TMRC2 202405: Visualizing Analyses following differential expression

1 Introduction

This is intended to contain analyses which logically follow our transciptomic analyses. It is therefore a bit of a grab bag, it may eventually comprise the variant search; but currently that is intertwined with the DE results. As a result, this and the ‘pre_visualization’ document are currently very redundant.

1.1 Creating trees of important strains

I have a few methods of creating (phylogenetic) trees describing the relationship of the strains of interest in this data.

• Calculate matrices of the variants in the data and use tools like euclidean distance and hclust to calculate the degree of similarity.
• Create a kmer index of each genome and use ape to calculate distance metrics and neighbor joining trees describing them.
• Extract CDS sequences of known interest (likely zymodeme genes, since they are stuff like GP6/GAPDH) and perform a MSA->tree operation.

my_genes <- c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
              "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300",
              "other")
my_names <- c("ALAT", "ASAT", "G6PD", "NHv1", "NHv2", "MPI", "other")

2 kmer based comparison

The following block was generated via the following methods:

The hisat2-based alignments were passed to freebayes2[ref], which generated a set of high-confidence transcriptome-based vcf files for each sample of sufficient coverage. These were sorted, compressed, and indexed via bcftools[ref]; and high-confidence variants (more than 80% with coverage higher than 5 reads per position) were extracted into a table variants as well as used to modify the reference genome to represent these filtered variants. These modified genomes were passed to ape[ref], indexed, and used to create a kmer index and distance matrix; these distances were used by ape to create a neighbor joining tree.

2.1 Simplified version of the above

I wrote a little function which in theory should make the above a bit simpler and more robust for future analyses. Lets see if it works. It currently takes a directory containing the fasta files of the sequences to compare and an optional root.

The following block requires copying a group of fasta files into the ‘compare_strains/’ directory, which in our container image is not available, so turning this off.

strain_tree <- genomic_sequence_phylo("compare_strains")
plot(strain_tree$phylo)

3 Phylogenetic analysis of genes of interest

In order to perform this, I will use the same fasta files, but extract the zymodeme genes from them and write out a set of fasta files containing their sequences. I therefore wrote a function which takes in the annotation data and fasta files in order to extract the data of interest.

Sadly, I will need to read in the annotations for braziliensis/panamensis panama and any other sequences. But the sequences which are directly extracted from panamensis colombia I will be able to use the same annotations.

wanted_ids <- c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
                "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300")
reference <- write_cds_entries("compare_strains/lpanamensis_v36.fasta", all_lp_annot,
                               ids = wanted_ids, output = "compare_strains/lpanamensis_cds.fasta")
modified_12588 <- write_cds_entries("compare_strains/strain_12588_modified_z21.fasta", all_lp_annot,
                                    name_prefix = "z21", ids = wanted_ids, output = "compare_strains/lpanamensis_z21_cds.fasta")
modified_2272 <- write_cds_entries("compare_strains/strain_2272_modified_z22.fasta", all_lp_annot,
                                   name_prefix = "z22", ids = wanted_ids, output = "compare_strains/lpanamensis_z22_cds.fasta")
modified_2168 <- write_cds_entries("compare_strains/strain_2168_modified_z23.fasta", all_lp_annot,
                                   name_prefix = "z23", ids = wanted_ids, output = "compare_strains/lpanamensis_z23_cds.fasta")
modified_12444 <- write_cds_entries("compare_strains/strain_12444_modified_z24.fasta", all_lp_annot,
                                    name_prefix = "z24", ids = wanted_ids, output = "compare_strains/lpanamensis_z24_cds.fasta")

Having written these files, I concatenated the zymodeme CDS sequences into individual sequences/strain and performed a MSA and MP tree using clustalo[ref] and PhyML[ref] via seaview[ref]. Sadly, there were only 12 informative sites in the 6 zymodeme defining genes. Happily, the tree generated looks pretty much exactly like my genome-based tree. Also, I didn’t bother to add the other genomes because with only 12 variant positions it did not feel interesting.

PhyML based tree of the zymodeme sequences

4 SNP profiles

Over the last couple of weeks, I redid all the variant searches with a newer, (I think) more sensitive and more specific variant tool. In addition I changed my script which interprets the results so that it is able to extract any tags from it, instead of just the one or two that my previous script handled. In addition, at least in theory it is now able to provide the set of amino acid substitutions for every gene in species without or with introns (not really relevant for Leishmania panamensis).

both_norm <- set_expt_conditions(both_snps, fact = "knnv2classification")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'both_snps' not found

## strains <- both_norm[["design"]][["strain"]]
both_strain <- set_expt_conditions(both_norm, fact = "strain")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'both_norm' not found

The data structure ‘both_norm’ now contains our 2016 data along with the newer data collected since 2019.

