'paletton.com' for this kind of
stuff and I will try and pick out palettes which basically match what
I am getting with the original colors.

```{r}
color_choices <- list(
  "all" = list(
    "p08_het_dlgn" = "#E7298A",
    "p15_het_dlgn" = "#E7298A",
    "p08_het_retina" = "#238B45",
    "p15_het_retina" = "#238B45",
    "p08_het_scn" = "#4292C6",
    "p15_het_scn" = "#4292C6",
    "p08_ko_dlgn" = "#C994C7",
    "p15_ko_dlgn" = "#C994C7",
    "p08_ko_retina" = "#74c476",
    "p15_ko_retina" = "#74c476",
    "p08_ko_scn" = "#9BCAE1",
    "p15_ko_scn" = "#9BCAE1",
    "p08_wt_dlgn" = "#980043",
    "p15_wt_dlgn" = "#980043",
    "p08_wt_retina" = "#004008",
    "p15_wt_retina" = "#004008",
    "p08_wt_scn" = "#08519C",
    "p15_wt_scn" = "#08519C",
    "p60_wt_dlgn" = "#333333",
    "p60_wt_retina" = "#222222",
    "p60_wt_scn" = "#111111"),
  "geno_loc" = list(
    "het_dlgn" = "#E7298A",
    "het_retina" = "#238B45",
    "het_scn" = "#4292C6",
    "ko_dlgn" = "#C994C7",
    "ko_retina" = "#74c476",
    "ko_scn" = "#9BCAE1",
    "wt_dlgn" = "#980043",
    "wt_retina" = "#004008",
    "wt_scn" = "#08519C"),
  "location" = list(
    "retina" = "#004008",
    "dlgn" = "#980043",
    "scn" = "#08519C"),
  "genotype" = list(
    "wt" = "#74c476",
    "het" = "#238B45",
    "ko" = "#006D2C"),
  "time" = list(
    "p08" = "#5E104B",
    "p15" = "#4E9231"))
label_column <- "mgi_symbol" ## Set the column used to extract gene symbols rather than ENSG.....
colors <- color_choices[["geno_loc"]]
time_colors <- list(
  "p08_het_dlgn" = "#E7298A",
  "p15_het_dlgn" = "#8a1852",
  "p08_het_retina" = "#238B45",
  "p15_het_retina" = "#155329",
  "p08_het_scn" = "#4292C6",
  "p15_het_scn" = "#275776",
  "p08_ko_dlgn" = "#C994C7",
  "p15_ko_dlgn" = "#785877",
  "p08_ko_retina" = "#74C476",
  "p15_ko_retina" = "#457546",
  "p08_ko_scn" = "#9BCAE1",
  "p15_ko_scn" = "#5d7987")
```

There is one noteworthy sample: iprgc_103, it was effectively replaced
when April renamed the samples and so exists in the v1 data, but not
v2/v3; they instead have the newly named samples which I called
iprgc_123 to iprgc_130.  As a result, I copied the annotations for
iprgc_123 to my column so that there is no discrepency in terms of
genotype/location/time.

## The original count tables

At the moment I have not included the original counts in this
container because we made some changes to the mapping strategy and
also found that a couple samples were mixed up in sequencing; as a
result I documented all of the changes in the sample sheets and
preprocessing documents and excluded the original files.

This is also why some columns in the sample sheet have suffixes like
'adh' and 'atb', those denote from whom the relevant metadata columns
came from.

```{r, eval=FALSE}
mm38_hisat_v1 <- create_se(sample_sheet,
                           gene_info = mm_annot,
                           file_column = "symlink") %>%
  set_conditions(fact = "geno_loc_atb") %>%
  set_batches(fact = "time_atb") %>%
  set_colors(color_choices[["geno_loc"]])
mm38_hisat_v1
```

## Recounted tables and the deduplicated result

In the following I make two more versions of the data, one remapped
with the changes to the sample identities, and one with deduplication
applied.

```{r}
mm38_hisat_v2 <- create_se(sample_sheet, gene_info = mm_annot,
                           file_column = "hisat_count_table") %>%
  set_conditions(fact = "geno_loc_atb") %>%
  set_batches(fact = "time_atb") %>%
  set_colors(color_choices[["geno_loc"]])
mm38_hisat_v2
mm38_hisat_v3 <- create_se(sample_sheet, gene_info = mm_annot,
                           file_column = "umi_dedup_output_count") %>%
  set_conditions(fact = "geno_loc_atb") %>%
  set_batches(fact = "time_atb") %>%
  set_colors(color_choices[["geno_loc"]])
mm38_hisat_v3

all_fact <- paste0(colData(mm38_hisat_v3)[["time_atb"]], "_",
                   colData(mm38_hisat_v3)[["geno_loc_atb"]])
colData(mm38_hisat_v3)[["time_geno_loc"]] <- all_fact
```

Note the end of the previous block, I created a factor out of the
combination of time, genotype, and location.  In a future invocation
of this notebook, I will change the pairwise comparisons to add each
of these three factors to the statistical model instead of this.  The
code to do that is not _quite_ ready yet.

# Non-zero Counts per Sample

Let's look at the number of non-zero genes for all samples versus the
coverage.

As above, this does not get run because I did not copy the count tables.

```{r eval=FALSE}
v1_nonzero <- plot_nonzero(mm38_hisat_v1)
v1_nonzero
```

But these do!

```{r}
plot_legend(mm38_hisat_v2)
v2_nonzero  <- plot_nonzero(mm38_hisat_v2, y_intercept = 0.65)
v2_nonzero
pp(file = "01diagnostic_images/nonzero_v2_unfiltered.pdf")
v2_nonzero[["plot"]]
plotted <- dev.off()

v3_nonzero  <- plot_nonzero(mm38_hisat_v3, y_intercept = 0.65)
v3_nonzero
pp(file = "01diagnostic_images/nonzero_v3_unfiltered.pdf")
v3_nonzero[["plot"]]
plotted <- dev.off()
```

Oh wow, I did not expect such a profound effect on the cpm values on
the more saturated libraries.  I guess in retrospect I should have?

Also note to self, we are not messing with p60.

## Exclude p60

```{r}
mm38_hisat_v2 <- subset_se(mm38_hisat_v2, subset = "time_atb!='p60'")
mm38_hisat_v3 <- subset_se(mm38_hisat_v3, subset = "time_atb!='p60'")
```

## Replot the nonzero gene plots

```{r}
v2_nonzero_filt <- plot_nonzero(mm38_hisat_v2, plot_labels = FALSE)
pp(file = "01diagnostic_images/nonzero_v2_filt.pdf")
v2_nonzero_filt[["plot"]]
plotted <- dev.off()

v3_nonzero_filt <- plot_nonzero(mm38_hisat_v3, plot_labels = FALSE)
pp(file = "01diagnostic_images/nonzero_v3_filt.pdf")
v3_nonzero_filt[["plot"]]
plotted <- dev.off()
```

Once again, I do not want to lose the previous code, so here is the v1 invocation

```{r, eval=FALSE}
mm38_hisat_v1 <- subset_se(mm38_hisat_v1, subset = "time_atb!='p60'")
```

# Quick PCA, then return to Theresa's document

```{r}
v2_norm <- normalize(mm38_hisat_v2, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
v2_norm_pca <- plot_pca(v2_norm)
v2_norm_pca
pp(file = "01diagnostic_images/v2_norm_pca.pdf")
v2_norm_pca[["plot"]]
plotted <- dev.off()

v3_norm <- normalize(mm38_hisat_v3, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
v3_norm_pca <- plot_pca(v3_norm)
v3_norm_pca
pp(file = "01diagnostic_images/v3_norm_pca.pdf")
v3_norm_pca[["plot"]]
plotted <- dev.off()
```

Ibid.

```{r, eval=FALSE}
v1_norm <- normalize(mm38_hisat_v1, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
plot_pca(v1_norm)
```

To my eyes it looks like we just have 1 weirdo p15 sample?
Deduplication had a minor but significant effect on the PCA.

With that in mind, let us look at Theresa's WORKING document and see
what we can recapitulate.

Theresa's document: The TRAP protocol has some variability which is
introduced at different stpdf including homogenization, antibody
labeling, pulldown efficiency/specificity, sample handling during
cleanup stpdf, and library prep/sequencing. We know from Rashmi's QC
that there is variability at the level of pulldown efficiency (amount
of RNA isolated). She is doing a good job of keeping track of this for
all her samples and we have validated her P8 results (attached
supplementary figure 3D). We consistently see clear differences
between control and cre samples for the retina, which makes sense
because the cell bodies are in the retina. The target tissue
differences are smaller, which also makes sense for axon-TRAP. We
think that some of her P15 samples are not good based on low amounts
of isolated RNA from cre(+) retina samples. We plan to drop these
samples and not perform additional isolations at this time
point. Based on this (and the general lack of large developmental
effects), we were planning to focus on presenting the P8 data only in
the paper. Interested to hear your thoughts in this...

My notes: Theresa's first operations in this notebook were to:

1.  Set location as condition, genotype as batch.
2.  Perform PCA before/after sva.

```{r}
v3_loc_geno <- set_conditions(mm38_hisat_v3, fact = "location_atb",
                              colors = color_choices[["location"]]) %>%
  set_batches(fact = "genotype_atb")
```

## The associated PCA

At different times, it appears to me that Theresa has preferred
slightly different normalization methods, primarily a mix of TMM and
quantile.

Thus I will use different suffix letters to denote various
normalizations employed, and if they turn out the same I will pick one arbitrarily.

```{r}
loc_geno_nq <- normalize(v3_loc_geno, transform = "log2", convert = "cpm",
                         filter = TRUE, norm = "quant")
location_genotype_pca <- plot_pca(loc_geno_nq)
pp(file = "01diagnostic_images/location_genotype_norm_pca.pdf")
location_genotype_pca[["plot"]]
plotted <- dev.off()
location_genotype_pca
## ok, I have two weirdo samples which look very much like they are actually dlgn.
## These are sample IDs iprgc_66 and iprgc_130

loc_geno_nt <- normalize(v3_loc_geno, transform = "log2", convert = "cpm",
                         filter = TRUE, norm = "tmm")
location_genotype_tmm_pca <- plot_pca(loc_geno_nt)
pp(file = "01diagnostic_images/location_genotype_tmm_pca.pdf")
location_genotype_tmm_pca[["plot"]]
plotted <- dev.off()
location_genotype_tmm_pca
```

A random thought about these PCA plots, it might be worth while to add
a panel below the legend with the sample numbers per condition/batch.

Of course, the same information is provided in a more fun fashion via
my silly sankey function:

```{r}
sample_sankey <- plot_meta_sankey(v3_loc_geno, color_choices = color_choices,
                                  factors = c("genotype_atb", "location_atb", "time_atb"))
pp(file = "01diagnostic_images/design_sankey.pdf")
sample_sankey[["ggplot"]]
plotted <- dev.off()
sample_sankey
```

# A Short conversation with Rashmi

Rashmi came by and we discussed the samples a little.  She suggested
that is likely that we will need to exclude the 202205 samples, these
may be identified by a few ways, most easily I think via the
'project_ah' column, they are the 021_1 samples.

My sense was that she concurred with my interpretation of the umi
deduplication, so I will continue using the deduplicated results
exclusively, at least for now.

# Melanopsin Sanity Check

One of Theresa's first checks was wisely for melanopsin.  Let us
repeat a version of this:

An important note: Indrajeet Patil removed the groupedstats and its
associated plotting library from CRAN/github/etc.  I am not certain
what happened, but that necessitates a change in how I plot this.

```{r}
opn4_exprs <- data.frame(combined = colData(loc_geno_nt)[["geno_loc_atb"]],
                         location = colData(loc_geno_nt)[["location_atb"]],
                         genotype = colData(loc_geno_nt)[["genotype_atb"]],
                         opn = assay(loc_geno_nt)["ENSMUSG00000021799", ])

## groupedstats::grouped_summary(opn4_exprs, location, opn)
## opn4_location <- ggbetweenstats(data = opn4_exprs, x = location, y = opn)
## pp(file = "images/ggbetween_location.pdf")
## opn4_location
## plotted <- dev.off()
## opn4_location

## opn4_genotype <- ggbetweenstats(data = opn4_exprs, x = genotype, y = opn)
## pp(file = "images/ggbetween_location.pdf")
## opn4_genotype
## plotted <- dev.off()
## opn4_genotype

## opn4_combined <- ggbetweenstats(data = opn4_exprs, x = combined, y = opn)
## pp(file = "images/ggbetween_combined.pdf")
## opn4_combined
## plotted <- dev.off()
## opn4_combined
```

ok, so I plotted the question a bit differently, but got the same
answer.

Here is the text of Theresa's notebook following this analysis:

"Ugh oh, looks like there is at least one retina KO sample that has
some melanopsin expression in it. Turns out ipRGC_07 is a bad egg
which is supposed to be a KO but has melanopsin expression. It’s
friends which were pooled from the same mice are iprgc_06 and
iprgc_08, so we need to exclude all these samples."

I am also seeing some knockout expression with some caveats: I do not
have the affected samples in my dataset (iprgc_07) and the levels I am
seeing are quite low -- I will look in IGV to double check, but I
strongly suspect that these are some piddly reads near the UTRs.

Onward!

# PCA plots

## PCA of all genes by location

Theresa's first pca was of log2 cpm values.  I might add quantile/tmm
to this?

```{r}
v3_location <- set_conditions(mm38_hisat_v3, fact = "location_atb") %>%
  set_batches(fact = "genotype_atb") %>%
  set_colors(color_choices[["location"]])
v3_location_norm <- normalize(v3_location, filter = TRUE, norm = "quant",
                                   transform = "log2", convert = "cpm")
v3_location_pca <- plot_pca(v3_location_norm)
pp(file = "01diagnostic_images/v3_location_norm_pca.pdf")
v3_location_pca
dev.off()
v3_location_pca
```

Once again we see that samples iprgc_66 and iprgc_130 are likely
actually DLGN and not SCN.  I am therefore going to add a column to
the sample sheet noting this, and remove them from the expressionset.

I will thus replot the data after removing those two.  If we want to
see what it looks like with the re-attributed locations, we can do so.

Theresa has a nice change to the PCA plotter in which she sets the
alpha channel as an additional visual queue for a metadata factor...

```{r}
mm38_hisat_v3 <- subset_se(mm38_hisat_v3, subset="sampleid!='iprgc_130'") %>%
  subset_se(subset="sampleid!='iprgc_66'")
v3_location <- set_conditions(mm38_hisat_v3, fact = "location_atb") %>%
  set_batches(fact = "genotype_atb") %>%
  set_colors(color_choices[["location"]])

v3_location_norm <- normalize(v3_location, filter = TRUE, norm = "quant",
                                   transform = "log2", convert = "cpm")
filtered_location_pca <- plot_pca(v3_location_norm)
pp(file = "02filtered_images/filtered_location_pca.pdf")
filtered_location_pca[["plot"]]
plotted <- dev.off()
filtered_location_pca

removed_sankey <- plot_meta_sankey(v3_location, color_choices = color_choices,
                                   factors = c("genotype_atb", "location_atb", "time_atb"))
pp(file = "02filtered_images/filtered_sankey.pdf")
removed_sankey[["ggplot"]]
plotted <- dev.off()
removed_sankey
```

Here is Theresa's text, recall once again that I do not have some of
these older samples (iprgc_62):

PC1 vs PC2 identifies retina vs axon is still the main component of
variation. We do see though that in the PC2 direction, we see with the
new samples added, we don’t see separation based on axonal targets
(dLGN vs SCN). In the PC1 vs PC3 plot, we see that it’s PC3 where we
start to see variation correlated with axonal compartment. Let’s look
at PC1 vs PC2 colored by batch (when they were processed/sequenced) to
see if that is what is contributing so much variation in PC2.

Side note: ipRGC 62 seems like an odd ball. This seems to me like it
should have been a dLGN P08 sample. Is there any possibility this got
mislabeled early on? I went back and double checked to see if all my
processing is correct and it indeed was labeled an SCN P15 from the
time I got the samples, and it is indeed.

# DE

I now switched to Theresa's document 'WORKING_axonTRAP...' and will
start pulling sections from it.  I am reasonably certain I have
reasonably similar sample distributions, so I presume I can invoke
similar/identical calls for DESeq and friends.