4.1 Plot of SNP profiles for zymodemes

The following plot shows the SNP profiles of all samples (old and new) where the colors at the top show either the 2.2 strains (orange), 2.3 strains (green), the previous samples (purple), or the various lab strains (pink etc).

new_variant_heatmap <- plot_corheat(new_snps)
dev <- pp(file = "images/raw_snp_corheat.png", height = 12, width = 12)
new_variant_heatmap$plot
closed <- dev.off()
new_variant_heatmap$plot

The function get_snp_sets() takes the provided metadata factor (in this case ‘condition’) and looks for variants which are exclusive to each element in it. In this case, this is looking for differences between 2.2 and 2.3, as well as the set shared among them.

snp_sets <- get_snp_sets(both_snps, factor = "knnhclusttogethercall")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'both_snps' not found

snp_sets

## Error in eval(expr, envir, enclos): object 'snp_sets' not found

Biobase::annotation(lp_previous$expressionset) <- Biobase::annotation(lp_expt$expressionset)

## Error: object 'lp_previous' not found

lp_knn <- set_expt_conditions(lp_expt, fact = "knnhclusttogethercall")

## The numbers of samples by condition are:

## 
## null  z21  z22  z23 
##    3    9   37   42

both_expt <- combine_expts(lp_knn, lp_previous)

## Error in eval(expr, envir, enclos): object 'lp_previous' not found

snp_genes <- snps_vs_genes(both_expt, snp_sets, expt_name_col = "chromosome")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'both_expt' not found

## I think we have some metrics here we can plot...s
snp_subset <- snp_subset_genes(
  both_expt, both_snps,
  genes = c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
            "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300"))

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'both_expt' not found

zymo_heat <- plot_sample_heatmap(
  normalize_expt(snp_subset, transform = "log2"),
  row_label = rownames(exprs(snp_subset)))

## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_sample_heatmap': error in evaluating the argument 'expt' in selecting a method for function 'normalize_expt': object 'snp_subset' not found

zymo_heat

## Error in eval(expr, envir, enclos): object 'zymo_heat' not found

most_variant <- head(snp_genes$count_by_gene, n = 100)

## Error in eval(expr, envir, enclos): object 'snp_genes' not found

least_variant <- tail(snp_genes$count_by_gene, n = 100)

## Error in eval(expr, envir, enclos): object 'snp_genes' not found

test <- simple_goseq(names(most_variant), go_db = lp_go, length_db = lp_lengths)

## Error in eval(expr, envir, enclos): object 'most_variant' not found

4.2 Compare variants to DE genes

Najib has asked a few times about the relationship between variants and DE genes. In subsequent conversations I figured out what he really wants to learn is variants in the UTR (most likely 5’) which might affect expression of genes. The following explicitly does not help this question, but is a paralog: is there a relationship between variants in the CDS and differential expression?

4.2.1 Collect DE data

In order to do this comparison, we need to reload some of the DE results.

rda <- glue("rda/zymo_tables_nobatch-v{ver}.rda")
varname <- gsub(x = basename(rda), pattern = "\\.rda", replacement = "")
loaded <- load(file = rda)
zy_df <- get0(varname)[["data"]][["zymodeme"]]

rda <- glue("rda/sus_tables_nobatch-v{ver}.rda")
varname <- gsub(x = basename(rda), pattern = "\\.rda", replacement = "")
loaded <- load(file = rda)

## Warning in readChar(con, 5L, useBytes = TRUE): cannot open compressed file
## 'rda/sus_tables_nobatch-v202405.rda', probable reason 'No such file or
## directory'

## Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection

sus_df <- get0(varname)[["data"]][["resistant_sensitive"]]

vars_df <- data.frame(ID = names(snp_genes[["count_by_gene"]]),
                      variants = as.numeric(snp_genes[["count_by_gene"]]))

## Error in eval(expr, envir, enclos): object 'snp_genes' not found

vars_df <- merge(vars_df, lp_lengths, by = "ID")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'vars_df' not found

vars_df[["length"]] <- as.numeric(vars_df[["length"]])

## Error in eval(expr, envir, enclos): object 'vars_df' not found

vars_df[["var_len"]] <- vars_df[["variants"]] / vars_df[["length"]]

## Error in eval(expr, envir, enclos): object 'vars_df' not found

vars_by_de_gene <- merge(zy_df, vars_df, by.x="row.names", by.y="ID")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'y' in selecting a method for function 'merge': object 'vars_df' not found

rownames(vars_by_de_gene) <- vars_by_de_gene[["Row.names"]]

## Error in eval(expr, envir, enclos): object 'vars_by_de_gene' not found

vars_by_de_gene[["Row.names"]] <- NULL

## Error: object 'vars_by_de_gene' not found

cor.test(vars_by_de_gene$deseq_logfc, vars_by_de_gene$var_len)

## Error in eval(expr, envir, enclos): object 'vars_by_de_gene' not found

variants_wrt_logfc <- plot_linear_scatter(vars_by_de_gene, xcol = "deseq_logfc",
                                          ycol = "var_len", text_col = "annotgeneproduct")

## Error in eval(expr, envir, enclos) : object 'vars_by_de_gene' not found

## Warning in is.data.frame(x): restarting interrupted promise evaluation

## Error in eval(expr, envir, enclos): object 'vars_by_de_gene' not found

scatter <- variants_wrt_logfc$scatter

## Error in eval(expr, envir, enclos): object 'variants_wrt_logfc' not found

plotly::ggplotly(scatter, tooltip = c("x", "y", "text"))

## Error in eval(expr, envir, enclos): object 'scatter' not found

## It looks like there might be some genes of interest, even though this is not actually
## the question of interest.

Ok, I think I can do this on a UTR basis.

snp_utrs <- snps_vs_genes_padded(both_expt, snp_sets, expt_name_col = "chromosome")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'both_expt' not found

fivep_vars_df <- data.frame(ID = names(snp_utrs[["count_fivep_by_gene"]]),
                            variants = as.numeric(snp_utrs[["count_fivep_by_gene"]]))

## Error in eval(expr, envir, enclos): object 'snp_utrs' not found

fivep_vars_by_de_gene <- merge(zy_df, fivep_vars_df, by.x="row.names", by.y="ID")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'y' in selecting a method for function 'merge': object 'fivep_vars_df' not found

rownames(fivep_vars_by_de_gene) <- fivep_vars_by_de_gene[["Row.names"]]

## Error in eval(expr, envir, enclos): object 'fivep_vars_by_de_gene' not found

fivep_vars_by_de_gene[["Row.names"]] <- NULL

## Error: object 'fivep_vars_by_de_gene' not found

cor.test(fivep_vars_by_de_gene$deseq_logfc, fivep_vars_by_de_gene[["variants"]])

## Error in eval(expr, envir, enclos): object 'fivep_vars_by_de_gene' not found

fivep_variants_wrt_logfc <- plot_linear_scatter(fivep_vars_by_de_gene, xcol = "deseq_logfc",
                                                ycol = "variants", text_col = "annotgeneproduct")

## Error in eval(expr, envir, enclos) : 
##   object 'fivep_vars_by_de_gene' not found

## Warning in is.data.frame(x): restarting interrupted promise evaluation

## Error in eval(expr, envir, enclos): object 'fivep_vars_by_de_gene' not found

scatter <- fivep_variants_wrt_logfc$scatter

## Error in eval(expr, envir, enclos): object 'fivep_variants_wrt_logfc' not found

scatter

## Error in eval(expr, envir, enclos): object 'scatter' not found

plotly::ggplotly(scatter, tooltip = c("x", "y", "text"))

## Error in eval(expr, envir, enclos): object 'scatter' not found

threep_vars_df <- data.frame(ID = names(snp_utrs[["count_threep_by_gene"]]),
                            variants = as.numeric(snp_utrs[["count_threep_by_gene"]]))

## Error in eval(expr, envir, enclos): object 'snp_utrs' not found

threep_vars_by_de_gene <- merge(zy_df, threep_vars_df, by.x="row.names", by.y="ID")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'y' in selecting a method for function 'merge': object 'threep_vars_df' not found

rownames(threep_vars_by_de_gene) <- threep_vars_by_de_gene[["Row.names"]]

## Error in eval(expr, envir, enclos): object 'threep_vars_by_de_gene' not found

threep_vars_by_de_gene[["Row.names"]] <- NULL

## Error: object 'threep_vars_by_de_gene' not found

cor.test(threep_vars_by_de_gene$deseq_logfc, threep_vars_by_de_gene[["variants"]])

## Error in eval(expr, envir, enclos): object 'threep_vars_by_de_gene' not found

threep_variants_wrt_logfc <- plot_linear_scatter(threep_vars_by_de_gene, xcol = "deseq_logfc",
                                                ycol = "variants", text_col = "annotgeneproduct")

## Error in eval(expr, envir, enclos) : 
##   object 'threep_vars_by_de_gene' not found

## Warning in is.data.frame(x): restarting interrupted promise evaluation

## Error in eval(expr, envir, enclos): object 'threep_vars_by_de_gene' not found

scatter <- threep_variants_wrt_logfc$scatter

## Error in eval(expr, envir, enclos): object 'threep_variants_wrt_logfc' not found

plotly::ggplotly(scatter, tooltip = c("x", "y", "text"))

## Error in eval(expr, envir, enclos): object 'scatter' not found

Didn’t I create a set of densities by chromosome? Oh I think they come in from get_snp_sets()

4.3 SNPS associated with clinical response in the TMRC samples

clinical_sets <- get_snp_sets(new_snps, factor = "clinicalresponse")