## p8 retinas

In the block immediately before the DE analyses, Theresa created a
subset expressionset of only p08 retinas.  Thus this initial DE I
assume will be used to subtract for the SCN/DLGN analyses that follow.
(I guess I could read ahead and find out, but no! I want to be a
blank slate)

Theresa's primary workflow makes heavy use of DESeq2
(@loveModeratedEstimationFold2014) and sva
(@leekSVAPackageRemoving2012).  In some(most?) of Theresa's
invocations of the all_pairwise() function, she excludes the other
methods that it performs.  In this workbook, I left those methods on,
thus we can evaluate the relative performance DESeq2 vs. some (all? I
may have disabled EBSeq/dream because they were taking too long)
of the following:

* limma: (@ritchieLimmaPowersDifferential2015) (among other
  references) originally written for microarrays.
* EdgeR: (@robinsonEdgeRBioconductorPackage2010), which shares many
  assumptions with DESeq2.
* EBSeq: (@lengEBSeqEmpiricalBayes2013), because I have a soft spot
  for any Bayesian method.
* Noiseq: (@tarazonaNOIseqRNAseqDifferential2011), which seeks to
  directly model variance in an RNASeq dataset and use that to improve
  the sensitivity of the result, much like:
* Dream: (@hoffmanDreamPowerfulDifferential2020), written by the same
  authors (and uses very similar logic) as one of my favorite tools,
  variancePartition(@hoffmanVariancePartitionInterpretingDrivers2016).

```{r}
mm38_p8_retina <- subset_se(mm38_hisat_v3, subset = "time_atb=='p08' & location_atb=='retina'")
mm_normal_p8_ret_de <- all_pairwise(mm38_p8_retina, model_svs = "svaseq",
                                    model_fstring = "~ 0 + condition", filter = TRUE)
mm_normal_p8_ret_de
```

There seems to be a discrepency with previous iterations of this.
Let us simplify to just doing deseq and find what is causing it.
In my previous iteration, I got 3632 genes in the unique(c()) or het+ko.

```{r}
deseq_only <- deseq_pairwise(mm38_p8_retina, model_svs = "svaseq",
                             model_fstring = default_fstring, filter = TRUE)
deseq_hetkeeper_genes <- deseq_only$all_tables$wt_retina_vs_het_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_kokeeper_genes <- deseq_only$all_tables$wt_retina_vs_ko_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_keepergenes <- unique(c(rownames(deseq_hetkeeper_genes),
                        rownames(deseq_kokeeper_genes)))
length(deseq_keepergenes)

deseq_pair_hetkeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_het_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_pair_kokeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_ko_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
deseq_pair_keepergenes <- unique(c(rownames(deseq_pair_hetkeeper_genes),
                        rownames(deseq_pair_kokeeper_genes)))
length(deseq_pair_keepergenes)
```

The following invocation performed by Theresa filters the wt/het
comparison for only those genes which increased by at least 0.25 logFC
with a significant adjusted p-value.  I assume that this is to use the
wt samples as a translational control for the ket/ko comparisons; I am
therefore thinking that for my purposes, I will therefore separate the
contrasts from all_pairwise do this in a stepwise fashion...

The block of code immediately following Theresa's all_pairwise()
invocation is a little confusing for me and warrants some explanation
by me to me in the hopes that I do not misunderstand what is happening
and the goals therein.

I think I can safely assume that the goal here is to pull out the IDs
which increased in het with respect to wild type; even if by a small
margin, as long as it is statistically significant vis a vis the
adjusted p-value.

I am going to perform what I think is the same thing in a slightly
different fashion so that I can share a copy of the results with
whomever is interested.  I will also repeat Theresa's invocation and
prove to myself that I understood and got the same answer.

```{r}
wt_het_keeper <- list("het_vs_wt" = c("het_retina", "wt_retina"))
het_wt_table <- combine_de_tables(
  mm_normal_p8_ret_de, keepers = wt_het_keeper, label_column = label_column,
  excel = "03theresa_comparison_excel/het_retina_control.xlsx")
wanted_sig <- extract_significant_genes(
  het_wt_table, lfc = 0.25, according_to = "deseq",
  excel = "03theresa_comparison_excel/het_retina_control-sig.xlsx")
wanted_het_increased <- wanted_sig[["deseq"]][["ups"]][["het_vs_wt"]]
increased_het_genes <- rownames(wanted_het_increased)
```

Here are Theresa's next lines:

```{r}
mm_de_normal_p8_ret <- mm_normal_p8_ret_de
hetkeeper_genes <- mm_de_normal_p8_ret$deseq$all_tables$wt_retina_vs_het_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
kokeeper_genes <- mm_de_normal_p8_ret$deseq$all_tables$wt_retina_vs_ko_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(hetkeeper_genes),
                        rownames(kokeeper_genes)))
## We know a priori that Opn4 is ENSMUSG00000021799
## I do not expect to see it in this set, it should be higher in wt
## retina vs ko retina by a significant margin.
"ENSMUSG00000021799" %in% keepergenes
## Oooohhh but it _is_ higher in het vs. wt, as we saw in
## the violin plot earlier.
```

I think Rashmi made a compelling point which illustrates why we likely
should expect the expression of Opn4 to significantly higher in the
heterozygotes vs wild-type:

1.  Recall that the assay is using the immunopurification to extract
    the RNAs.
2.  The wt samples do not have the cre recombinase and therefore no HA
    and therefore everything we observe is due to non-specific
    binding.
3.  The set of genes observed due to non-specific binding is different
    than het/ko (presumably a larger number of relatively small
    values), therefore the divisor performed in the cpm is likely
    relativly large resulting in normalized values getting shifted
    down to some degree.
4.  On the other hand, the set of genes observed in het/ko are more
    likely to be only the specific binders and therefore smaller (I
    can test this) resulting in a smaller divisor and slight shifting
    up in the cpm values.

This makes me wonder if any normalization methods exist which do
something like multiply the values by some value related to the
proportion of observed genes; and/or if this is a good/bad/indifferent
idea.

Also, just a note for me to remember: RPL22, not RPS22, for some
reason I keep thinking the small subunit.

## Prove I understood

```{r}
hetkeeper_genes <- mm_normal_p8_ret_de$deseq$all_tables$wt_retina_vs_het_retina %>%
  filter(logFC <= -0.25 & adj.P.Val <= 0.05)
testthat::expect_true(nrow(hetkeeper_genes) == length(increased_het_genes))
taa_keepers <- sort(rownames(hetkeeper_genes))
atb_keepers <- sort(increased_het_genes)
testthat::expect_equal(taa_keepers, atb_keepers)
```

Yay! I can read!  Now let us repeat for the KO vs wt

```{r}
wt_ko_keeper <- list("ko_vs_wt" = c("ko_retina", "wt_retina"))
ko_wt_table <- combine_de_tables(
  mm_normal_p8_ret_de, keepers = wt_ko_keeper, label_column = label_column,
  excel = "03theresa_comparison_excel/ko_retina_control.xlsx")
wanted_sig <- extract_significant_genes(
  ko_wt_table, lfc = 0.25, according_to = "deseq",
  excel = "03theresa_comparison_excel/ko_retina_control-sig.xlsx")

wanted_ko_increased <- wanted_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
increased_ko_genes <- rownames(wanted_ko_increased)
```

The next thing performed in Theresa's document is a unique(concatenation of
these two gene groups), thus sucking up every gene which was
significantly higher in either the knockout _or_ heterzyous samples
with respect to wild-type.

This was followed by a couple of merge operations of a little bit of
the annotation data; I am not sure I understand the goal yet...

Here is her code. I copied the annotation 'mgi_symbol' column to
'external_gene_name' so that I need not change any of her code.  I am
assuming this is the appropriate column of interest, I do not know
this for certain, but it seems quite likely.

While I am at it, here is the set_sig_limma() function from Theresa's helpers.R

```{r}
set_sig_limma <- function(limma_tbl, factors = NULL) {
  if (is.null(factors)) {
    #set significance for plotting colors
    limma_tbl$Significance <- NA
    limma_tbl[abs(limma_tbl$logFC) < 1 | limma_tbl$adj.P.Val > .05, "Significance"] <- "Not \nEnriched"
    limma_tbl[limma_tbl$logFC >= 1  & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- "Disease \nUpregulated"
    limma_tbl[limma_tbl$logFC <= -1  & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- "Disease \nDownregulated"
    limma_tbl$Significance <- factor(limma_tbl$Significance, levels = c("Upregulated", "Downregulated",  "Not \nEnriched"))
  } else {
    limma_tbl$Significance <- NA
    limma_tbl[abs(limma_tbl$logFC) < 1 | limma_tbl$adj.P.Val > .05, "Significance"] <- "Not \nEnriched"
    if(nrow(limma_tbl[limma_tbl$logFC >= 1  & limma_tbl$adj.P.Val <= .05, ]) != 0) {
      limma_tbl[limma_tbl$logFC >= 1  & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- factors[1]
    }
    if (nrow(limma_tbl[limma_tbl$logFC <= -1  & limma_tbl$adj.P.Val <= .05, ]) != 0) {
      limma_tbl[limma_tbl$logFC <= -1  & limma_tbl$adj.P.Val <= .05, ][["Significance"]] <- factors[2]
    }
    limma_tbl$Significance <- factor(limma_tbl$Significance, levels = c(factors,  "Not \nEnriched"))
  }
  return(limma_tbl)
}
```

### Combining het/wt and ko/wt

```{r}
mm_annot[["external_gene_name"]] <- mm_annot[["mgi_symbol"]]
keepergenes <- unique(c(rownames(hetkeeper_genes), rownames(kokeeper_genes)))
length(keepergenes)
annots_to_merge <- mm_annot %>%
  select(ensembl_gene_id, external_gene_name) %>%
  filter(ensembl_gene_id %in%
           rownames(mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina)) %>%
  distinct()
mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina <- merge(
  mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina, annots_to_merge,
  by.x = 0, by.y = "ensembl_gene_id", all.x = TRUE)
df <- mm_de_normal_p8_ret$deseq$all_tables$ko_retina_vs_het_retina %>%
  dplyr::mutate(logFC = -logFC) %>%
  set_sig_limma(factors = c("Het Enriched", "KO Enriched"))
```

My version of the above task makes use of the excludes option of
combine_de_tabes.  Given the set of unique gene IDs increased in the
het/ko, I can ask to exlude anything not in that set.  I could also
have more parsimoniously directly excluded any gene ID increased in
the wt samples.  But, Theresa already provided the code to do the
former, so it will be less typing/opportunity for silly mistakes to
just do that.

```{r}
both_increased_genes <- unique(c(increased_het_genes, increased_ko_genes))
## arbitrairly grab all genes from one of my data structures.
all_genes <- rownames(exprs(mm38_hisat_v3))
exclude_idx <- all_genes %in% both_increased_genes
summary(exclude_idx)
```

My April 2025 version of this shows 21,793 and 3,632 genes in this
set.  Is that still true?  (As of 20260311, it is!)

```{r}
exclude_increased_genes <- all_genes[exclude_idx]
retina_keepers <- list(
  "het_vs_wt" = c("het_retina", "wt_retina"),
  "ko_vs_wt" = c("ko_retina", "wt_retina"),
  "ko_vs_het" = c("ko_retina", "het_retina"))
## A reminder to myself: there is also a parameter 'wanted_genes'
## which does effectively the same thing as excludes in this context;
## excludes was originally written to allow flexible, keyword-based
## exclusion.
p8_retina_tables <- combine_de_tables(
  mm_normal_p8_ret_de, keepers = retina_keepers,
  wanted_genes = both_increased_genes, label_column = label_column,
  excel = glue("03theresa_comparison_excel/p8_retina_kept_genes_increased_in_wt_tables-v{ver}.xlsx"))

p8_retina_sig <- extract_significant_genes(
  p8_retina_tables, according_to = "deseq",
  excel = glue("03theresa_comparison_excel/p8_retina_kept_genes_increased_in_wt_sig-v{ver}.xlsx"))

opposite_p8_retina_tables <- combine_de_tables(
  mm_normal_p8_ret_de, keepers = retina_keepers,
  excludes = both_increased_genes, label_column = label_column,
  excel = glue("03theresa_comparison_excel/p8_retina_removed_genes_increased_in_wt_tables-v{ver}.xlsx"))

opposite_p8_retina_sig <- extract_significant_genes(
  p8_retina_tables, according_to = "deseq",
  excel = glue("03theresa_comparison_excel/p8_retina_removed_genes_increased_in_wt_sig-v{ver}.xlsx"))
```

# Filtering out non-specific genes and examining the results

The following is a copy/paste from Theresa containing the remaining
tasks she performed and will provide the template for implementation
of the final tasks.

This picks up with the lines from her notebook immediately following
the invocation of 'set_sig_limma(factors = c("Het Enriched" ...'.

For all of the remaining blocks I will copy in her code, turn off its
evaluation, run the blocks manually, compare them to her notebook
output, then enable each block as I ensure I understand it.

I will likely therefore introduce some small formatting changes and
add some additional GSEA/enrichment tasks once the non-specific
filtering is complete.

```{r}
df <- df %>%
  filter(Row.names %in% keepergenes)
labels_ups <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
  arrange(logFC) %>%
  head(n = 9)
labels_downs <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
  arrange(-logFC) %>%
  head(n = 11)
labels <- rbind(labels_ups, labels_downs)
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
  geom_point(aes(colour = Significance), size = 4) +
  geom_vline(xintercept = c(-1, 1)) +
  geom_hline(yintercept = -log10(0.05)) +
  theme_classic(base_size = 20) +
  xlab("log2(FC)") +
  ylab("-log10(p-value)") +
  theme(legend.position = "right") +
  scale_color_manual(values = c("#F8766D", "#00BFC4", "Grey")) +
  geom_label_repel(
    data = filter(df,
                  ## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
                  external_gene_name %in% labels$external_gene_name),
    ## nudge_x = -0.5,
    nudge_y = 3, max.overlaps = 15) +
  xlim(c(-3, 6))

pp(file = "03theresa_comparison_images/p08_retina_DE_1312024.pdf")
DEplot
plotted <- dev.off()
DEplot
write_xlsx(df, excel = "excel/retinahet_vs_retinako_WTfiltered.xlsx")
```

## How many ups/downs

```{r}
ko_enriched <- df %>%
  filter(Significance == "KO Enriched")
nrow(ko_enriched)
het_enriched <-  df %>%
  filter(Significance == "Het Enriched")
nrow(het_enriched)
```

## category enrichment/GSEA

```{r}
regulated_genes <- res_tbl %>%
  filter(adj.P.Val <= 0.05) %>%
  arrange(logFC) %>%
  select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
  filter(abs(logFC) >= 1)
## gsea_result_ko <- gost(query = ko_genes$external_gene_name,
##                        organism = "mmusculus",
##                        evcodes = TRUE,
##                        ordered_query = TRUE)
gsea_result_het <- gost(query = het_enriched$external_gene_name,
                        organism = "mmusculus",
                        evcodes = TRUE,
                        ordered_query = TRUE)
##gsea_result_alldysregulated <- gost(query = alldysregulated_genes$external_gene_name,
##                                    organism = "mmusculus",
##                                    evcodes = TRUE,
##                                    ordered_query = TRUE)
```

I have a function in my package which seeks to make gProfiler queries
a bit more complete and easy.  Let us see how similar the result is...

```{r, eval=FALSE}
rownames(alldysregulated_genes) <- alldysregulated_genes[["Row.names"]]
alldysregulated_genes[["Row.names"]] <- NULL

het_gp <- simple_gprofiler(rownames(alldysregulated_genes),
                           species = "mmusculus",
                           excel = glue("excel/het_gprofiler-v{ver}.xlsx"))
het_gp
enrichplot::dotplot(het_gp[["BP_enrich"]])
gp_pair <- enrichplot::pairwise_termsim(het_gp[["BP_enrich"]])
enrichplot::emapplot(gp_pair)
enrichplot::ssplot(gp_pair)
enrichplot::treeplot(gp_pair)
upsetplot(het_gp[["BP_enrich"]])

enrichplot::dotplot(het_gp[["REAC_enrich"]])
gp_pair <- enrichplot::pairwise_termsim(het_gp[["REAC_enrich"]])
enrichplot::emapplot(gp_pair)
enrichplot::ssplot(gp_pair)
enrichplot::treeplot(gp_pair)
upsetplot(het_gp[["REAC_enrich"]])
```

I make a somewhat arbitrary distinction between the concepts of
over-enrichment analyses and GSEA: the former (as performed by
gprofiler) (@raudvereProfilerWebServer2019) seeks to find groups of
genes overrepresented in GO/reactome/etc.  These groups of genes are
taken exclusively from the top-n/bottom-n genes with respect to
fold-change between conditions of interest; in this case most
different than wt in the p08 retina ko or het samples.

With that in mind, I can invoke a similar function using the full
table of DE results to get what I call the GSEA result using
clusterProfiler (@yuIntroductionBiomedicalKnowledge).  In the
following block I will use the 'all_cprofiler' function on the data
structures named 'p8_retina_tables' and 'opposite_p8_retina_tables' in
order to get these GSEA results for each contrast performed (het/wt,
ko/wt, het/ko).  I will follow that up with 'all_gprofiler' which does
the same, but uses gProfiler's enrichment analyses (it will therefore
include what we just looked at).

```{r}
p08_retina_all_cp <- all_cprofiler(
  p8_retina_sig, p8_retina_tables, orgdb = "org.Mm.eg.db", orgdb_from = orgdb_from,
  excel = "03theresa_comparison_excel/cprofiler_p08_retina.xlsx")

enrichplot::dotplot(p08_retina_all_cp[["ko_vs_het_up"]][["enrich_objects"]][["MF_all"]])
p08_topn_gsea <- plot_topn_gsea(p08_retina_all_cp)

pp(file = "03theresa_comparison_images/gsea_p08_retina_ko_vs_het_top_hit.pdf")
p08_topn_gsea[["GO_ko_vs_het_up"]][[1]]
plotted <- dev.off()

p08_topn_gsea[["GO_ko_vs_het_up"]][[1]]
p08_topn_gsea[["GO_ko_vs_het_up"]][[2]]
p08_topn_gsea[["GO_ko_vs_het_up"]][[3]]
p08_topn_gsea[["GO_ko_vs_het_up"]][[4]]
p08_topn_gsea[["GO_ko_vs_het_up"]][[5]]

pp(file = "03theresa_comparison_images/gsea_p08_retina_het_vs_wt_top_hit.pdf")
p08_topn_gsea[["GO_het_vs_wt_up"]][[1]]
plotted <- dev.off()
```

```{r}
#gsea_ko <-  gsea_result_ko[["result"]] %>%
#    select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
#    arrange(desc(recall)) %>%
#    head(n = 10)
#  gsea_plots_ko <- ggplot(gsea_ko, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
#  geom_bar(stat = "identity")+
#  scale_fill_continuous(low = "blue", high = "red") +
#  theme_bw()+
#  ylab("") +
#  xlab("GSEA Score")
gsea_het <-  gsea_result_het[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 10)
gsea_plots_het <- ggplot(gsea_het, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over Representation Score")
pp(file = "03theresa_comparison_images/GSEA_p08_axontrap_retinahet_upregulated_vs_retinako.pdf")
gsea_plots_het
plotted <- dev.off()
gsea_plots_het
```

```{r, eval=FALSE}
gsea_all <-  gsea_result_alldysregulated[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 10)
gsea_plots_all <- ggplot(gsea_all, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over Representation Score")

pp(file = "images/GSEA_p08_retina_axontrap_alldysregulatedgenes.pdf")
gsea_plots_all
plotted <- dev.off()
```

# SCN Het vs KO

It is only now that I realized we are splitting the data by location
for each set of comparisons.  I think that, left to my own devices, I
would prefer to keep the input data structure intact, perform the
somewhat larger number of contrasts, and then split up the results.
Ideally this will slightly improve the fidelity of the results
returned by DESeq2 and friends.  But, I will run the state of
Theresa's notebook with as few changes as possible first, then add
this.