## The samples represent the following categories:

## 
##    cure failure      nd 
##      39      34      18

## Using a proportion of observed variants, converting the data to binary observations.

## The factor cure has 39 rows.

## The factor failure has 34 rows.

## The factor nd has 18 rows.

## Finished iterating over the chromosomes.

clinical_sets

## A set of variants observed when cross referencing all variants against
## the samples associated with each metadata factor: clinicalresponse.  3
## categories and 943592 variants were observed with 7
## combinations among them.  725 chromosomes/scaffolds were observed with a
## density of variants ranging from 0.000652315720808871 to 0.115079365079365.

density_vec <- clinical_sets[["density"]]
chromosome_idx <- grep(pattern = "LpaL", x = names(density_vec))
density_df <- as.data.frame(density_vec[chromosome_idx])
density_df[["chr"]] <- rownames(density_df)
colnames(density_df) <- c("density_vec", "chr")
ggplot(density_df, aes_string(x = "chr", y = "density_vec")) +
  ggplot2::geom_col() +
  ggplot2::theme(axis.text = ggplot2::element_text(size = 10, colour = "black"),
                 axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5))

## 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.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## clinical_written <- write_variants(new_snps)

4.3.1 Cross reference these variants by gene

clinical_genes <- snps_vs_genes(lp_expt, clinical_sets, expt_name_col = "chromosome")

## The snp grange data has 943592 elements.

## There are 437801 overlapping variants and genes.

clinical_genes

## When the variants observed were cross referenced against annotated genes,
## 8627 genes were observed with at least 1 variant.
## LPAL13_250017600 had the most variants, with 893.

snp_density <- merge(as.data.frame(clinical_genes[["count_by_gene"]]),
                     as.data.frame(fData(lp_expt)),
                     by = "row.names")
snp_density <- snp_density[, c(1, 2, 4, 15)]
colnames(snp_density) <- c("name", "snps", "product", "length")
snp_density[["product"]] <- tolower(snp_density[["product"]])
snp_density[["length"]] <- as.numeric(snp_density[["length"]])
snp_density[["density"]] <- snp_density[["snps"]] / snp_density[["length"]]
snp_idx <- order(snp_density[["density"]], decreasing = TRUE)
snp_density <- snp_density[snp_idx, ]

removers <- c("amastin", "gp63", "leishmanolysin")
for (r in removers) {
  drop_idx <- grepl(pattern = r, x = snp_density[["product"]])
  snp_density <- snp_density[!drop_idx, ]
}
## Filter these for [A|a]mastin gp63 Leishmanolysin

clinical_snps <- snps_intersections(lp_expt, clinical_sets, chr_column = "chromosome")

fail_ref_snps <- as.data.frame(clinical_snps[["inters"]][["failure, reference strain"]])
fail_ref_snps <- rbind(fail_ref_snps,
                       as.data.frame(clinical_snps[["inters"]][["failure"]]))
cure_snps <- as.data.frame(clinical_snps[["inters"]][["cure"]])

head(fail_ref_snps)

##                                                     seqnames  start    end
## chr_LPAL13-SCAF000063_pos_2573_ref_C_alt_A LPAL13-SCAF000063   2573   2574
## chr_LPAL13-SCAF000165_pos_363_ref_G_alt_C  LPAL13-SCAF000165    363    364
## chr_LPAL13-SCAF000627_pos_2267_ref_G_alt_T LPAL13-SCAF000627   2267   2268
## chr_LpaL13-01_pos_26758_ref_T_alt_C                LpaL13-01  26758  26759
## chr_LpaL13-03_pos_164108_ref_A_alt_G               LpaL13-03 164108 164109
## chr_LpaL13-03_pos_236263_ref_T_alt_C               LpaL13-03 236263 236264
##                                            width strand
## chr_LPAL13-SCAF000063_pos_2573_ref_C_alt_A     2      +
## chr_LPAL13-SCAF000165_pos_363_ref_G_alt_C      2      +
## chr_LPAL13-SCAF000627_pos_2267_ref_G_alt_T     2      +
## chr_LpaL13-01_pos_26758_ref_T_alt_C            2      +
## chr_LpaL13-03_pos_164108_ref_A_alt_G           2      +
## chr_LpaL13-03_pos_236263_ref_T_alt_C           2      +

head(cure_snps)

##                                                     seqnames  start    end
## chr_LPAL13-SCAF000627_pos_1869_ref_A_alt_G LPAL13-SCAF000627   1869   1870
## chr_LpaL13-06_pos_2975_ref_T_alt_C                 LpaL13-06   2975   2976
## chr_LpaL13-09_pos_58219_ref_G_alt_A                LpaL13-09  58219  58220
## chr_LpaL13-12_pos_194617_ref_C_alt_T               LpaL13-12 194617 194618
## chr_LpaL13-13_pos_19037_ref_A_alt_G                LpaL13-13  19037  19038
## chr_LpaL13-13_pos_337854_ref_T_alt_C               LpaL13-13 337854 337855
##                                            width strand
## chr_LPAL13-SCAF000627_pos_1869_ref_A_alt_G     2      +
## chr_LpaL13-06_pos_2975_ref_T_alt_C             2      +
## chr_LpaL13-09_pos_58219_ref_G_alt_A            2      +
## chr_LpaL13-12_pos_194617_ref_C_alt_T           2      +
## chr_LpaL13-13_pos_19037_ref_A_alt_G            2      +
## chr_LpaL13-13_pos_337854_ref_T_alt_C           2      +

write.csv(file="csv/cure_variants.txt", x=rownames(cure_snps))

## Warning in file(file, ifelse(append, "a", "w")): cannot open file
## 'csv/cure_variants.txt': No such file or directory

## Error in file(file, ifelse(append, "a", "w")): cannot open the connection

write.csv(file="csv/fail_variants.txt", x=rownames(fail_ref_snps))

## Warning in file(file, ifelse(append, "a", "w")): cannot open file
## 'csv/fail_variants.txt': No such file or directory

## Error in file(file, ifelse(append, "a", "w")): cannot open the connection

annot <- fData(lp_expt)
clinical_interest <- as.data.frame(clinical_snps[["gene_summaries"]][["cure"]])
clinical_interest <- merge(clinical_interest,
                           as.data.frame(clinical_snps[["gene_summaries"]][["failure, reference strain"]]),
                           by = "row.names")
rownames(clinical_interest) <- clinical_interest[["Row.names"]]
clinical_interest[["Row.names"]] <- NULL
colnames(clinical_interest) <- c("cure_snps","fail_snps")

## Error in names(x) <- value: 'names' attribute [2] must be the same length as the vector [1]

annot <- merge(annot, clinical_interest, by = "row.names")
rownames(annot) <- annot[["Row.names"]]
annot[["Row.names"]] <- NULL
fData(lp_expt$expressionset) <- annot

5 Zymodeme for new samples

The heatmap produced here should show the variants only for the zymodeme genes.

5.1 Hunt for snp clusters

I am thinking that if we find clusters of locations which are variant, that might provide some PCR testing possibilities.

## Drop the 2.1, 2.4, unknown, and null
knn_snps <- set_expt_conditions(new_snps, fact = "knnv2classification") %>%
  subset_expt(subset="condition=='z22'|condition=='z23'")
knn_sets <- get_snp_sets(knn_snps, factor = "knnv2classification")
knn_sets
summary(knn_sets)
## 1000000: 2.2
## 0100000: 2.3

pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
new_sets <- get_snp_sets(pruned_snps, factor = "condition")
new_sets

summary(new_sets[["intersections"]][["10"]])
write.csv(file="csv/variants_22.csv", x=new_sets[["intersections"]][["10"]])
summary(new_sets[["intersections"]][["01"]])
write.csv(file="csv/variants_23.csv", x=new_sets[["intersections"]][["01"]])

summary(knn_sets[["intersections"]][["10"]])
write.csv(file="csv/knn_variants_22.csv", x=new_sets[["intersections"]][["10"]])
summary(knn_sets[["intersections"]][["01"]])
write.csv(file="csv/knn_variants_23.csv", x=new_sets[["intersections"]][["01"]])

Thus we see that there are 601 variants associated with 2.2 and 67,171 associated with 2.3.

5.1.1 Search for PCR primers

The sequential_variants() function searches for variants which are clustered close together in the hopes that choosing PCR primers focused on this positions will(not) anneal and may be used as a quick way to identify strains.

The current set of strains

zymo22_sequentials <- sequential_variants(new_sets, conditions = "z2.2",
                                          minimum = 1, maximum_separation = 2)

## Error in eval(expr, envir, enclos): object 'new_sets' not found

dim(zymo22_sequentials)

## Error in eval(expr, envir, enclos): object 'zymo22_sequentials' not found

## 7 candidate regions for zymodeme 2.2 -- thus I am betting that the reference strain is a 2.2
zymo23_sequentials <- sequential_variants(new_sets, conditions = "z2.3",
                                          minimum = 2, maximum_separation = 2)

## Error in eval(expr, envir, enclos): object 'new_sets' not found

dim(zymo23_sequentials)

## Error in eval(expr, envir, enclos): object 'zymo23_sequentials' not found

## In contrast, there are lots (587) of interesting regions for 2.3!

knn_zymo22_sequentials <- sequential_variants(knn_sets, conditions = "z2.2",
                                              minimum = 1, maximum_separation = 2)

## Error in eval(expr, envir, enclos): object 'knn_sets' not found

dim(knn_zymo22_sequentials)

## Error in eval(expr, envir, enclos): object 'knn_zymo22_sequentials' not found

## 7 candidate regions for zymodeme 2.2 -- thus I am betting that the reference strain is a 2.2
knn_zymo23_sequentials <- sequential_variants(knn_new_sets, conditions = "z2.3",
                                              minimum = 2, maximum_separation = 2)