## PCA

I am going to skip this PCA plot for a couple of reasons: I already
did a superset of it, and the subset Theresa performed is not valid
given the set of samples included in my sample sheet, and figuring out
the actually corresponding subset will take me forever...  In
addition, I want to use my mm38_hisat_v3 for everything...

```{r, eval=FALSE}
mm38_subset <- subset_se(
  mm38_hisat,
  subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08' & location == 'scn' | sampleid == 'iprgc_03'")
mm38_norm <- normalize(mm38_subset, filter = TRUE, convert = "cpm",
                            transform = "log2", batch = "svaseq")
mm38_norm <- set_batches(mm38_norm, fact = "location")
mm38_norm <- set_conditions(mm38_norm, fact = "genotype")
pca_norm <- plot_pca(mm38_norm, max_overlaps = 70)
pca_norm$plot
```

Instead I will simplify the subset and see what happens...

```{r}
scn_samples <- subset_se(mm38_hisat_v3,
                           subset = "location_atb == 'scn'") %>%
  set_batches(fact = "location_atb") %>%
  set_conditions(fact = "genotype_atb", colors = color_choices[["genotype"]])
scn_norm <- normalize(scn_samples, filter = TRUE, convert = "cpm",
                           transform = "log2", batch = "svaseq")
scn_norm_pca <- plot_pca(scn_norm)
scn_norm_pca
```

# Library sizes post-deduplication

Theresa's next operation was to perform libsize/nonzero plots.  I
already did the pre/post deduplication nonzero, here is the analagous
libsize.

v2 is pre-deduplication and v3 is post.

```{r}
plot_libsize(mm38_hisat_v2)
post_filter_nonzero <- plot_libsize(mm38_hisat_v3, text = FALSE)
pp(file = "01diagnostic_images/post_all_filteres_nonzero.pdf")
post_filter_nonzero[["plot"]]
plotted <- dev.off()
post_filter_nonzero
```

I am a bit concerned about some of these library sizes
post-deduplication.

Let us look at the relationship between reads and duplication, which I
assume will be relatively linear.

```{r}
test <- colData(mm38_hisat_v3)[, c("hisat_genome_single_all", "umi_dedup_pct_reads")]
test_plot <- plot_linear_scatter(test, loess = TRUE)
test_plot[["scatter"]]
test_plot[["scatter"]]
pp(file = "01diagnostic_images/deduplication_vs_hisat_scatter.pdf")
test_plot[["scatter"]]
dev.off()
```

Theresa also produced a density/sample plot, that might prove quite
useful for these due to their significantly larger variance across
samples (due to deduplication).

```{r}
pp(file = "01diagnostic_images/sample_density.pdf")
mm38_density <- plot_density(loc_geno_nt)
mm38_density[["plot"]] +
  theme(legend.position = "none")
plot_boxplot(loc_geno_nt)
dev.off()
mm38_density[["plot"]] +
  theme(legend.position = "none")
box <- plot_boxplot(loc_geno_nt)
pp(file = "01diagnostic_images/sample_boxplot.pdf")
box
dev.off()
box
```

There is some difference across sample densities, but it is not too
crazytown.

# Diverging a little

At this point in the document I read ahead a bit and came to the
conclusion that it repeats the above logic of taking the union of wt
comparisons to remove genes from the appropriate het/ko or p15/p08 or
location comparisons.  This seems quite reasonable to me, but I would
prefer to not separate all the data, so I will attempt to duplicate
and slightly streamline this logic on the full dataset.  Thus I am
going to skip down to the end and attempt to implement this.

## DE

Note: The following few blocks are all copy/pasted directly from
Theresa's notebook and are not evaluated because they are performed
almost identically later but with slightly different logic/orders.

```{r, eval=FALSE}
mm_de_normal_p8_scn <- all_pairwise(mm38_subset, model_batch = "svaseq",
                                    parallel = FALSE, do_ebseq = FALSE, do_basic = FALSE,
                                    do_dream = FALSE, do_noiseq = FALSE, do_edger = FALSE,
                                    filter = TRUE)
annots_to_merge <- mm_annot %>%
  select(ensembl_gene_id, external_gene_name) %>%
  filter(ensembl_gene_id %in% rownames(mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn)) %>%
  distinct()
mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn <- merge(
  mm_de_normal_p8_scn$deseq$all_tables$ko_scn_vs_het_scn,
  annots_to_merge, by.x = 0, by.y = "ensembl_gene_id", all.x = TRUE)
```

```{r, eval=FALSE}
hetkeeper_genes <- mm_de_normal_p8_scn$deseq$all_tables$wt_scn_vs_het_scn %>%
  filter(logFC <= -0.1 & adj.P.Val <= 0.05)
kokeeper_genes <- mm_de_normal_p8_scn$deseq$all_tables$wt_scn_vs_ko_scn %>%
  filter(logFC <= -0.1 & adj.P.Val <= 0.05)
keepergenes <- unique(c(rownames(hetkeeper_genes), rownames(kokeeper_genes)))
df <- mm_de_normal_p8_scn$deseq$all_tables$koscn_vs_hetscn %>%
  dplyr::mutate(logFC = -logFC) %>%
  set_sig_limma(factors = c("Het Enriched",
                            "KO Enriched"))
df <- df %>%
  filter(Row.names %in% keepergenes)
labels_ups <- df %>%
  filter(abs(logFC) > 1) %>%
  arrange(logFC) %>%
  head(n = 1)
labels_downs <- df %>%
  filter(abs(logFC) > 1) %>%
  arrange(-logFC) %>%
  head(n = 1)
labels <- rbind(labels_ups, labels_downs)
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
  geom_point(aes(colour = Significance), size = 4) +
  geom_vline(xintercept = c(-1, 1)) +
  geom_hline(yintercept = -log10(0.05)) +
  theme_classic(base_size = 20) +
  xlab("log2(FC)") +
  ylab("-log10(p-value)") +
  ## ggtitle(title, subtitle = subtitle) +
  theme(legend.position="right") +
  scale_color_manual(values=c("Het Enriched" = "#F8766D",
                              "KO Enriched" = "#00BFC4",
                              "Not\n Enriched" = "Grey")) +
  geom_label_repel(data=filter(df,
                               ## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
                               external_gene_name %in% labels$external_gene_name),
                   ## nudge_x = -0.5,
                   nudge_y = 3, max.overlaps = 15) +
  ggtitle("SCN Het vs KO Translatome")

pp(file = "images/p08_scn_DE_1312024.pdf")
DEplot
plotted <- dev.off()

write_xlsx(df, excel = "excel/scnhet_vs_scnko_WTfiltered.xlsx")
```

## How many ups/downs

```{r, eval=FALSE}
ko_enriched <- df %>%
  filter(Significance == "KO Enriched")

het_enriched <- df %>%
  filter(Significance == "Het Enriched")
```

## GSEA

```{r, eval=FALSE}
ko_genes <- res_tbl %>%
  filter(adj.P.Val <= 0.05) %>%
  arrange(-abs(logFC)) %>%
  select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
  filter(logFC <= -1)

het_genes <- res_tbl %>%
  filter(adj.P.Val <= 0.05) %>%
  arrange(-abs(logFC)) %>%
  select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
  filter(logFC >= 1)

alldysregulated_genes <- res_tbl %>%
  filter(adj.P.Val <= 0.05) %>%
  arrange(logFC) %>%
  select(Row.names, logFC, adj.P.Val, external_gene_name, Significance) %>%
  filter(abs(logFC) >= 1)

gsea_result_ko <- gost(query = ko_genes$external_gene_name,
                       organism = "mmusculus",
                       evcodes = TRUE,
                       ordered_query = TRUE)

gsea_result_het <- gost(query = het_genes$external_gene_name,
                        organism = "mmusculus",
                        evcodes = TRUE,
                        ordered_query = TRUE)

gsea_result_alldysregulated <- gost(query = alldysregulated_genes$external_gene_name,
                                    organism = "mmusculus",
                                    evcodes = TRUE,
                                    ordered_query = TRUE)
```

```{r, eval=FALSE}
gsea_ko <-  gsea_result_ko[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 10)
gsea_plots_ko <- ggplot(gsea_ko, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over enrichment Score")

gsea_het <-  gsea_result_het[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 10)
gsea_plots_het <- ggplot(gsea_het, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over enrichment Score")

gsea_all <-  gsea_result_alldysregulated[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 10)
gsea_plots_all <- ggplot(gsea_all, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over enrichment Score")

pp(file = "images/GSEA_p08_retina_axontrap_alldysregulatedgenes.pdf")
gsea_plots_all
plotted <- dev.off()
```

# Het Retina vs Het SCN

```{r, eval=FALSE}
mm38_subset2 <- subset_se(
  mm38_hisat,
  subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08' & genotype != 'ko' & location != 'dlgn' | sampleid == 'iprgc_03'")

mm38_subset2 <- subset_se(mm38_subset2, subset = "sampleid != 'iprgc_89'")
mm38_subset2$design %>%
  select(genotype, location) %>%
  table()
mm38_norm2 <- normalize(mm38_subset2, filter=TRUE,
                             convert="cpm",
                             transform="log2", batch = "svaseq")
```

## PCA

```{r, eval=FALSE}
mm38_norm2 <- set_batches(mm38_norm2, fact = "location")
mm38_norm2 <- set_conditions(mm38_norm2, fact = "genotype")
pca_norm2 <- plot_pca(mm38_norm2, max_overlaps = 70)
pca_norm2$plot
```

## DE

```{r, eval=FALSE}
mm_de_subset2 <- all_pairwise(mm38_subset2,
                              model_batch="svaseq",
                              parallel=FALSE, do_ebseq=FALSE,
                              do_basic = FALSE, do_dream = FALSE,
                              do_noiseq = FALSE, do_edger = FALSE,
                              filter = TRUE)
```

```{r, eval=FALSE}
retinakeeper_genes <- mm_de_subset2$deseq$all_tables$wt_retina_vs_het_retina %>%
  filter(logFC <= -0.1 & adj.P.Val <= 0.05)

scnkeeper_genes <- mm_de_subset2$deseq$all_tables$wt_scn_vs_het_scn %>%
  filter(logFC <= -0.1 & adj.P.Val <= 0.05)

keepergenes <- unique(c(rownames(retinakeeper_genes), rownames(scnkeeper_genes)))

annots_to_merge <- mm_annot %>%
  select(ensembl_gene_id, external_gene_name) %>%
  filter(ensembl_gene_id %in% rownames(mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina)) %>%
  distinct()

mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina <- merge(
  mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina,
  annots_to_merge, by.x = 0,
  by.y = "ensembl_gene_id", all.x = TRUE)

df <- mm_de_subset2$deseq$all_tables$het_scn_vs_het_retina %>%
  mutate(Significance = case_when(logFC <= -1.0 ~ "Retina Enriched",
                                  logFC >= 1.0 ~ "SCN Enriched",
                                  logFC > -1.0 & logFC < 1.0 ~ "Not\n Enriched"))

df <- df %>%
  filter(Row.names %in% keepergenes)

scn_enriched <- df %>%
  filter(adj.P.Val <= 0.05 & logFC >= 1.0) %>%
  arrange(-logFC) %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
  mutate(Significance = "SCN Enriched") %>%
  filter(Row.names %in% rownames(scnkeeper_genes))

retina_enriched <- df %>%
  filter(adj.P.Val <= 0.05 & logFC <= -1.0) %>%
  arrange(logFC)  %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
  mutate(Significance = "Retina Enriched") %>%
  filter(Row.names %in% rownames(retinakeeper_genes))

notenriched <- df %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val, Significance) %>%
  filter(Row.names %in% c(rownames(retinakeeper_genes),
                          rownames(scnkeeper_genes))[duplicated(c(rownames(retinakeeper_genes),
                                                                  rownames(scnkeeper_genes)))]) %>%
  filter(Significance == "Not\n Enriched")

df <- rbind(scn_enriched, retina_enriched, notenriched)
df <- df %>%
  distinct()

## writexl::write_xlsx(df, path = "axonTRAP_DE_results_20240202/retinahet_vs_scn_het_WTfiltered.xlsx")
```

```{r, eval=FALSE}
labels_ups <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1.0) %>%
  arrange(logFC) %>%
  head(n = 10)

labels_downs <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1.0) %>%
  arrange(-logFC) %>%
  head(n = 10)

labels <- rbind(labels_ups, labels_downs)

labels_requested <- c("Cdh10","Cdh12","Cdh13","Cdh18",
                      "Cdh7","Cdh8","Cdh9","Cntn3",
                      "Cntn4","Cntn5","Cntn6","Kirrel3",
                      "Nrxn1","Nrxn3","Sema3c","Sema6d",
                      "Tenm1","Tenm2","Tenm4")
res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
  geom_point(aes(colour = Significance), size = 4) +
  geom_vline(xintercept = c(-1, 1)) +
  geom_hline(yintercept = -log10(0.05)) +
  theme_classic(base_size = 20) +
  xlab("log2(FC)") +
  ylab("-log10(p-value)") +
  ## ggtitle(title, subtitle = subtitle) +
  theme(legend.position="right") +
  scale_color_manual(values=c("Grey", "#F8766D", "#00BFC4")) +
  geom_label_repel(data=filter(df,
                               external_gene_name %in% labels_requested),
                   ## c(labels$external_gene_name, "Opn4")), #c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
                   ## nudge_x = -0.5,
                   nudge_y = 15, max.overlaps = 25)

#pp(file = "axonTRAP_Volcanoplots_20240202/p08_retinavsscnhet_DE_requested_genelabels_02052024.pdf")
DEplot
#dev.off()
```

## How many ups/downs

```{r, eval=FALSE}
scn_enriched <- df %>%
  filter(adj.P.Val <= 0.05 & logFC >= 1.0) %>%
  arrange(-logFC) %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val, Significance)

retina_enriched <- df %>%
  filter(adj.P.Val <= 0.05 & logFC <= -1.0) %>%
  arrange(logFC)  %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val, Significance)

scn_enriched
retina_enriched

df %>%
  filter(Significance == "Not\n Enriched")
```

## GSEA

```{r, eval=FALSE}
gsea_result_scn <- gost(query = scn_enriched$external_gene_name,
                        organism = "mmusculus", evcodes = TRUE,
                        ordered_query = TRUE, source = c("GO"))

gsea_result_ret <- gost(query = retina_enriched$external_gene_name,
                        organism = "mmusculus", evcodes = TRUE,
                        ordered_query = TRUE, source = c("GO"))
```

```{r, eval=FALSE}
gsea_scn <-  gsea_result_scn[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 20)
gsea_plots_scn <- ggplot(gsea_scn, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over enrichment Score")

pp(file = "images/GSEA_SCNhet_vs_retina_enriched_P08.pdf")
gsea_plots_scn
plotted <- dev.off()

gsea_ret <-  gsea_result_ret[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 20)
gsea_plots_ret <- ggplot(gsea_ret, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("Over enrichment Score")

pp(file = "images/GSEA_Retinahet_vs_SCN_enriched_P08.pdf")
gsea_plots_ret
plotted <- dev.off()
```

# KO Retina vs KO SCN

```{r, eval=FALSE}
mm38_subset3 <- subset_se(
  mm38_hisat,
  subset = "(batch == '4' | batch == '5' | batch == '6') & time == 'p08'  & genotype != 'het' & location != 'dlgn' | sampleid == 'iprgc_03'")

mm38_subset3 <- subset_se(mm38_subset3, subset = "sampleid != 'iprgc_86'")
mm38_subset3$design %>%
  select(genotype, location) %>%
  table()
mm38_norm3 <- normalize(mm38_subset3, filter=TRUE,
                             convert="cpm", transform="log2", batch = "svaseq")
```

## PCA

```{r, eval=FALSE}
mm38_norm3 <- set_batches(mm38_norm3, fact = "location")
mm38_norm3 <- set_conditions(mm38_norm3, fact = "genotype")
pca_norm3 <- plot_pca(mm38_norm3, max_overlaps = 70)
pca_norm3$plot
```