## Error in eval(expr, envir, enclos): object 'knn_new_sets' not found

dim(knn_zymo23_sequentials)

## Error in eval(expr, envir, enclos): object 'knn_zymo23_sequentials' not found

5.1.2 Extract a promising region from the genome

The first 4 candidate regions from my set of remaining: * Chr Pos. Distance * LpaL13-15 238433 448 * LpaL13-18 142844 613 * LpaL13-29 830342 252 * LpaL13-33 1331507 843

Lets define a couple of terms: * Third: Each of the 4 above positions. * Second: Third - Distance * End: Third + PrimerLen * Start: Second - Primerlen

In each instance, these are the last positions, so we want to grab three things:

• The entire region from End -> Start, this way we can have a quick sanity check.
• Start -> Second.
• (Third -> End) <- Reverse complemented

## * LpaL13-15 238433 448
first_candidate_chr <- genome[["LpaL13_15"]]
primer_length <- 22
amplicon_length <- 448
first_candidate_third <- 238433
first_candidate_second <- first_candidate_third - amplicon_length
first_candidate_start <- first_candidate_second - primer_length
first_candidate_end <- first_candidate_third + primer_length
first_candidate_region <- subseq(first_candidate_chr, first_candidate_start, first_candidate_end)
first_candidate_region
first_candidate_5p <- subseq(first_candidate_chr, first_candidate_start, first_candidate_second)
as.character(first_candidate_5p)
first_candidate_3p <- spgs::reverseComplement(subseq(first_candidate_chr, first_candidate_third, first_candidate_end))
first_candidate_3p

## * LpaL13-18 142844 613
second_candidate_chr <- genome[["LpaL13_18"]]
primer_length <- 22
amplicon_length <- 613
second_candidate_third <- 142844
second_candidate_second <- second_candidate_third - amplicon_length
second_candidate_start <- second_candidate_second - primer_length
second_candidate_end <- second_candidate_third + primer_length
second_candidate_region <- subseq(second_candidate_chr, second_candidate_start, second_candidate_end)
second_candidate_region
second_candidate_5p <- subseq(second_candidate_chr, second_candidate_start, second_candidate_second)
as.character(second_candidate_5p)
second_candidate_3p <- spgs::reverseComplement(subseq(second_candidate_chr, second_candidate_third, second_candidate_end))
second_candidate_3p


## * LpaL13-29 830342 252
third_candidate_chr <- genome[["LpaL13_29"]]
primer_length <- 22
amplicon_length <- 252
third_candidate_third <- 830342
third_candidate_second <- third_candidate_third - amplicon_length
third_candidate_start <- third_candidate_second - primer_length
third_candidate_end <- third_candidate_third + primer_length
third_candidate_region <- subseq(third_candidate_chr, third_candidate_start, third_candidate_end)
third_candidate_region
third_candidate_5p <- subseq(third_candidate_chr, third_candidate_start, third_candidate_second)
as.character(third_candidate_5p)
third_candidate_3p <- spgs::reverseComplement(subseq(third_candidate_chr, third_candidate_third, third_candidate_end))
third_candidate_3p
## You are a garbage polypyrimidine tract.
## Which is actually interesting if the mutations mess it up.


## * LpaL13-33 1331507 843
fourth_candidate_chr <- genome[["LpaL13_33"]]
primer_length <- 22
amplicon_length <- 843
fourth_candidate_third <- 1331507
fourth_candidate_second <- fourth_candidate_third - amplicon_length
fourth_candidate_start <- fourth_candidate_second - primer_length
fourth_candidate_end <- fourth_candidate_third + primer_length
fourth_candidate_region <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_end)
fourth_candidate_region
fourth_candidate_5p <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_second)
as.character(fourth_candidate_5p)
fourth_candidate_3p <- spgs::reverseComplement(subseq(fourth_candidate_chr, fourth_candidate_third, fourth_candidate_end))
fourth_candidate_3p

5.2 Go hunting for Sanger sequencing regions

I made a fun little function which should find regions which have lots of variants associated with a given experimental factor.

pheno <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
pheno <- subset_expt(pheno, subset = "!is.na(pData(pheno)[['bcftable']])")
pheno_snps <- sm(count_expt_snps(pheno, annot_column = "bcftable"))

fun_stuff <- snp_density_primers(
    pheno_snps,
    bsgenome = "BSGenome.Leishmania.panamensis.MHOMCOL81L13.v53",
    gff = "reference/TriTrypDB-53_LpanamensisMHOMCOL81L13.gff")
drop_scaffolds <- grepl(x = rownames(fun_stuff$favorites), pattern = "SCAF")
favorite_primer_regions <- fun_stuff[["favorites"]][!drop_scaffolds, ]
favorite_primer_regions[["bin"]] <- rownames(favorite_primer_regions)
library(dplyr)
favorite_primer_regions <- favorite_primer_regions %>%
  relocate(bin)

5.3 Combine this table with 2.2/2.3 genes

Here is my note from our meeting:

Cross reference primers to DE genes of 2.2/2.3 and/or resistance/suscpetible, add a column to the primer spreadsheet with the DE genes (in retrospect I am guessing this actually means to put the logFC as a column.