## DE

```{r, eval=FALSE}
mm_de_subset3 <- all_pairwise(mm38_subset3,
                              model_batch="svaseq",
                              parallel=FALSE, do_ebseq=FALSE,
                              do_basic = FALSE, do_dream = FALSE,
                              do_noiseq = FALSE, do_edger = FALSE,
                              filter = TRUE)

retinakeeper_genes <- mm_de_subset3$deseq$all_tables$wtretina_vs_koretina %>%
  filter(logFC <= -1.0 & adj.P.Val <= 0.05)

scnkeeper_genes <- mm_de_subset3$deseq$all_tables$wtscn_vs_koscn %>%
  filter(logFC <= -1.0 & adj.P.Val <= 0.05)

keepergenes <- unique(c(rownames(retinakeeper_genes), rownames(scnkeeper_genes)))

annots_to_merge <- mm_annot %>%
  select(ensembl_gene_id, external_gene_name) %>%
  filter(ensembl_gene_id %in% rownames(mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina)) %>%
  distinct()

mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina <- merge(
  mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina,
  annots_to_merge, by.x = 0,
  by.y = "ensembl_gene_id", all.x = TRUE)

df <- mm_de_subset3$deseq$all_tables$ko_scn_vs_ko_retina %>%
  mutate(Significance = case_when(logFC <= -1 ~ "Retina Enriched",
                                  logFC >= 1 ~ "SCN Enriched",
                                  logFC > -1 & logFC < 1 ~ "Not\n Enriched"))

df <- df %>%
  filter(Row.names %in% keepergenes)

scn_enriched <- df %>%
  filter(adj.P.Val <= 0.05 & logFC >= 1) %>%
  arrange(-logFC) %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
  mutate(Significance = "SCN Enriched") %>%
  filter(Row.names %in% rownames(scnkeeper_genes))

df %>%
  filter(adj.P.Val <= 0.05 & logFC <= -1) %>%
  arrange(logFC)  %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val) %>%
  mutate(Significance = "Retina Enriched") %>%
  filter(Row.names %in% rownames(retinakeeper_genes)) -> retina_enriched

notenriched <- df %>%
  select(Row.names, external_gene_name, logFC, adj.P.Val, Significance) %>%
  filter(Row.names %in% c(rownames(retinakeeper_genes),
                          rownames(scnkeeper_genes))[duplicated(c(rownames(retinakeeper_genes),
                                                                  rownames(scnkeeper_genes)))])

df <- rbind(scn_enriched, retina_enriched, notenriched)
```

```{r, eval=FALSE}
labels_ups <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
  arrange(logFC) %>%
  head(n = 10)

labels_downs <- df %>%
  filter(adj.P.Val <= 0.05 & abs(logFC) > 1) %>%
  arrange(-logFC) %>%
  head(n = 10)

labels <- rbind(labels_ups, labels_downs)
## wanted_column <- "Significance"

res_tbl <- df
DEplot <- ggplot(res_tbl, aes(x = logFC, y = -log10(adj.P.Val), label = external_gene_name)) +
  geom_point(aes(colour = Significance), size = 4) +
  ## geom_point(aes(colour = !!sym(wanted_column)), size = 4) +
  geom_vline(xintercept = c(-1, 1)) +
  geom_hline(yintercept = -log10(0.05)) +
  theme_classic(base_size = 20) +
  xlab("log2(FC)") +
  ylab("-log10(p-value)") +
  ## ggtitle(title, subtitle = subtitle) +
  theme(legend.position = "right") +
  scale_color_manual(values = c("Grey", "#F8766D", "#00BFC4")) +
  geom_label_repel(data = filter(
    df, external_gene_name %in% c(labels$external_gene_name, "Opn4")),
    ## c('s5_het_dlgn', 's5_het_ret', 's5_het_scn')),
    ## nudge_x = -0.5,
    nudge_y = 10, max.overlaps = 25)

pp(file = "images/p08_retinavsscnko_DE_1312024.pdf")
DEplot
plotted <- dev.off()
```

## How many ups/downs

```{r, eval=FALSE}
scn_enriched
retina_enriched
notenriched %>%
  filter(Significance == "Not\n Enriched")
```

## GSEA

```{r, eval=FALSE}
gsea_result_scn <- gost(query = scn_enriched$external_gene_name,
                        organism = "mmusculus",
                        evcodes = TRUE,
                        ordered_query = TRUE,
                        source = c("GO"))

gsea_result_ret <- gost(query = retina_enriched$external_gene_name,
                        organism = "mmusculus",
                        evcodes = TRUE,
                        ordered_query = TRUE,
                        source = c("GO"))
```

```{r, eval=FALSE}
gsea_scn <-  gsea_result_scn[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 20)
gsea_plots_scn <- ggplot(gsea_scn, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("GSEA Score")

pp(file = "images/GSEA_SCNko_enriched_vs_retina_P08.pdf")
gsea_plots_scn
plotted <- dev.off()

gsea_ret <-  gsea_result_ret[["result"]] %>%
  select(term_name, p_value, term_size, intersection_size, recall, source, intersection) %>%
  arrange(desc(recall)) %>%
  head(n = 20)
gsea_plots_ret <- ggplot(gsea_ret, aes(x = recall, y = reorder(term_name, recall), fill = p_value)) +
  geom_bar(stat = "identity") +
  scale_fill_continuous(low = "blue", high = "red") +
  theme_bw() +
  ylab("") +
  xlab("GSEA Score")

pp(file = "images/GSEA_Retinako_enriched_vs_SCN_P08.pdf")
gsea_plots_ret
plotted <- dev.off()
```

# My version of the global analysis

I want to have an invocation of all_pairwise() which uses all samples,
in the following block I will set that up using a set of 'keepers'
which will be named by time, location, then 2 letters for the
numerator/denominator: w for WT, h for het, d for delta; thus
"p08_retina_hw" is comparing the het/wt for the p08 retina samples.

If they are of interest, I will have a separate set which follows the
same convention with names like "p08_ko_sr" to compare p08 deltas with
SCN as the numerator and retina as the denominator.

## Set up the exclusion dataset

The most peculiar aspect of this analysis resides in the choices
around choosing which genes to consider when comparing the
genotypes/locations/times.  The general idea is pretty clear: find the
genes which are non-specifically being pulled down in the WT samples
and either exclude or discount them.  The various potential methods
for performing this are confusing:

1.  Which set of comparisons of wt/ko wt/het do we use to
    exclude/discount genes?
    a.  Should it be a combination of all samples wt vs. x?
    b.  Should it be only the 'relevant' comparison, e.g. if we are
    comparing p08_dlgn_het vs. p08_scn_het; do we remove genes
    observed in (p08_dlgn_het/wt && p08_scn_het/wt)
2.  Do we instead attempt to use this x/wt information to normalize
    the expression values in the other conditions and keep those
    genes?

Theresa's current worksheet implements a version of 1b in which she
separated the various input gene sets to define the exclusion genes.
I am going to repeat this, but leave the starting data structure
intact.

In this first iteration, I will do that by creating a simplified model
of the data which combines the time/genotype/location and using sva.
In my next iteration I will use a full statistical model containing
each of those factors (and probably also using sva).

Note: my color choices are kind of garbage.

In addition, the exclusion dataset is the same as the analysis
dataset, it is really only the contrasts which will be different.

```{r}
v3_pairwise_input <- set_conditions(mm38_hisat_v3, fact = "time_geno_loc",
                                    colors = color_choices[["all"]])
```

## A heatmap of specific genes across all conditions

```{r}
all_cond_gene_heatmap_start <- normalize(v3_pairwise_input, filter = "simple",
                                         length_column = "cds_length",
                                         convert = "rpkm", transform = "log2")
all_cond_gene_heatmap_input <- median_by_factor(all_cond_gene_heatmap_start)
all_cond_mtrx <- all_cond_gene_heatmap_input[["medians"]]
color_order <- colnames(all_cond_mtrx)
na_idx <- is.na(all_cond_mtrx)
all_cond_mtrx[na_idx] <- 0
variances <- matrixStats::rowVars(as.matrix(all_cond_mtrx))
variant_genes <- variances > 6.2
input_mtrx <- all_cond_mtrx[variant_genes, ]
cond_colors <- get_colors_by_condition(v3_pairwise_input, levels = color_order)
dim(input_mtrx)
pp(file = "04inclusion_comparisons/top_104_variant_rpkm_genes_heatmap.pdf")
gplots::heatmap.2(as.matrix(input_mtrx), scale = "none", trace = "none",
                  ColSideColors = cond_colors)
dev.off()
```

# TODO: Change the above to subtract wt

Rashmi suggested we should do the above plot after subtracting the wt
counts.  This is a good idea, but it will have to wait until we finish
the current set.

## Set up the contrasts

In the following few blocks I will set up the various comparisons of
interest.  Starting with the set of genes to exclude because they were
observed to bind non-specifically in the wt samples.

### Inclusion contrasts

In each exclusion I will have the contrast first followed by the pair
of contrasts which will be used to define the gene set to exclude.

* p15_het_dlgn/p08_het_dlgn: p15_wt_dlgn/p15_het_dlgn,
  p08_wt_dlgn/p08_het_dlgn; remove the genes increased in wt.
* p15_ko_scn/p08_ko_scn: p15_wt_scn/p15_ko_scn, p08_wt_scn/p15_ko_scn
* p15_het_retina/p08_het_retina: I think you get it, wt/het for both
  p15 retinas and p08 retinas...

Put slightly differently, for every term of interest I will create a
contrast with the wt as numerator and the desired term as denominator,
then pull out the genes increased in wt.

```{r}
inclusions <- list(
  ## I like alphabetizing things, start with dlgn
  "p15_het_dlgn" = c("p15_het_dlgn", "p15_wt_dlgn"),
  "p08_het_dlgn" = c("p08_het_dlgn", "p08_wt_dlgn"),
  "p15_ko_dlgn" = c("p15_ko_dlgn", "p15_wt_dlgn"),
  "p08_ko_dlgn" = c("p08_ko_dlgn", "p08_wt_dlgn"),
  ## Then retinas
  "p15_het_retina" = c("p15_het_retina", "p15_wt_retina"),
  "p08_het_retina" = c("p08_het_retina", "p08_wt_retina"),
  "p15_ko_retina" = c("p15_ko_retina", "p15_wt_retina"),
  "p08_ko_retina" = c("p08_ko_retina", "p08_wt_retina"),
  ## Then scn
  "p15_het_scn" = c("p15_het_scn", "p15_wt_scn"),
  "p08_het_scn" = c("p08_het_scn", "p08_wt_scn"),
  "p15_ko_scn" = c("p15_ko_scn", "p15_wt_scn"),
  "p08_ko_scn" = c("p08_ko_scn", "p08_wt_scn"))
```

### Time contrasts

For each location/genotype of interest, let us compare p15/p08

```{r}
time_keepers <- list(
  ## DLGN
  "t_het_dlgn" = c("p15_het_dlgn", "p08_het_dlgn"),
  "t_ko_dlgn" = c("p15_ko_dlgn", "p08_ko_dlgn"),
  ## Retina
  "t_het_retina" = c("p15_het_retina", "p08_het_retina"),
  "t_ko_retina" = c("p15_ko_retina", "p08_ko_retina"),
  ## SCN
  "t_het_scn" = c("p15_het_scn", "p08_het_scn"),
  "t_ko_scn" = c("p15_ko_scn", "p08_ko_scn"))
```

### Location contrasts

Compare locations and keep time/genotype consistent.  I will use the
location initials to define numerator/denominator.

```{r}
location_keepers <- list(
  ## dlgn/retina
  "dr_p08_het" = c("p08_het_dlgn", "p08_het_retina"),
  "dr_p15_het" = c("p15_het_dlgn", "p15_het_retina"),
  "dr_p08_ko" = c("p08_ko_dlgn", "p08_ko_retina"),
  "dr_p15_ko" = c("p15_ko_dlgn", "p15_ko_retina"),
  ## scn/retina
  "sr_p08_het" = c("p08_het_scn", "p08_het_retina"),
  "sr_p15_het" = c("p15_het_scn", "p15_het_retina"),
  "sr_p08_ko" = c("p08_ko_scn", "p08_ko_retina"),
  "sr_p15_ko" = c("p15_ko_scn", "p15_ko_retina"),
  ## dlgn/scn
  "ds_p08_het" = c("p08_het_dlgn", "p08_het_scn"),
  "ds_p15_het" = c("p15_het_dlgn", "p15_het_scn"),
  "ds_p08_ko" = c("p08_ko_dlgn", "p08_ko_scn"),
  "ds_p15_ko" = c("p15_ko_dlgn", "p15_ko_scn"))
```

### Genotype contrasts

Compare ko/het while keeping time/location constant.  Similarly, use
the initials to denote numerator/denominator, which will always be kh.

```{r}
genotype_keepers <- list(
  ## DLGN
  "kh_p08_dlgn" = c("p08_ko_dlgn", "p08_het_dlgn"),
  "kh_p15_dlgn" = c("p15_ko_dlgn", "p15_het_dlgn"),
  ## Retina
  "kh_p08_retina" = c("p08_ko_retina", "p08_het_retina"),
  "kh_p15_retina" = c("p15_ko_retina", "p15_het_retina"),
  ## SCN
  "kh_p08_scn" = c("p08_ko_scn", "p08_het_scn"),
  "kh_p15_scn" = c("p15_ko_scn", "p15_het_scn"))
```

## Perform the exclusion comparison

My all_pairwise() function now has a parameter which allows me to
choose which contrasts to perform instead of literally doing every
possible comparison.  That is well suited for these operations:

In a container, the following appears to fail with:

"error code 1 from Lapack routine 'dgesdd'"

Running it manually outside the container results in it working
without error.  I assume therefore that the problem lies in the
compilation flags of LAPACK in the container.

Note: This problem was fixed by removing some parallelization.

```{r}
inclusion_de <- all_pairwise(
  v3_pairwise_input, filter = "simple", model_fstring = default_fstring,
  keepers = inclusions, model_svs = "svaseq")
inclusion_de

inclusion_tables <- combine_de_tables(
  inclusion_de, keepers = inclusions, label_column = label_column,
  excel = glue("04inclusion_comparisons/inclusion_tables-v{ver}.xlsx"))
inclusion_tables
## 202603: I successfully recapitulated previous non-container result.

inclusion_sig <- extract_significant_genes(
  inclusion_tables, lfc = lfc_cutoff, p = adjp_cutoff, according_to = "deseq",
  excel = glue("04inclusion_comparisons/inclusion_sig-v{ver}.xlsx"))
inclusion_sig
```

202505: A strange thing happened here in this iteration: the plot of
the significant genes is the exact same as the previous iteration; but
the table of numbers of genes looks different.

For example, the previous table showed: p15_het_dlgn with 2067 up and
2381 down.  The plot shows exactly that; but the new table shows 607
up and 1229 down.  Let us check the actual data structure and see what
is up?

I think I get it: when we do the extract_significant_genes above, we
explicitly set a non-standard p-value and logFC because we are
explicitly attempting to use a very loose definition of the set of
genes which are in greater abundance than their most similar
wild-type. However, when I create the barplot of significant genes;
those values are explicitly set to 0,1,2 logFCs and p-value 0.05.
Therefore, what I need to do, in order to check consistency, is to
repeat this call but with the default FC/p values and see what the
numbers look like.

```{r}
test_inclusion <- extract_significant_genes(
  inclusion_tables, according_to = "deseq", excel = "excel/default_inclusion_sig.xlsx")
test_inclusion
```

Yeah, I think this makes sense; what I need to do: change the
significant bar plot so that it uses the lfc cutoff argument as the
second of its 3 cutoffs.  That should ensure that these numbers are
consistent across analyses and parameters provided.

```{r}
dim(inclusion_sig$deseq$ups$p15_het_dlgn)
test_all_up <- inclusion_tables$data$p15_het_dlgn[["deseq_logfc"]] > 0.1 &
  inclusion_tables$data$p15_het_dlgn[["deseq_adjp"]] <= 0.1
summary(test_all_up)
```

Ohh, I get it, when I was testing this out manually, I set the logFC
to 1.0 instead of the very minimal 0.1 we have been using for this!

# MA/Volcano plots of x vs wt

Rashmi asked to see the comparisons against wt; I will name each file
xw to show that it is x vs wt. for whatever other parameters are being
examined.  It is likely that some colors will be wrong because this is
my first time creating these plots and we are doing them manually.

## p15_het_dlgn

```{r}
allc <- color_choices[["all"]]
table_name <- "p15_het_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_dlgn"
denom <- "p15_wt_dlgn"
hw_p15_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)

pp(file = "05inclusion_volcano_ma/hw_p15_dlgn_volcano.pdf", width = 9, height = 9)
hw_p15_dlgn_volcano[["plot"]]
plotted <- dev.off()
hw_p15_dlgn_volcano[["plot"]]

hw_p15_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)

pp(file = "05inclusion_volcano_ma/hw_p15_dlgn_ma.pdf", width = 9, height = 9)
hw_p15_dlgn_ma[["plot"]]
plotted <- dev.off()
hw_p15_dlgn_ma[["plot"]]
```

## p08_het_dlgn

```{r}
table_name <- "p08_het_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_dlgn"
denom <- "p08_wt_dlgn"
hw_p08_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_dlgn_volcano.pdf", width = 9, height = 9)
hw_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
hw_p08_dlgn_volcano[["plot"]]
hw_p08_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_dlgn_ma.pdf", width = 9, height = 9)
hw_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
hw_p08_dlgn_ma[["plot"]]
```

## p15_ko_dlgn

```{r}
table_name <- "p15_ko_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_dlgn"
denom <- "p15_wt_dlgn"
kw_p15_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_dlgn_volcano.pdf", width = 9, height = 9)
kw_p15_dlgn_volcano[["plot"]]
plotted <- dev.off()
kw_p15_dlgn_volcano[["plot"]]
kw_p15_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_dlgn_ma.pdf", width = 9, height = 9)
kw_p15_dlgn_ma[["plot"]]
plotted <- dev.off()
kw_p15_dlgn_ma[["plot"]]
```

## p08_ko_dlgn

```{r}
table_name <- "p08_ko_dlgn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_dlgn"
denom <- "p08_wt_dlgn"
kw_p08_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_dlgn_volcano.pdf", width = 9, height = 9)
kw_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
kw_p08_dlgn_volcano[["plot"]]
kw_p08_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/kw_p08_dlgn_ma.pdf", width = 9, height = 9)
kw_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
kw_p08_dlgn_ma[["plot"]]
```

## p15_het_retina

```{r}
table_name <- "p15_het_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_retina"
denom <- "p15_wt_retina"
hw_p15_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/hw_p15_retina_volcano.pdf", width = 9, height = 9)
hw_p15_retina_volcano[["plot"]]
plotted <- dev.off()
hw_p15_retina_volcano[["plot"]]
hw_p15_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/hw_p15_retina_ma.pdf", width = 9, height = 9)
hw_p15_retina_ma[["plot"]]
plotted <- dev.off()
hw_p15_retina_ma[["plot"]]
```

## p08_het_retina

```{r}
table_name <- "p08_het_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_retina"
denom <- "p08_wt_retina"
hw_p08_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_retina_volcano.pdf", width = 9, height = 9)
hw_p08_retina_volcano[["plot"]]
plotted <- dev.off()
hw_p08_retina_volcano[["plot"]]
hw_p08_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_retina_ma.pdf", width = 9, height = 9)
hw_p08_retina_ma[["plot"]]
plotted <- dev.off()
hw_p08_retina_ma[["plot"]]
```

## p15_ko_retina

```{r}
table_name <- "p15_ko_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_retina"
denom <- "p15_wt_retina"
kw_p15_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_retina_volcano.pdf", width = 9, height = 9)
kw_p15_retina_volcano[["plot"]]
plotted <- dev.off()
kw_p15_retina_volcano[["plot"]]
kw_p15_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_retina_ma.pdf", width = 9, height = 9)
kw_p15_retina_ma[["plot"]]
plotted <- dev.off()
kw_p15_retina_ma[["plot"]]
```

## p08_ko_retina

```{r}
table_name <- "p08_ko_retina"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_retina"
denom <- "p08_wt_retina"
kw_p08_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_retina_volcano.pdf", width = 9, height = 9)
kw_p08_retina_volcano[["plot"]]
plotted <- dev.off()
kw_p08_retina_volcano[["plot"]]
kw_p08_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma_p08_retina_ma.pdf", width = 9, height = 9)
kw_p08_retina_ma[["plot"]]
plotted <- dev.off()
kw_p08_retina_ma[["plot"]]
```

## p15_het_scn

```{r}
table_name <- "p15_het_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_het_scn"
denom <- "p15_wt_scn"
hw_p15_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/hw_p15_scn_volcano.pdf", width = 9, height = 9)
hw_p15_scn_volcano[["plot"]]
plotted <- dev.off()
hw_p15_scn_volcano[["plot"]]
hw_p15_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/hw_p15_scn_ma.pdf", width = 9, height = 9)
hw_p15_scn_ma[["plot"]]
plotted <- dev.off()
hw_p15_scn_ma[["plot"]]
```

## p08_het_scn

```{r}
table_name <- "p08_het_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_het_scn"
denom <- "p08_wt_scn"
hw_p08_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/hw_p08_scn_volcano.pdf", width = 9, height = 9)
hw_p08_scn_volcano[["plot"]]
plotted <- dev.off()
hw_p08_scn_volcano[["plot"]]
hw_p08_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/hw_p08_scn_ma.pdf", width = 9, height = 9)
hw_p08_scn_ma[["plot"]]
plotted <- dev.off()
hw_p08_scn_ma[["plot"]]
```

## p15_ko_scn

```{r}
table_name <- "p15_ko_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p15_ko_scn"
denom <- "p15_wt_scn"
kw_p15_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p15_scn_volcano.pdf", width = 9, height = 9)
kw_p15_scn_volcano[["plot"]]
plotted <- dev.off()
kw_p15_scn_volcano[["plot"]]
kw_p15_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/kw_p15_scn_ma.pdf", width = 9, height = 9)
kw_p15_scn_ma[["plot"]]
plotted <- dev.off()
kw_p15_scn_ma[["plot"]]
```

## p08_ko_scn

```{r}
table_name <- "p08_ko_scn"
table <- inclusion_tables[["data"]][[table_name]]
num <- "p08_ko_scn"
denom <- "p08_wt_scn"
kw_p08_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = allc[[denom]], color_high = allc[[num]],
  label_column = "mgi_symbol", label = 10, alpha = 1.0,
  size = 4)
pp(file = "05inclusion_volcano_ma/kw_p08_scn_volcano.pdf", width = 9, height = 9)
kw_p08_scn_volcano[["plot"]]
plotted <- dev.off()
kw_p08_scn_volcano[["plot"]]
kw_p08_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = allc[[denom]], color_high = allc[[num]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", outline = outline,
  label = 10)
pp(file = "05inclusion_volcano_ma/kw_p08_scn_ma.pdf", width = 9, height = 9)
kw_p08_scn_ma[["plot"]]
plotted <- dev.off()
kw_p08_scn_ma[["plot"]]
```

See the shared/unique genes in these sets.

```{r}
inclusion_upsets <- upsetr_sig(inclusion_sig)
inclusion_intersects <- write_upset_groups(
  inclusion_upsets, excel = "04inclusion_comparison/inclusion_gene_groups.xlsx")
```

### Extract genes included for each set of contrasts

Now, using that function, pull out the gene IDs of genes we do not
trust because they were too high in wt for every contrast we are
likely to perform.

The following was a modified version of the inclusion function which is somewhat more restrictive.

```{r}
extract_inclusions_strict <- function(inclusion_sig, inclusion_tables, inclusions, keepers,
                                      all_genes, according_to = "deseq", which = "ups") {
  retlist <- list()
  table_names <- names(inclusion_sig[[according_to]][[which]])
  for (c_num in seq_along(keepers)) {
    contrast <- names(keepers)[c_num]
    numerator_name <- keepers[[c_num]][1]
    denominator_name <- keepers[[c_num]][2]
    ## In my new branch I cleaned up the sanitizer function for contrasts so this is not needed.
    ## The following two lines are no longer needed because of the cleanups I performed.
    ##numerator_name <- gsub(x = numerator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
    ##denominator_name <- gsub(x = denominator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
    numerator_table <- inclusion_sig[[according_to]][[which]][[numerator_name]]
    numerator_genes <- rownames(numerator_table)
    denominator_table <- inclusion_sig[[according_to]][[which]][[denominator_name]]
    denominator_genes <- rownames(denominator_table)
    df_columns <- paste0("deseq_", c("logfc", "adjp", "den"))
    included_num <- inclusion_tables[["data"]][[numerator_name]][, df_columns]
    colnames(included_num) <- c("numerator_vs_wt_logfc", "numerator_vs_wt_adjp", "num_wt_mean_exprs")
    included_den <- inclusion_tables[["data"]][[denominator_name]][, df_columns]
    colnames(included_den) <- c("denominator_vs_wt_logfc", "denominator_vs_wt_adjp", "den_wt_mean_exprs")
    ## I think this is where things went wrong,
    ## compare this modified line to the original to prove it.
    included_df <- merge(included_num, included_den, by = "row.names")
    ## Previously, I did not specify the merge action, all = FALSE by default.
    ## This then will result in a difference in the rows observed
    ## included_df <- merge(included_num, included_den, by = "row.names", all = FALSE)
    rownames(included_df) <- included_df[["Row.names"]]
    included_df[["Row.names"]] <- NULL
    concatenated_genes <- c(numerator_genes, denominator_genes)
    both_gene_idx <- duplicated(concatenated_genes)
    genes_in_both <- concatenated_genes[both_gene_idx]
    message("The set of unique genes higher in ", numerator_name,
            " vs. wt is ", length(numerator_genes), ".")
    message("The set of unique genes higher in ", denominator_name,
            " vs. wt is ", length(denominator_genes), ".")
    message("The intersection of them is ", length(genes_in_both), " genes.")
    include_name <- paste0("inc_", contrast)
    include_idx <- all_genes %in% genes_in_both
    include_genes <- all_genes[include_idx]
    df_name <- paste0("df_", contrast)
    retlist[[df_name]] <- included_df
    written_inclusion <- write_xlsx(
      data = included_df,
      excel = glue("07included_strict_genes_excel/{include_name}-v{ver}.xlsx"))
    retlist[[include_name]] <- include_genes
    retlist[[contrast]] <- include_genes
  }
  return(retlist)
}
```

This is the pre-202505 version of this function.

```{r}
extract_inclusions <- function(inclusion_sig, inclusion_tables, inclusions, keepers, all_genes,
                               according_to = "deseq", which = "ups") {
  retlist <- list()
  table_names <- names(inclusion_sig[[according_to]][[which]])
  for (c_num in seq_along(keepers)) {
    contrast <- names(keepers)[c_num]
    numerator_name <- keepers[[c_num]][1]
    denominator_name <- keepers[[c_num]][2]
    ## In my new branch I cleaned up the sanitizer function for contrasts so this is not needed.
    ## The following two lines are no longer needed because of the cleanups I performed.
    ##numerator_name <- gsub(x = numerator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
    ##denominator_name <- gsub(x = denominator_name, pattern = "(het|ko|wt)", replacement = "_\\1_")
    numerator_table <- inclusion_sig[[according_to]][[which]][[numerator_name]]
    numerator_genes <- rownames(numerator_table)
    denominator_table <- inclusion_sig[[according_to]][[which]][[denominator_name]]
    denominator_genes <- rownames(denominator_table)
    df_columns <- paste0("deseq_", c("logfc", "adjp", "den"))
    included_num <- inclusion_tables[["data"]][[numerator_name]][, df_columns]
    colnames(included_num) <- c("numerator_vs_wt_logfc", "numerator_vs_wt_adjp", "num_wt_mean_exprs")
    included_den <- inclusion_tables[["data"]][[denominator_name]][, df_columns]
    colnames(included_den) <- c("denominator_vs_wt_logfc", "denominator_vs_wt_adjp", "den_wt_mean_exprs")
    included_df <- merge(included_num, included_den, by = "row.names")
    rownames(included_df) <- included_df[["Row.names"]]
    included_df[["Row.names"]] <- NULL
    include_genes <- unique(c(numerator_genes, denominator_genes))
    message("The set of unique genes higher in ", numerator_name,
            " vs. wt is ", length(numerator_genes), ".")
    message("The set of unique genes higher in ", denominator_name,
            " vs. wt is ", length(denominator_genes), ".")
    message("The unique union of them is ", length(include_genes), " genes.")
    include_name <- paste0("inc_", contrast)
    include_idx <- all_genes %in% include_genes
    include_genes <- all_genes[include_idx]
    df_name <- paste0("df_", contrast)
    retlist[[df_name]] <- included_df
    written_inclusion <- write_xlsx(data = included_df,
                                    excel = glue("included_genes/{include_name}-v{ver}.xlsx"))
    retlist[[include_name]] <- include_genes
    retlist[[contrast]] <- include_genes
  }
  return(retlist)
}
```

# 'Normal' Inclusion extraction

Here is the full set of gene IDs

```{r}
all_genes <- rownames(assay(v3_pairwise_input))
```

In the following blocks I am including the union of genes observed
higher than wt in either of the numerator or denominator for each
contrast.

## Time

```{r}
time_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                      time_keepers, all_genes)
```

## Location

```{r}
location_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                          location_keepers, all_genes)
```

## Genotype

```{r}
genotype_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                          genotype_keepers, all_genes)
```

# The strict 'inclusion' set

## Time

```{r}
time_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
                                                    time_keepers, all_genes)
```

## Location

```{r}
location_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
                                                        location_keepers, all_genes)
```

## Genotype

```{r}
genotype_inclusions_strict <- extract_inclusions_strict(inclusion_sig, inclusion_tables, inclusions,
                                                        genotype_keepers, all_genes)
```

### Check vs Theresa's filter

Up above Theresa performed a 0.25 log2FC and 0.05 adjp filter which
provided a set of 2,640 genes observed higher in the p08 het retinas
vs. wt retinas.  I should see that in this inclusion_sig data structure.

There is an important caveat though: in Theresa's filter above, she
did a DE of _only_ the retina samples but I did all samples.  I
expected that this would result in basically the same result (I
actually assumed I would get a few more genes), but instead it appears
to have retrieved a significantly smaller number of genes (about 1/2,
happily they pretty much all appear in the previous filter).  As a
result, I am going to try relaxing my constraints slightly to see if I
can recapitulate her filter (which would match Theresa's later filter,
though I guess that in turn will lead to a smaller set of genes
compared to her later, relaxed 0.1 filter).

```{r}
comparison <- inclusion_sig[["deseq"]][["ups"]][["p08_het_retina"]]
comp <- list(
  "taa" = taa_keepers,
  "new" = rownames(comparison))
test_comparison <- Vennerable::Venn(comp)
Vennerable::plot(test_comparison)
```

I want to have a little function which, given a contrast of interest,
will extract the gene sets which should be included/excluded given the
above.

```{r}
write_all_cp <- function(all_cp, prefix = "12", suffix = "") {
  all_written <- list()
  for (g in seq_len(length(all_cp))) {
    name <- names(all_cp)[g]
    datum <- all_cp[[name]]
    filename <- glue("{prefix}enrichment_excel/{name}_cprofiler{suffix}-v{ver}.xlsx")
    written <- sm(write_cp_data(datum, excel = filename))
    all_written[[g]] <- written
  }
  return(all_written)
}
write_all_gp <- function(all_gp, prefix = "13", suffix = "") {
  all_written <- list()
  for (g in seq_len(length(all_gp))) {
    name <- names(all_gp)[g]
    datum <- all_gp[[name]]
    filename <- glue("{prefix}enrichment_excel/{name}_gprofiler{suffix}-v{ver}.xlsx")
    written <- sm(write_gprofiler_data(datum, excel = filename))
    all_written[[g]] <- written
  }
  return(all_written)
}
write_all_en <- function(all_en, prefix = "14", suffix = "") {
  all_written <- list()
  for (e in seq_len(length(all_en))) {
    name <- names(all_en)[e]
    datum <- all_en[[name]]
    filename <- glue("{prefix}enrichment_excel/{name}_enricher{suffix}-v{ver}.xlsx")
    written <- sm(write_enricher_data(datum, excel = filename))
    all_written[[e]] <- written
  }
  return(all_written)
}
```

### Extract genes included for each set of contrasts

Now, using that function, pull out the gene IDs of genes we do not
trust because they were too high in wt for every contrast we are
likely to perform.

```{r}
all_genes <- rownames(assay(v3_pairwise_input))
time_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                      time_keepers, all_genes)
location_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                          location_keepers, all_genes)
genotype_inclusions <- extract_inclusions(inclusion_sig, inclusion_tables, inclusions,
                                          genotype_keepers, all_genes)
```

## Perform the DE analyses and exclude the target genes


### Genotype

```{r}
genotype_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
                            keepers = genotype_keepers, model_svs = "svaseq")
genotype_de
```

### Location

```{r}
location_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
                            keepers = location_keepers, model_svs = "svaseq")
location_de
```

### Time

```{r}
time_de <- all_pairwise(v3_pairwise_input, filter = TRUE, model_fstring = default_fstring,
                        keepers = time_keepers, model_svs = "svaseq")
time_de
```

It is near here when the computer sometimes fails with no more
tempfiles.  In another window I am messing with tempfile() in R to try
to understand where it is going off the rails...

## Extract the relevant tables and include genes lower in wt

### Genotype contrasts

I will start with the tables and no inclusions so I can check my work.

In this first block I will explain a little more thoroughly what is
going on:

1.  Dump the full table of the contrasts I defined above comparing the
    3 genotypes across time/location.
2.  Iterate over each of those contrasts and do the following:
    a.  Extract the name of the contrast, 'kh_p08_dlgn' for example
    b.  Yank out that specific entry from the keeper list and its name
    c.  Yank out the corresponding set of genes to include from the
        inclusions data structure.
    d.  Create a filename given the name in (a) above and the logFC
        cutoff chosen for the inclusions (I am assuming we may change
        this)
    e.  Given (b), (c), and (d), extract the corresponding table from
        the differential expression analysis and include the appropriate
        genes.