One nice thing, I did a semantic removal on the lp_expt, so the set of logFC/pvalues should not have any of the offending types; thus I should be able to automagically get rid of them in the merge.

logfc_columns <- zy_df[, c("deseq_logfc", "deseq_adjp")]
colnames(logfc_columns) <- c("z23_logfc", "z23_adjp")
new_table <- merge(favorite_primer_regions, logfc_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names")
sus_columns <- sus_df[, c("deseq_logfc", "deseq_adjp")]
colnames(sus_columns) <- c("sus_logfc", "sus_adjp")
new_table <- merge(new_table, sus_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names") %>%
  relocate(bin)
written <- write_xlsx(data=new_table,
                      excel="excel/favorite_primers_xref_zy_sus.xlsx")

5.4 Make a heatmap describing the clustering of variants

We can cross reference the variants against the zymodeme status and plot a heatmap of the results and hopefully see how they separate.

snp_genes <- snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome")

## Error in eval(expr, envir, enclos): object 'new_sets' not found

clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, norm = "quant") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  set_expt_colors(clinical_colors_v2)

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': error in evaluating the argument 'object' in selecting a method for function 'pData': error in evaluating the argument 'expt' in selecting a method for function 'normalize_expt': object 'pruned_snps' not found

zymo_heat <- plot_disheat(new_zymo_norm)

## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt_data' in selecting a method for function 'plot_heatmap': object 'new_zymo_norm' not found

pp(file = "images/onlyz22_z23_snp_heatmap.png", width=12, height=12)
zymo_heat$plot

## Error in eval(expr, envir, enclos): object 'zymo_heat' not found

closed <- dev.off()
zymo_heat[["plot"]]

## Error in eval(expr, envir, enclos): object 'zymo_heat' not found

5.4.1 Annotated heatmap of variants

Now let us try to make a heatmap which includes some of the annotation data.

des <- pData(both_norm)
zymo_column <- "zymodemecategorical"
undef_idx <- is.na(des[[zymo_column]])
des[undef_idx, "strain"] <- "unknown"

##hmcols <- colorRampPalette(c("yellow","black","darkblue"))(256)
correlations <- hpgl_cor(exprs(both_norm))
na_idx <- is.na(correlations)
correlations[na_idx] <- 0

zymo_missing_idx <- is.na(des[[zymo_column]])
des[[zymo_column]] <- as.character(des[[zymo_column]])
des[["clinicalcategorical"]] <- as.character(des[["clinicalcategorical"]])
des[zymo_missing_idx, zymo_column] <- "unknown"
mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c(zymo_column, "clinicalcategorical")])
colnames(col_data) <- c("zymodeme", "outcome")
unknown_clinical <- is.na(col_data[["clinicalcategorical"]])
col_data[unknown_clinical, "outcome"] <- "undefined"

row_data <- as.data.frame(des[, c("sus_category_current", "knnv2classification")])
colnames(row_data) <- c("susceptibility", "mlclass")

myannot <- list(
  "Col" = list("data" = col_data),
  "Row" = list("data" = row_data))
myclust <- list("cuth" = 1.0,
                "col" = BrewerClusterCol)
mylabs <- list(
  "Row" = list("nrow" = 4),
  "Col" = list("nrow" = 4))
hmcols <- colorRampPalette(c("darkblue", "beige"))(380)
zymo_annot_heat <- annHeatmap2(
    correlations,
    dendrogram = mydendro,
    annotation = myannot,
    cluster = myclust,
    labels = mylabs,
    ## The following controls if the picture is symmetric
    scale = "none",
    col = hmcols)

dev <- pp(file = "images/dendro_heatmap.png", height = 20, width = 20)
plot(zymo_annot_heat)
closed <- dev.off()
plot(zymo_annot_heat)

Print the larger heatmap so that all the labels appear. Keep in mind that as we get more samples, this image needs to continue getting bigger.