    FIXME: my gprofiler function just assumes human and so if passed mmusculus will incorrectly
    attempt to connect to non-existant databases.  Let us fix that now.

```{r}
genotype_tables_full <- combine_de_tables(
  genotype_de, keepers = genotype_keepers, label_column = label_column,
  fancy = TRUE,
  excel = glue("08full_contrasts_excel/genotype_full_tables-v{ver}.xlsx"))
genotype_tables_full
genotype_sig_full <- extract_significant_genes(
  genotype_tables_full, according_to = "deseq",
  excel = glue("08full_contrasts_excel/genotype_full_sig-v{ver}.xlsx"))
genotype_sig_full
```

### Search Enrichment of these sets

In this run, we will search the full set of genes, next we will only
do the inclusions.

```{r}
genotype_full_gp <- all_gprofiler(genotype_sig_full, species = "mmusculus",
                                  excel = "09full_contrasts_enrich/genotype_full_gprofiler.xlsx")
genotype_full_cp <- all_cprofiler(genotype_sig_full, genotype_tables_full,
                                  orgdb = "org.Mm.eg.db", go_level = go_level, organism = "mouse",
                                  orgdb_from = "ENSEMBL", max_groupsize = max_groupsize,
                                  excel = "09full_contrasts_enrich/genotype_full_cprofiler.xlsx")
genotype_full_upset <- upsetr_sig(genotype_sig_full)
genotype_full_intersects <- write_upset_groups(genotype_full_upset,
                                               excel = "09full_contrasts_intersections/genotype_full_gene_groups.xlsx")
```






```{r}
genotype_tables <- list()
genotype_sig <- list()
genotype_gp <- list()
genotype_cp <- list()
for (k in seq_along(genotype_keepers)) {
  name <- names(genotype_keepers)[k]
  message("Examining ", name)
  keeper <- genotype_keepers[name]
  include_name <- paste0("inc_", name)
  include_df_name <- paste0("df_", name)
  include_df <- genotype_inclusions[[include_df_name]]
  includes <- genotype_inclusions[[include_name]]
  summary(rownames(genotype_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
  include_filename <- glue("genotype_contrasts/genotype_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
  include_sig_filename <- glue("genotype_contrasts/genotype_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
  genotype_tables[[name]] <- combine_de_tables(
    genotype_de, extra_annot = include_df,
    keepers = keeper, label_column = label_column,
    excel = include_filename, wanted_genes = includes)
  print(genotype_tables[[name]])
  genotype_sig[[name]] <- extract_significant_genes(
    genotype_tables[[name]], according_to = "deseq",
    excel = include_sig_filename)
  print(genotype_sig[[name]])
  num_rows <- nrow(genotype_sig[[name]][["deseq"]][["ups"]][[name]]) +
    nrow(genotype_sig[[name]][["deseq"]][["downs"]][[name]])
  message("There are ", num_rows, " significant up and down genes.")
  if (num_rows >= 10) {
    message("Performing gprofiler/clusterProfiler.")
    genotype_gp[[name]] <- all_gprofiler(genotype_sig[[name]], species = "mmusculus")
    gp_written <- write_all_gp(genotype_gp[[name]])
    genotype_cp[[name]] <- all_cprofiler(genotype_sig[[name]], genotype_tables[[name]],
                                         orgdb = "org.Mm.eg.db", kegg_organism = "mmu")
    cp_written <- write_all_cp(genotype_cp[[name]])
  } else {
    warning("There are less than 10 genes up and down in the ", name, " comparison.")
    message("There are less than 10 genes up and down in the ", name, " comparison.")
  }
}
```

A few specific plots of interest: Colenso asked to label a few genes
for the knockout/het p08_retinas, p08_scn, and p08_dlgn: either the
top-15 or all significant.  I am pretty sure if I tell it 15 and there
are not that many, it will just do the significant?  Let us find out!

#### ko/het for p08 retinas

For some crazy reason, this plot is double-labelling!

```{r}
table_name <- "kh_p08_retina"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting <- c("Opn4", "Gm9008", "Lrr1", "Cnbd1")
kh_p08_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
  label_column = "mgi_symbol", label = interesting, alpha = 1.0,
  size = 4)
pp(file = "images/kh_p08_retina_volcano.pdf", width = 9, height = 9)
kh_p08_retina_volcano[["plot"]]
plotted <- dev.off()
kh_p08_retina_volcano[["plot"]]
## why in the crap is it double-labelling!?

## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting)
pp(file = "images/kh_p08_retina_ma.pdf", width = 9, height = 9)
kh_p08_retina_ma[["plot"]]
plotted <- dev.off()
kh_p08_retina_ma[["plot"]]
```

#### ko/het p08 SCN

Holy crappers, this plot did _not_ double label; oooh I have a check
in my plotter to see if there are too few/too many labels and I
foolishly allowed it to concatenate the labels!  What in the crap was
I thinking?

I am going to make an executive decision for this plot, 15 is too many
and makes it crazy cluttered.

### Repeat this with two sets of genes

```{r}
table_name <- "kh_p08_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Fign", "Nrn1", "Dpysl2", "Actb", "Fgf9", "Otx2", "Sec23",
                       "Ncam1", "Map4", "Sec22b", "Nlgn3", "Marcks", "Cd47",
                       "Dpysl3", "Lin7c", "Cadm1", "Snx12", "Rhoa", "Inpp5f",
                       "Atg12", "Set", "Gsk3b", "Pdcd4", "Gabra2", "Tmco1", "Anapc16")
kh_p08_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0,
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]])
pp(file = "images/kh_p08_scn_volcano.pdf", width = 9, height = 9)
kh_p08_scn_volcano[["plot"]]
plotted <- dev.off()
kh_p08_scn_volcano[["plot"]]
## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/kh_p08_scn_ma.pdf", width = 9, height = 9)
kh_p08_scn_ma[["plot"]]
plotted <- dev.off()
kh_p08_scn_ma[["plot"]]
```

#### Same plot but a different set of labeled genes

```{r}
table_name <- "kh_p08_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c(
  "Anapc16", "Gabra2", "Tmco1", "Sod2", "Fgf9", "Pdcd4", "Rhoa", "Gsk3b", "Foxp1",
  "Ncam1", "Marcks", "Fign", "Dpysl3", "Inpp5f", "Cadm1", "Map4", "Ugcg", "Elovl4",
  "Elavl1", "Cfl2", "Tnnt1", "Gnb1", "Impact", "Nrn1", "Nlgn3", "Actb", "Cd47",
  "Sec22b", "Slc17a7", "Vglut1", "Actb", "B4galt5", "Foxp1", "Otx2", "Lin7c",
  "Snx12", "Atg12", "Set")
kh_p08_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0)
pp(file = "images/kh_p08_scn_volcano_v2.pdf", width = 9, height = 9)
kh_p08_scn_volcano[["plot"]]
plotted <- dev.off()
kh_p08_scn_volcano[["plot"]]
## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/kh_p08_scn_ma_v2.pdf", width = 9, height = 9)
kh_p08_scn_ma[["plot"]]
plotted <- dev.off()
kh_p08_scn_ma[["plot"]]
```

#### ko/het p08 dLGN

```{r}
table_name <- "kh_p08_dlgn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
kh_p08_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
  label_column = "mgi_symbol", label = 10, size = 4, alpha = 1.0)
pp(file = "images/kh_p08_dlgn_volcano.pdf", width = 9, height = 9)
kh_p08_dlgn_volcano[["plot"]]
plotted <- dev.off()
kh_p08_dlgn_volcano[["plot"]]
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p08_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = 10)
pp(file = "images/kh_p08_dlgn_ma.pdf", width = 9, height = 9)
kh_p08_dlgn_ma[["plot"]]
plotted <- dev.off()
kh_p08_dlgn_ma[["plot"]]
```

#### ko/het for p15 retinas

For some crazy reason, this plot is double-labelling!

```{r}
table_name <- "kh_p15_retina"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting <- c("Opn4", "Gm9008", "Lrr1", "Cnbd1")
kh_p15_retina_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp", fill = "black",
  color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
  label_column = "mgi_symbol", label = interesting, alpha = 1.0,
  size = 4)
pp(file = "images/kh_p15_retina_volcano.pdf", width = 9, height = 9)
kh_p15_retina_volcano[["plot"]]
plotted <- dev.off()
kh_p15_retina_volcano[["plot"]]
## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_retina_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_retina"]], color_high = colors[["het_retina"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting)
pp(file = "images/kh_p15_retina_ma.pdf", width = 9, height = 9)
kh_p15_retina_ma[["plot"]]
plotted <- dev.off()
kh_p15_retina_ma[["plot"]]
```

#### ko/het p15 SCN

Holy crappers, this plot did _not_ double label; oooh I have a check
in my plotter to see if there are too few/too many labels and I
foolishly allowed it to concatenate the labels!  What in the crap was
I thinking?

I am going to make an executive decision for this plot, 15 is too many
and makes it crazy cluttered.

### Repeat this with two sets of genes

```{r}
table_name <- "kh_p15_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Fign", "Nrn1", "Dpysl2", "Actb", "Fgf9", "Otx2", "Sec23",
                       "Ncam1", "Map4", "Sec22b", "Nlgn3", "Marcks", "Cd47",
                       "Dpysl3", "Lin7c", "Cadm1", "Snx12", "Rhoa", "Inpp5f",
                       "Atg12", "Set", "Gsk3b", "Pdcd4", "Gabra2", "Tmco1", "Anapc16")
kh_p15_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label_column = "mgi_symbol", size = 4, alpha = 1.0,
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]])
pp(file = "images/kh_p15_scn_volcano.pdf", width = 9, height = 9)
kh_p15_scn_volcano[["plot"]]
plotted <- dev.off()
kh_p15_scn_volcano[["plot"]]
## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/kh_p15_scn_ma.pdf", width = 9, height = 9)
kh_p15_scn_ma[["plot"]]
plotted <- dev.off()
kh_p15_scn_ma[["plot"]]
```

```{r}
table_name <- "kh_p15_scn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
interesting_genes <- c(
  "Anapc16", "Gabra2", "Tmco1", "Sod2", "Fgf9", "Pdcd4", "Rhoa", "Gsk3b", "Foxp1",
  "Ncam1", "Marcks", "Fign", "Dpysl3", "Inpp5f", "Cadm1", "Map4", "Ugcg", "Elovl4",
  "Elavl1", "Cfl2", "Tnnt1", "Gnb1", "Impact", "Nrn1", "Nlgn3", "Actb", "Cd47",
  "Sec22b", "Slc17a7", "Vglut1", "Actb", "B4galt5", "Foxp1", "Otx2", "Lin7c",
  "Snx12", "Atg12", "Set")
kh_p15_scn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  label_column = "mgi_symbol", label = interesting_genes, size = 4, alpha = 1.0)
pp(file = "images/kh_p15_scn_volcano_v2.pdf", width = 9, height = 9)
kh_p15_scn_volcano[["plot"]]
plotted <- dev.off()
kh_p15_scn_volcano[["plot"]]
## why in the crap is it double-labelling!?
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_scn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["het_scn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/kh_p15_scn_ma_v2.pdf", width = 9, height = 9)
kh_p15_scn_ma[["plot"]]
plotted <- dev.off()
kh_p15_scn_ma[["plot"]]
```

#### ko/het p15 dLGN

```{r}
table_name <- "kh_p15_dlgn"
table_input <- genotype_tables[[table_name]]
table <- table_input[["data"]][[table_name]]
kh_p15_dlgn_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
  label_column = "mgi_symbol", label = 10, size = 4, alpha = 1.0)
pp(file = "images/kh_p15_dlgn_volcano.pdf", width = 9, height = 9)
kh_p15_dlgn_volcano[["plot"]]
plotted <- dev.off()
kh_p15_dlgn_volcano[["plot"]]
## My MA plotter isn't as smart as the volcano plotter, the genes are:
kh_p15_dlgn_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_dlgn"]], color_high = colors[["het_dlgn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = 10)
pp(file = "images/kh_p15_dlgn_ma.pdf", width = 9, height = 9)
kh_p15_dlgn_ma[["plot"]]
plotted <- dev.off()
kh_p15_dlgn_ma[["plot"]]
```

### Location contrasts with genes removed/kept

Repeat the same block with a find/replace of genotype/location.

```{r}
location_tables_full <- combine_de_tables(
  location_de, keepers = location_keepers, label_column = label_column,
  excel = glue("full_contrasts/location_full_tables-v{ver}.xlsx"))
location_tables_full
location_sig_full <- extract_significant_genes(
  location_tables_full, according_to = "deseq",
  excel = glue("full_contrasts/location_full_sig-v{ver}.xlsx"))
location_sig_full
location_full_upset <- upsetr_sig(location_sig_full)
##location_full_intersects <- write_upset_groups(
##  location_full_upset,
##  excel = "excel/location_full_gene_groups.xlsx")
location_tables <- list()
location_sig <- list()
location_gp <- list()
location_cp <- list()
for (k in seq_along(location_keepers)) {
  name <- names(location_keepers)[k]
  message("Examining ", name)
  keeper <- location_keepers[name]
  includes <- location_inclusions[[name]]
  include_name <- paste0("inc_", name)
  include_df_name <- paste0("df_", name)
  include_df <- location_inclusions[[include_df_name]]
  includes <- location_inclusions[[include_name]]
  summary(rownames(location_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
  include_filename <- glue("location_contrasts/location_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
  include_sig_filename <- glue("location_contrasts/location_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
  location_tables[[name]] <- combine_de_tables(
    location_de, extra_annot = include_df,
    keepers = keeper, label_column = label_column,
    excel = include_filename, wanted_genes = includes)
  print(location_tables[[name]])
  location_sig[[name]] <- extract_significant_genes(
    location_tables[[name]], according_to = "deseq",
    excel = include_sig_filename)
  print(location_sig[[name]])
  num_rows <- nrow(location_sig[[name]][["deseq"]][["ups"]][[name]]) +
    nrow(location_sig[[name]][["deseq"]][["downs"]][[name]])
  message("There are ", num_rows, " significant up and down genes.")
  if (num_rows > 10) {
    location_gp[[name]] <- all_gprofiler(location_sig[[name]], species = "mmusculus")
    gp_written <- write_all_gp(genotype_gp[[name]])
    location_cp[[name]] <- all_cprofiler(location_sig[[name]], location_tables[[name]],
                                         orgdb = "org.Mm.eg.db", organism = "mouse")
    cp_written <- write_all_cp(genotype_cp[[name]])
  }
}
```

Colenso sent a specific query of interest, comparing SCN vs. Retinas
at p08 in the heterozygotes including a set of genes of particular
interest.  Perhaps I can use some of these as markers to quality
control my work in the future?

Here are the genes:

Opn4, Eomes, Trpc7, Oprm1, Nr4a3, Tbx20, Irx6, AW551984, Pcdh19,
Adcyap1, Baiap3, Chl1, Grin3a, Igf1, Gria1, Grin2d, Grin3a, Chrna6,
Chrna3, Htr5a, Htr2a, Htr7, Irx4, PlxnC1, Sema6d, Sema4f, Sema4a,
Sema6b, Lrrc4b, Lrrc58, Lrrc3b, Wnt4, Wnt9b, Ctxn3, Tenm1, Gna14,
Rgs4, Rgs6, Rgs5

```{r}
table_input <- location_tables[["sr_p08_het"]]
table_name <- "sr_p08_het"
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Opn4", "Eomes", "Trpc7", "Oprm1", "Nr4a3", "Tbx20",
                       "Irx6", "AW551984", "Pcdh19", "Adcyap1r1", "Baiap3",
                       "Chl1", "Grin3a", "Igf1", "Gria1", "Grin2d", "Grin3a",
                       "Chrna6", "Chrna3", "Htr5a", "Htr2a", "Htr7", "Irx4",
                       "PlxnC1", "Sema6d", "Sema4f", "Sema4a", "Sema6b", "Lrrc4b",
                       "Lrrc58", "Lrrc3b", "Wnt4", "Wnt9b", "Ctxn3", "Tenm1", "Gna14",
                       "Rgs4", "Rgs6", "Rgs5", "Pou4f2", "Chrnb3", "Bcan")
sr_p08_het_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
  label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
  size = 4)
pp(file = "images/sr_p08_het_volcano.pdf", width = 9, height = 9)
sr_p08_het_volcano[["plot"]]
plotted <- dev.off()
sr_p08_het_volcano[["plot"]]
sr_p08_het_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/sr_p08_het_ma.pdf", width = 9, height = 9)
sr_p08_het_ma[["plot"]]
plotted <- dev.off()
sr_p08_het_ma[["plot"]]
```

### Also the ko

```{r}
table_input <- location_tables[["sr_p08_ko"]]
table_name <- "sr_p08_ko"
table <- table_input[["data"]][[table_name]]
sr_p08_ko_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
  label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
  size = 4)
pp(file = "images/sr_p08_ko_volcano.pdf", width = 9, height = 9)
sr_p08_ko_volcano[["plot"]]
plotted <- dev.off()
sr_p08_ko_volcano[["plot"]]
sr_p08_ko_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/sr_p08_ko_ma.pdf", width = 9, height = 9)
sr_p08_ko_ma[["plot"]]
plotted <- dev.off()
sr_p08_ko_ma[["plot"]]
```

```{r}
table_input <- location_tables[["sr_p15_het"]]
table_name <- "sr_p15_het"
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Opn4", "Eomes", "Trpc7", "Oprm1", "Nr4a3", "Tbx20",
                       "Irx6", "AW551984", "Pcdh19", "Adcyap1r1", "Baiap3",
                       "Chl1", "Grin3a", "Igf1", "Gria1", "Grin2d", "Grin3a",
                       "Chrna6", "Chrna3", "Htr5a", "Htr2a", "Htr7", "Irx4",
                       "PlxnC1", "Sema6d", "Sema4f", "Sema4a", "Sema6b", "Lrrc4b",
                       "Lrrc58", "Lrrc3b", "Wnt4", "Wnt9b", "Ctxn3", "Tenm1", "Gna14",
                       "Rgs4", "Rgs6", "Rgs5", "Pou4f2", "Chrnb3", "Bcan")
sr_p15_het_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["het_scn"]], color_high = colors[["het_retina"]],
  label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
  size = 4)
pp(file = "images/sr_p15_het_volcano.pdf", width = 9, height = 9)
sr_p15_het_volcano[["plot"]]
plotted <- dev.off()
sr_p15_het_volcano[["plot"]]
sr_p15_het_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["het_retina"]], color_high = colors[["het_scn"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/sr_p15_het_ma.pdf", width = 9, height = 9)
sr_p15_het_ma[["plot"]]
plotted <- dev.off()
sr_p15_het_ma[["plot"]]
```

### Also the ko

```{r}
table_input <- location_tables[["sr_p15_ko"]]
table_name <- "sr_p15_ko"
table <- table_input[["data"]][[table_name]]
sr_p15_ko_volcano <- plot_volcano_condition_de(
  table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
  color_low = colors[["ko_retina"]], color_high = colors[["ko_scn"]],
  label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
  size = 4, min.segment.length = 0, point.padding = 0.2)
pp(file = "images/sr_p15_ko_volcano.pdf", width = 12, height = 12)
sr_p15_ko_volcano[["plot"]]
plotted <- dev.off()
sr_p15_ko_volcano[["plot"]]
sr_p15_ko_ma <- plot_ma_condition_de(
  table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
  color_low = colors[["ko_scn"]], color_high = colors[["ko_retina"]],
  p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes)
pp(file = "images/sr_p15_ko_ma.pdf", width = 9, height = 9)
sr_p15_ko_ma[["plot"]]
plotted <- dev.off()
sr_p15_ko_ma[["plot"]]
```

#### Test a specific location query for duplicated IDs

Let us see if any Ensembl gene IDs and/or MGI IDs are shared in the
worksheet location_sr_p08_ko_including_wt_0.1_decreased_sig up/down.

```{r}
test_table_up <- location_sig[["sr_p08_ko"]][["deseq"]][["ups"]][[1]]
test_table_down <- location_sig[["sr_p08_ko"]][["deseq"]][["downs"]][[1]]

query <- list("up" = rownames(test_table_up),
              "down" = rownames(test_table_down))
query_upset <- UpSetR::fromList(query)
UpSetR::upset(query_upset)

query <- list("up" = test_table_up[["mgi_symbol"]],
              "down" = test_table_down[["mgi_symbol"]])
query_upset <- UpSetR::fromList(query)
UpSetR::upset(query_upset)
## ok, good.
```

### And time

```{r}
time_tables_full <- combine_de_tables(
  time_de, keepers = time_keepers,
  label_column = label_column,
  excel = glue("full_contrasts/time_full_tables-v{ver}.xlsx"))
time_sig_full <- extract_significant_genes(
  time_tables_full, according_to = "deseq",
  excel = glue("full_contrasts/time_full_sig-v{ver}.xlsx"))
time_tables <- list()
time_sig <- list()
time_gp <- list()
time_cp <- list()
for (k in seq_along(time_keepers)) {
  name <- names(time_keepers)[k]
  message("Examining ", name)
  keeper <- time_keepers[name]
  includes <- time_inclusions[[name]]
  include_name <- paste0("inc_", name)
  include_df_name <- paste0("df_", name)
  include_df <- time_inclusions[[include_df_name]]
  includes <- time_inclusions[[include_name]]
  summary(rownames(time_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
  include_filename <- glue("time_contrasts/time_{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
  include_sig_filename <- glue("time_contrasts/time_{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
  time_tables[[name]] <- combine_de_tables(
    time_de, extra_annot = include_df,
    keepers = keeper, label_column = label_column,
    excel = include_filename, wanted_genes = includes)
  print(time_tables[[name]])
  time_sig[[name]] <- extract_significant_genes(
    time_tables[[name]], according_to = "deseq",
    excel = include_filename)
  print(time_sig[[name]])
  num_rows <- nrow(time_sig[[name]][["deseq"]][["ups"]][[name]]) +
    nrow(time_sig[[name]][["deseq"]][["downs"]][[name]])
  message("There are ", num_rows, " significant up and down genes.")
  if (num_rows > 10) {
    time_gp[[name]] <- all_gprofiler(time_sig[[name]], species = "mmusculus")
    gp_written <- write_all_gp(time_gp[[name]])
    time_cp[[name]] <- all_cprofiler(time_sig[[name]], time_tables[[name]],
                                     orgdb = "org.Mm.eg.db")
    cp_written <- write_all_cp(time_cp[[name]])
  }
}
```

# Translatome queries

In conversation with Colenso, he spoke about a series of contrasts
which would be interesting to attempt in order to query the changes
across both locations and genotypes and/or both locations and time,
thus:

(p08_het_scn / p08_het_retina) / (p08_ko_scn / p08_ko_retina)

as an example.  We can definitely do these, but they do not work for
all methods employed (I think they work best with limma and edgeR).

Lets find out!

## Two scn/retina comparisons

* (p08_het_scn / p08_het_retina) / (p08_ko_scn / p08_ko_retina)
* (p15_het_scn / p15_het_retina) / (p15_ko_scn / p15_ko_retina)

```{r}
scn_extra <- glue("\\
  p08het = (conditionp08_het_scn - conditionp08_het_retina), \\
  p08ko = (conditionp08_ko_scn - conditionp08_ko_retina), \\
  p08het_vs_p08ko = (conditionp08_het_scn - conditionp08_het_retina) - (conditionp08_ko_scn - conditionp08_ko_retina), \\
  p15het = (conditionp15_het_scn - conditionp15_het_retina), \\
  p15ko = (conditionp15_ko_scn - conditionp15_ko_retina), \\
  p15het_vs_p15ko = (conditionp15_het_scn - conditionp15_het_retina) - (conditionp15_ko_scn - conditionp15_ko_retina)")
scn_translatome_de_keepers <- list(
  "p08het" = c("p08_het_scn", "p08_het_retina"),
  "p08ko" = c("p08_ko_scn", "p08_ko_retina"),
  "p15het" = c("p15_het_scn", "p15_het_retina"),
  "p15ko" = c("p15_ko_scn", "p15_ko_retina"))
scn_translatome_keepers <- list(
  "p08het" = c("p08_het_scn", "p08_het_retina"),
  "p08ko" = c("p08_ko_scn", "p08_ko_retina"),
  "p08_scn_translatome" = c("p08het", "p08ko"),
  "p15het" = c("p15_het_scn", "p15_het_retina"),
  "p15ko" = c("p15_ko_scn", "p15_ko_retina"),
  "p15_scn_translatome" = c("p15het", "p15ko"))
filt <- normalize(v3_pairwise_input, filter = TRUE)
limma_test <- limma_pairwise(filt,
                             keepers = scn_translatome_de_keepers,
                             model_fstring = "~ 0 + condition",
                             model_svs = FALSE, extra_contrastrs = scn_extra)
edger_test <- edger_pairwise(filt,
                             keepers = scn_translatome_de_keepers,
                             model_fstring = "~ 0 + condition",
                             model_svs = FALSE, extra_contrasts = scn_extra)
scn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                   keepers = scn_translatome_de_keepers,
                                   model_svs = FALSE,
                                   model_fstring = "~ 0 + condition",
                                   do_basic = FALSE, do_dream = FALSE,
                                   do_noiseq = FALSE, do_ebseq = FALSE,
                                   extra_contrasts = scn_extra)
scn_combined_test <- combine_de_tables(
  scn_translatome_de, keepers = scn_translatome_keepers,
  excel = glue("translatome/test_scn_translatome_unfiltered_nosva-v{ver}.xlsx"))
scn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                       keepers = scn_translatome_de_keepers,
                                       model_svs = "svaseq",
                                       model_fstring = "~ 0 + condition",
                                       do_basic = FALSE, do_dream = FALSE,
                                       do_noiseq = FALSE, do_ebseq = FALSE,
                                       extra_contrasts = scn_extra)
scn_combined_test_sva <- combine_de_tables(
  scn_translatome_de_sva, keepers = scn_translatome_keepers,
  excel = glue("translatome/test_scn_translatome_unfiltered_sva-v{ver}.xlsx"))
```

### Subtracting DESeq2 results: p08 scn het vs ko

```{r}
p08_scn_combined_deseq <- subtract_deseq_results(
  first_table = scn_combined_test[["data"]][["p08het"]],
  second_table = scn_combined_test[["data"]][["p08ko"]],
  first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
  first_p = "deseq_adjp", second_p = "deseq_adjp",
  first_name = "het", second_name = "ko",
  excel = glue("translatome/translatome_p08_scn_combined_deseq-v{ver}.xlsx"))
```

### Subtracting DESeq2 results: p15 scn het vs ko

```{r}
p15_scn_combined_deseq <- subtract_deseq_results(
  first_table = scn_combined_test[["data"]][["p15het"]],
  second_table = scn_combined_test[["data"]][["p15ko"]],
  first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
  first_p = "deseq_adjp", second_p = "deseq_adjp",
  first_name = "het", second_name = "ko",
  excel = glue("translatome/translatome_p15_scn_combined_deseq-v{ver}.xlsx"))
```

## One dlgn/retina comparison

* (p08_het_dlgn / p08_het_retina) / (p08_ko_dlgn / p08_ko_retina)

```{r}
p08_dlgn_extra <- "p08het_vs_p08ko = (conditionp08_het_dlgn - conditionp08_het_retina) - (conditionp08_ko_dlgn - conditionp08_ko_retina)"
p08_dlgn_translatome_de_keepers <- list(
  "p08het" = c("p08_het_dlgn", "p08_het_retina"),
  "p08ko" = c("p08_ko_dlgn", "p08_ko_retina"))
p08_dlgn_translatome_keepers <- list(
  "p08_het_dlgn_vs_retina" = c("p08_het_dlgn", "p08_het_retina"),
  "p08_ko_dlgn_vs_retina" = c("p08_ko_dlgn", "p08_ko_retina"),
  "p08_dlgn_translatome" = c("p08het", "p08ko"))
p08_dlgn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                        keepers = p08_dlgn_translatome_de_keepers,
                                        model_svs = FALSE,
                                        model_fstring = "~ 0 + condition",
                                        do_basic = FALSE, do_dream = FALSE,
                                        do_noiseq = FALSE, do_ebseq = FALSE,
                                        extra_contrasts = p08_dlgn_extra)
p08_dlgn_combined_test <- combine_de_tables(
  p08_dlgn_translatome_de, keepers = p08_dlgn_translatome_keepers,
  label_column = label_column,
  excel = glue("translatome/test_p08_dlgn_translatome_unfiltered_nosva-v{ver}.xlsx"))
p08_dlgn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                            keepers = p08_dlgn_translatome_de_keepers,
                                            model_svs = "svaseq",
                                            model_fstring = "~ 0 + condition",
                                            do_basic = FALSE, do_dream = FALSE,
                                            do_noiseq = FALSE, do_ebseq = FALSE,
                                            extra_contrasts = p08_dlgn_extra)
p08_dlgn_combined_test_sva <- combine_de_tables(
  p08_dlgn_translatome_de_sva, keepers = p08_dlgn_translatome_keepers,
  label_column = label_column,
  excel = glue("translatome/test_p08_dlgn_translatome_unfiltered_sva-v{ver}.xlsx"))
```

### Subtracting the DESeq2 results

## Two scn/retina comparisons (p15/p08 across het/ko)

* (p15_het_scn / p15_het_retina) / (p08_het_scn / p08_het_retina)
* (p15_ko_scn / p15_ko_retina) / (p08_ko_scn / p08_ko_retina)

```{r}
time_scn_extra <- glue("\\
  p15het = (conditionp15_het_scn - conditionp15_het_retina), \\
  p08het = (conditionp08_het_scn - conditionp08_het_retina), \\
  p15het_vs_p08het = (conditionp15_het_scn - conditionp15_het_retina) - (conditionp08_het_scn - conditionp08_het_retina),
  p15ko = (conditionp15_ko_scn - conditionp15_ko_retina), \\
  p08ko = (conditionp08_ko_scn - conditionp08_ko_retina), \\
  p15ko_vs_p08ko = (conditionp15_ko_scn - conditionp15_ko_retina) - (conditionp08_ko_scn - conditionp08_ko_retina)")
time_scn_translatome_de_keepers <- list(
  "p15het" = c("p15_het_scn", "p15_het_retina"),
  "p08het" = c("p08_het_scn", "p08_het_retina"),
  "p15ko" = c("p15_ko_scn", "p15_ko_retina"),
  "p08ko" = c("p08_ko_scn", "p08_ko_retina"))
time_scn_translatome_keepers <- list(
  "p15het" = c("p15_het_scn", "p15_het_retina"),
  "p08het" = c("p08_het_scn", "p08_het_retina"),
  "p15ko" = c("p15_ko_scn", "p15_ko_retina"),
  "p08ko" = c("p08_ko_scn", "p08_ko_retina"),
  "p15_het_sc_vs_retina" = c("p15_het_scn", "p15_het_retina"),
  "p08_het_sc_vs_retina" = c("p08_het_scn", "p08_het_retina"),
  "scn_het_translatome" = c("p15het", "p08het"),
  "scn_ko_translatome" = c("p15ko", "p08ko"))
time_scn_translatome_de <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                        keepers = time_scn_translatome_de_keepers,
                                        model_svs = FALSE,
                                        model_fstring = "~ 0 + condition",
                                        do_basic = FALSE, do_dream = FALSE,
                                        do_noiseq = FALSE, do_ebseq = FALSE,
                                        extra_contrasts = time_scn_extra)
time_scn_translatome_test <- combine_de_tables(
  time_scn_translatome_de,
  keepers = time_scn_translatome_keepers,
  label_column = label_column,
  excel = glue("translatome/test_time_scn_translatome_unfiltered_nosva-v{ver}.xlsx"))
time_scn_translatome_de_sva <- all_pairwise(v3_pairwise_input, filter = TRUE,
                                            keepers = time_scn_translatome_de_keepers,
                                            model_svs = "svaseq",
                                            model_fstring = "~ 0 + condition",
                                            do_basic = FALSE, do_dream = FALSE,
                                            do_noiseq = FALSE, do_ebseq = FALSE,
                                            extra_contrasts = time_scn_extra)
time_scn_translatome_test_sva <- combine_de_tables(
  time_scn_translatome_de_sva,
  keepers = time_scn_translatome_keepers,
  label_column = label_column,
  excel = glue("translatome/test_time_scn_translatome_unfiltered_sva-v{ver}.xlsx"))
```

Next step: Perform the retina filter; need to think about the proper
union/intersection of the retina/x expression values

In the previous block, we are making 2 global comparisons, here is one
of them:

(p15hetscn/p15hetret)/(p08hetscn/p08hetret)

I therefore want to extract the most logical set of genes higher in
some/all of these conditions with respect to the corresponding wt
conditions.  Previously, in section 'Extract genes included for each
set of contrasts', I attempted to perform this operation for 2
specific wt conditions.  When this was performed, it took the
unique(union) of the two sets.  Thus it stands to reason that I want
to take the unique(union) of all 4 in this instance?  e.g.:

(p15hetscn > p15wtscn) | (p15hetret > p15wtret) |
  (p08hetscn > p08wtscn) | (p08hetret > p08wtret)

I kind of think it should be:

((p15hetscn > p15wtscn) | (p15hetret > p15wtret)) &
  ((p08hetscn > p08wtscn) | (p08hetret > p08wtret))

gross, perhaps I should just do this manually, given that there are
only a few putative translatomes to query?

# Quick and dirty DESeq2 contrast of contrasts

In a fashion similar to how Hector handled the effect of phagocytosis
with Laura and Najib a long time ago, I propose to do a simple
subtraction of the results of our two contrasts which comprise the
translatome query (I was thinking about this last week, thus the
inclusion of them in the de tables above).  Similarly to the
phagocytosis effect, I will simply take the worst posible adjusted
p-value.  I will repeat this with limma/EdgeR and see how similar the
final results are to what those methods provide in the (a/b)/(c/d)
comparisons.  I am reasonably certain that DESeq2's results() function
has the ability to perform these odd contrasts, but I have never
figured out how; perhaps I will use this as a chance to revisit that...

Let us test this idea with the p08 dlgn query, which seeks to compare:

(p08_het_dlgn / p08_het_retina) / (p08_ko_dlgn / p08_ko_retina)

These are maintained in the de_table with the names
'p08_het_dlgn_vs_retina' and 'p08_ko_dlgn_vs_retina'

## p08 dlgn het vs ko

```{r}
p08_dlgn_combined_deseq <- subtract_deseq_results(
  first_table = p08_dlgn_combined_test[["data"]][["p08_het_dlgn_vs_retina"]],
  second_table = p08_dlgn_combined_test[["data"]][["p08_ko_dlgn_vs_retina"]],
  first_lfc = "deseq_logfc", second_lfc = "deseq_logfc",
  first_p = "deseq_adjp", second_p = "deseq_adjp",
  first_name = "het", second_name = "ko",
  excel = glue("translatome/translatome_p08_dlgn_combined_deseq-v{ver}.xlsx"))
```

See how similar these results are to those obtained from limma/edger.

```{r}
test_columns <- c("edger_logfc", "limma_logfc", "edger_adjp", "limma_adjp")
test_df <- p08_dlgn_combined_test[["data"]][["p08_dlgn_translatome"]][, test_columns]
test_df <- merge(test_df, p08_dlgn_combined_deseq, by = "row.names")
rownames(test_df) <- test_df[["Row.names"]]
test_df[["Row.names"]] <- NULL
cor.test(test_df[["limma_logfc"]], test_df[["het_vs_ko_logfc"]])
cor.test(test_df[["edger_logfc"]], test_df[["het_vs_ko_logfc"]])
tt <- plot_linear_scatter(test_df[, c("limma_logfc", "het_vs_ko_logfc")])
tt[["scatter"]]
tt <- plot_linear_scatter(test_df[, c("edger_adjp", "het_vs_ko_p")])
tt[["scatter"]]
## So, using the maximum p-value is a complete failure; but the extreme similarities
## between this and edgeR suggest to me that it is likely possible to use the results
## from edgeR without concern (or limma for that matter, it was also extremely similar)
## Or I can spend a little time and collect the numbers on each side of the division
## and calculate a t statistic myself.
```

# Non-Specific filtering of the translatome data

I have on hand

* p08het_vs_p08ko : (p08_het_scn - p08_het_retina) - (p08_ko_scn - p08_ko_retina)
* p15het_vs_p15ko : (p15_het_scn - p15_het_retina) - (p15_ko_scn - p15_ko_retina)
* p08het_vs_p08ko : (p08_het_dlgn - p08_het_retina) - (p08_ko_dlgn - p08_ko_retina)
* p15het_vs_p08het : (p15_het_scn - p15_het_retina) - (p08_het_scn - p08_het_retina)
* p15ko_vs_p08ko : (p15_ko_scn - p15_ko_retina) - (p08_ko_scn - p08_ko_retina)

I have gene sets up above which define the genes suitable for each of
these pieces.  There are only 5 comparisons, let us step through them.

## SCN translatome het/ko at p08

The data for this contrast resides in
scn_combined_test$data$p08_scn_translatome or the same slot of
scn_combined_test_sva

* p08_het_scn - p08_het_retina) - (p08_ko_scn - p08_ko_retina)

Thus, the inclusion_sig portions to extract are found in:
inclusion_sig[["deseq"]][["ups"]], and are named exactly as written above!

```{r}
p08_het_vs_ko_translatome_unfilt <- scn_combined_test[["data"]][["p08_scn_translatome"]]
num_union <- unique(c(rownames(inclusion_sig[["deseq"]][["ups"]][["p08_het_scn"]]),
                      rownames(inclusion_sig[["deseq"]][["ups"]][["p08_het_retina"]])))
length(num_union)
den_union <- unique(c(rownames(inclusion_sig[["deseq"]][["ups"]][["p08_ko_scn"]]),
                      rownames(inclusion_sig[["deseq"]][["ups"]][["p08_ko_retina"]])))
length(den_union)
both_union <- unique(c(num_union, den_union))
length(both_union)
both_inter_idx <- num_union %in% den_union
both_inter <- num_union[both_inter_idx]
length(both_inter)
keeper <- list("p08_scn_translatome" = c("p08het", "p08ko"))
p08_scn_translatome_union_filtered <- combine_de_tables(
  scn_translatome_de, keepers = keeper,
  label_column = label_column,
  excel = glue("translatome/p08_scn_translatome_union_filtered_nosva-v{ver}.xlsx"),
  wanted_genes = both_union)
p08_scn_translatome_inter_filtered <- combine_de_tables(
  scn_translatome_de, keepers = keeper,
  label_column = label_column,
  excel = glue("translatome/p08_scn_translatome_intersect_filtered_nosva-v{ver}.xlsx"),
  wanted_genes = both_inter)
p08_scn_translatome_union_filtered_sva <- combine_de_tables(
  scn_translatome_de_sva, keepers = keeper,
  label_column = label_column,
  excel = glue("translatome/p08_scn_translatome_union_filtered_sva-v{ver}.xlsx"),
  wanted_genes = both_union)
p08_scn_translatome_union_filtered <- combine_de_tables(
  scn_translatome_de, keepers = keeper,
  label_column = label_column,
  excel = glue("translatome/p08_scn_translatome_intersect_filtered_sva-v{ver}.xlsx"),
  wanted_genes = both_inter)
```

# Venn/UpSet of Retina, SCN, and dLGN DE Genes

Here is a snippet from Rashmi which expresses nicely the DE-result
comparisons she is most interested:

Since, I want to know the number of DEG expressed in Retina, SCN and
dLGN with respect to genotype, Location and time. I prepared the venn
diagram for these comparison:

* Genotype: P8 Ret Het vs KO, P15 Ret Het vs KO, P8 SCN Het vs KO,
            P15 SCN Het vs KO, P8 dLGN Het vs KO, P15 dLGN Het vs KO
* Location: P8_het Ret vs SCN, P8_KO Ret vs SCN, P15_het Ret vs SCN,
            P15_KO Ret vs SCN, P8_het Ret vs dLGN, P8_KO Ret vs dLGN,
            P15_het Ret vs dLGN, P15_KO Ret vs dLGN, P8_het SCN vs dLGN,
            P8_KO SCN vs dLGN, P15_het SCN vs dLGN, P15_KO SCN vs dLGN.

Since I was interested in understanding the change in local translatome
according to Location for different developmental time points for Het
and KO. Hence, I tried to generate a venn diagram for Location (Ret
and SCN) at developmental time points P8 and P15 for genotype het and
KO.  So the venn diagram / upset plot will be for location where some
genes will be shared/unique for P8_Ret_het, P8_SCN_Het, P15_Ret_HET,
P15_SCN_HET.  We can prepare an upset plot for P8_Ret_KO,
P8_SCN_KO, P15_Ret_KO and P15_SCN_KO also. Or can generate an upset
plot by combining both P8_Ret_het, P8_SCN_Het, P15_Ret_HET and
P15_SCN_HET and P8_Ret_KO, P8_SCN_KO, P15_Ret_KO and P15_SCN_KO.

Ok, let us see if I can implement this, starting with the genotype query

* Genotype: P8 Ret Het vs KO, P15 Ret Het vs KO, P8 SCN Het vs KO,
            P15 SCN Het vs KO, P8 dLGN Het vs KO, P15 dLGN Het vs KO

## ko vs het; all locations and times

```{r}
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("kh_p08_retina", "kh_p15_retina", "kh_p08_scn",
                 "kh_p15_scn", "kh_p08_dlgn", "kh_p15_dlgn")
table_names %in% names(genotype_sig)
newsig <- genotype_sig[[1]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- genotype_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- genotype_sig[[name]][["deseq"]][["downs"]][[name]]
}
genotype_upsetr <- upsetr_sig(newsig)
genotype_upset_written <- write_upset_groups(genotype_upsetr, excel = "excel/genotype_upset_groups.xlsx")
genotype_upsetr[["all_plot"]]
pp(file = "images/test_genotype_upset.pdf")
print(genotype_upsetr[["all_plot"]])
plotted <- dev.off()
```

Now let us try the location-specific comparisons

## scn vs retina, p08

```{r}
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("sr_p08_het", "sr_p08_ko")
table_names %in% names(location_sig)
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/sr_p08_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
pp(file = "images/test_location_sr_p08_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

I am reasonably certain that Rashmi would like a table of the genes
shared among increased scn ko and het in the above plot along with the
increased retina (e.g. the 269 and 103 gene sets).

## scn vs retina, p15

```{r}
table_names <- c("sr_p15_het", "sr_p15_ko")
table_names %in% names(location_sig)
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/sr_p15_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
print(scn_retina_p15_upset_result)
pp(file = "images/test_location_sr_p15_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

## dlgn vs retina, p08

```{r}
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("dr_p08_het", "dr_p08_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/dr_p08_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
pp(file = "images/test_location_dr_p08_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

## dlgn vs retina, p15

```{r}
## The appropriate data structure is 'genotype_tables',
## and the tables of interest are:
table_names <- c("dr_p15_het", "dr_p15_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/dr_p15_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
pp(file = "images/test_location_dr_p15_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

## dlgn vs scn, p08

```{r}
table_names <- c("ds_p08_het", "ds_p08_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/ds_p08_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
pp(file = "images/test_location_ds_p08_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

## dlgn vs scn, p15

```{r}
table_names <- c("ds_p15_het", "ds_p15_ko")
location_upset_input <- list()
first_table <- table_names[1]
newsig <- location_sig[[first_table]]
for (sig in 2:length(table_names)) {
  name <- table_names[sig]
  newsig[["deseq"]][["ups"]][[name]] <- location_sig[[name]][["deseq"]][["ups"]][[name]]
  newsig[["deseq"]][["downs"]][[name]] <- location_sig[[name]][["deseq"]][["downs"]][[name]]
}
location_upsetr <- upsetr_sig(newsig)
location_upset_written <- write_upset_groups(location_upsetr, excel = "excel/ds_p15_hetko_upset_groups.xlsx")
location_upsetr[["all_plot"]]
pp(file = "images/test_location_ds_p15_hetko_upset.pdf")
print(location_upsetr[["all_plot"]])
plotted <- dev.off()
```

# Shared and unique gene sets across x/wt

In this block I want to find the unique and shared genes between:

1.  scn p8 het/wt and retina p8 het/wt: hwp08scninc, hwp08retinc, hwp08scndec, hwp08retdec
2.  scn p15 het/wt and retina p15 het/wt: hwp15scninc, hwp15retinc, hwp15scndec, hwp15retdec
3.  #1 and #2 together:  8 catgories above
4.  scn p8 ko/wt and retina p8 ko/wt
5.  scn p15 ko/wt and retina p15 ko/wt
6.  #4 and #5 together

The comparisons of het/wt are found in the 'inclusion_sig' dataset;
because they are providing our cutoffs for nonspecific binding.

## Number 1 above: p08_het vs wt for scn and retina.

```{r}
table_names <- c("p08_het_scn", "p08_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p08_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

## Number 2 above: p15_het vs wt for scn and retina.

```{r}
table_names <- c("p15_het_scn", "p15_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p15_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p15_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

## Number 3 above: combination of #1 and #2

```{r}
table_names <- c("p08_het_scn", "p08_het_retina", "p15_het_scn", "p15_het_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08p15_het_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p08p15_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

## Number 4 above p08_ko vs wt for scn and retina.

```{r}
table_names <- c("p08_ko_scn", "p08_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p08_ko_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

## Number 5 above p15_ko vs wt for scn and retina.

```{r}
table_names <- c("p15_ko_scn", "p15_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p15_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p15_ko_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

## Number 6 above: Combining #4 and #5

```{r}
table_names <- c("p08_ko_scn", "p08_ko_retina", "p15_ko_scn", "p15_ko_retina")
inclusion_upsetr <- upsetr_sig(inclusion_sig, contrasts = table_names)
inclusion_upset_written <- write_upset_groups(inclusion_upsetr, excel = "excel/rs_p08p15_ko_inclusion_upset_groups.xlsx")
inclusion_upsetr[["all_plot"]]
pp(file = "images/inclusion_sr_p08p15_ko_upset.pdf")
print(inclusion_upsetr[["all_plot"]])
plotted <- dev.off()
```

# GSVA

```{r}
msigdb <- "reference/msigdb_v2024.1.Mm.db"
if (file.exists(msigdb)) {
  v3_h_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "mh",
                           signatures = msigdb, id_source = "fdata",
                           required_id = "mgi_symbol")
  v3_h_gsva

  v3_h_gsva_sig <- get_sig_gsva_categories(
    v3_h_gsva, excel = "excel/gsva_sig_hallmark_categories.xlsx")
  v3_h_gsva_sig

  v3_m1_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m1",
                            signatures = msigdb, id_source = "fdata",
                            required_id = "mgi_symbol")
  v3_m1_gsva
  v3_m1_gsva_sig <- get_sig_gsva_categories(
    v3_m1_gsva, excel = "excel/gsva_sig_positional_categories.xlsx")
  v3_m1_gsva_sig

  v3_m2_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m2",
                            signatures = msigdb, id_source = "fdata",
                            required_id = "mgi_symbol")
  v3_m2_gsva
  v3_m2_gsva_sig <- get_sig_gsva_categories(
    v3_m2_gsva, excel = "excel/gsva_sig_curated_categories.xlsx")
  v3_m2_gsva_sig

  v3_m3_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m3",
                            signatures = msigdb, id_source = "fdata",
                            required_id = "mgi_symbol")
  v3_m3_gsva
  v3_m3_gsva_sig <- get_sig_gsva_categories(
    v3_m3_gsva, excel = "excel/gsva_sig_regulatory_categories.xlsx")
  v3_m3_gsva_sig

  v3_m5_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m5",
                            signatures = msigdb, id_source = "fdata",
                            required_id = "mgi_symbol")
  v3_m5_gsva
  v3_m5_gsva_sig <- get_sig_gsva_categories(
    v3_m5_gsva, excel = "excel/gsva_sig_ontology_categories.xlsx")
  v3_m5_gsva_sig

  v3_m8_gsva <- simple_gsva(v3_pairwise_input, orgdb = "org.Mm.eg.db", signature_category = "m8",
                            signatures = msigdb, id_source = "fdata",
                            required_id = "mgi_symbol")
  v3_m8_gsva
  v3_m8_gsva_sig <- get_sig_gsva_categories(
    v3_m8_gsva, excel = "excel/gsva_sig_celltype_categories.xlsx")
  v3_m8_gsva_sig
}
```

# GSEA images

Up above I created a fairly large set of enrichment/GSEA analyses.
Let us pull some of the most interesting results here and look at
them.

Here are the specific queries from Rashmi:

* Genotype (het vs ko):
  * P8 het and ko for Ret
  * SCN (P8 het vs KO SCN
  * P8 het vs KO Ret)
  * P15 het and ko for Ret
* Location (somal vs axonal):
  * SR_P08_KO
  * SR_P08_Het
  * SR_P15_KO
  * SR_P15_Het
* Time(p8vs p15):
  * t_het_Ret_ po8-p15
  * t_KO_Ret_C po8-p15
  * t_het_SCN_po8-p15
  * t_KO_SCN_po8-p15

## Genotype

Let us take a moment and see for which contrasts I acquired results:

I need to make a little summary for clusterprofiler too so that I can easily see how many hits there
are for each contrast.

```{r}
summary(genotype_full_gp)
for (i in names(genotype_full_gp)) {
  print(i)
  print(genotype_full_gp[[i]][["num_hits"]])
}
summary(genotype_full_cp)
for (i in names(genotype_full_cp)) {
  print(i)
  print(nrow(genotype_full_cp[[i]][["gse_go"]]))
}
```

### p8 het/ko for retina:

This contrast, even before filtering away the high-wt genes, only has 8 genes in the set of up and
down genes combined.  As a result, my function which performs gProfiler/clusterProfiler skips it,
and also skips the p15 het/ko for retina samples.

### p8 het/ko for scn:

This has a bunch more genes: 51 up and 128 down.
Unfortunately, gProfiler sees no significant over-representation in the up category of genes.
The down category has

The up/down sets from clusterProfiler have enrich_go, gse_go, and enrich_objects to look at.

```{r}
genotype_full_gp$kh_p08_scn_up$num_hits
genotype_full_gp$kh_p08_scn_down$num_hits

plots <- plot_enrichresult(genotype_full_gp$kh_p08_scn_down[["BP_enrich"]])
plots[["dot"]]
plots[["tree"]]
```

Perhaps I should just ask the question: for which categories did I get results back?

```{r}
summary(genotype_full_gp)
```

kh_p08_dlgn_up: No significant gProfiler results.
kh_p15_dlgn_up: Significant BP, HP, KEGG, MF, REAC, TF
kh_p08_scn_up: No significant gProfiler results.
kh_p08_scn_down: Significant BP, MiRNA, MF, TF
kh_p15_scn_down: Significant BP, MF


```{r}
plots <- plot_enrichresult(genotype_full_gp[["kh_p15_dlgn_up"]][["BP_enrich"]])
plots[["dot"]]
```

## Location

### Scn vs retina ko, p08

```{r}

plots <- plot_enrichresult(location_gp[["sr_p08_ko"]][["sr_p08_ko_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(location_gp[["sr_p08_ko"]][["sr_p08_ko_down"]][["BP_enrich"]])
plots[["dot"]]
```

### scn vs retina, het, p08

Enriched groups: BP, KEGG, MF, TF, CC

```{r}
summary(location_gp[["sr_p08_het"]][["sr_p08_het_up"]])
plots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_up"]][["CC_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(location_gp[["sr_p08_het"]][["sr_p08_het_down"]][["BP_enrich"]])
plots[["dot"]]

sr_p08_het_topn_gsea <- plot_topn_gsea(location_cp[[""]])

```

### Scn vs retina ko, p15

```{r}
plots <- plot_enrichresult(location_gp[["sr_p15_ko"]][["sr_p15_ko_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(location_gp[["sr_p15_ko"]][["sr_p15_ko_down"]][["BP_enrich"]])
plots[["dot"]]
```

### scn vs retina, het, p15

```{r}
plots <- plot_enrichresult(location_gp[["sr_p15_het"]][["sr_p15_het_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(location_gp[["sr_p15_het"]][["sr_p15_het_down"]][["BP_enrich"]])
plots[["dot"]]
```

## Time

### het retina

Ups: significant results for BP, MF, TF
Downs: BP, MF, REAC, TF, WP

```{r}
plots <- plot_enrichresult(time_gp[["t_het_retina"]][["t_het_retina_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(time_gp[["t_het_retina"]][["t_het_retina_down"]][["BP_enrich"]])
plots[["dot"]]
```

### ko retina

Up: BP, MiRNA, MF
Down: BP, MF, REAC, TF

```{r}
plots <- plot_enrichresult(time_gp[["t_ko_retina"]][["t_ko_retina_up"]][["BP_enrich"]])
plots[["dot"]]

plots <- plot_enrichresult(time_gp[["t_ko_retina"]][["t_ko_retina_down"]][["BP_enrich"]])
plots[["dot"]]
```

### het scn

Neither of the SCN gProfiler queries provided any results.

# Bibliography

```{r saveme, eval=FALSE}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename = savefile))
```

```{r loadme_after, eval=FALSE}
tmp <- loadme(filename = savefile)
```