big heatmap

xref_prop <- table(pheno_snps[["conditions"]])
pheno_snps$conditions
idx_tbl <- exprs(pheno_snps) > 5
new_tbl <- data.frame(row.names = rownames(exprs(pheno_snps)))
for (n in names(xref_prop)) {
  new_tbl[[n]] <- 0
  idx_cols <- which(pheno_snps[["conditions"]] == n)
  prop_col <- rowSums(idx_tbl[, idx_cols]) / xref_prop[n]
  new_tbl[n] <- prop_col
}
keepers <- grepl(x = rownames(new_tbl), pattern = "LpaL13")
new_tbl <- new_tbl[keepers, ]
new_tbl[["strong22"]] <- 1.001 - new_tbl[["z2.2"]]
new_tbl[["strong23"]] <- 1.001 - new_tbl[["z2.3"]]
s22_na <- new_tbl[["strong22"]] > 1
new_tbl[s22_na, "strong22"] <- 1
s23_na <- new_tbl[["strong23"]] > 1
new_tbl[s23_na, "strong23"] <- 1

new_tbl[["SNP"]] <- rownames(new_tbl)
new_tbl[["Chromosome"]] <- gsub(x = new_tbl[["SNP"]], pattern = "chr_(.*)_pos_.*", replacement = "\\1")
new_tbl[["Position"]] <- gsub(x = new_tbl[["SNP"]], pattern = ".*_pos_(\\d+)_.*", replacement = "\\1")
new_tbl <- new_tbl[, c("SNP", "Chromosome", "Position", "strong22", "strong23")]

library(CMplot)
simplify <- new_tbl
simplify[["strong22"]] <- NULL

CMplot(simplify, bin.size = 100000)

CMplot(new_tbl, plot.type="m", multracks=TRUE, threshold = c(0.01, 0.05),
       threshold.lwd=c(1,1), threshold.col=c("black","grey"),
       amplify=TRUE, bin.size=10000,
       chr.den.col=c("darkgreen", "yellow", "red"),
       signal.col=c("red", "green", "blue"),
       signal.cex=1, file="jpg", memo="", dpi=300, file.output=TRUE, verbose=TRUE)

5.5 Try out MatrixEQTL

This tool looks a little opaque, but provides sample data with things that make sense to me and should be pretty easy to recapitulate in our data.

1. covariates.txt: Columns are samples, rows are things from pData – the most likely ones of interest for our data would be zymodeme, sensitivity
2. geneloc.txt: columns are ‘geneid’, ‘chr’, ‘left’, ‘right’. I guess I can assume left and right are start/stop; in which case this is trivially acquirable from fData.
3. ge.txt: This appears to be a log(rpkm/cpm) table with rows as genes and columns as samples
4. snpsloc.txt: columns are ‘snpid’, ‘chr’, ‘pos’
5. snps.txt: columns are samples, rows are the ids from snsploc, values a 0,1,2. I assume 0 is identical and 1..12 are the various A->TGC T->AGC C->AGT G->ACT

## For this, let us use the 'new_snps' data structure.
## Caveat here: these need to be coerced to numbers.
my_covariates <- pData(new_snps)[, c(zymo_column, "clinicalcategorical")]
for (col in colnames(my_covariates)) {
  my_covariates[[col]] <- as.numeric(as.factor(my_covariates[[col]]))
}
my_covariates <- t(my_covariates)

my_geneloc <- fData(lp_expt)[, c("gid", "chromosome", "start", "end")]
colnames(my_geneloc) <- c("geneid", "chr", "left", "right")

my_ge <- exprs(normalize_expt(lp_expt, transform = "log2", filter = TRUE, convert = "cpm"))
used_samples <- tolower(colnames(my_ge)) %in% colnames(exprs(new_snps))
my_ge <- my_ge[, used_samples]

my_snpsloc <- data.frame(rownames = rownames(exprs(new_snps)))
## Oh, caveat here: Because of the way I stored the data,
## I could have duplicate rows which presumably will make matrixEQTL sad
my_snpsloc[["chr"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\1",
                            x = rownames(my_snpsloc))
my_snpsloc[["pos"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\2",
                            x = rownames(my_snpsloc))
test <- duplicated(my_snpsloc)
## Each duplicated row would be another variant at that position;
## so in theory we would do a rle to number them I am guessing
## However, I do not have different variants so I think I can ignore this for the moment
## but will need to make my matrix either 0 or 1.
if (sum(test) > 0) {
  message("There are: ", sum(duplicated), " duplicated entries.")
  keep_idx <- ! test
  my_snpsloc <- my_snpsloc[keep_idx, ]
}

my_snps <- exprs(new_snps)
one_idx <- my_snps > 0
my_snps[one_idx] <- 1

## Ok, at this point I think I have all the pieces which this method wants...
## Oh, no I guess not; it actually wants the data as a set of filenames...
library(MatrixEQTL)
write.table(my_snps, "eqtl/snps.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snps, "eqtl/snps.tsv", )
write.table(my_snpsloc, "eqtl/snpsloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snpsloc, "eqtl/snpsloc.tsv")
write.table(as.data.frame(my_ge), "eqtl/ge.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_ge), "eqtl/ge.tsv")
write.table(as.data.frame(my_geneloc), "eqtl/geneloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_geneloc), "eqtl/geneloc.tsv")
write.table(as.data.frame(my_covariates), "eqtl/covariates.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_covariates), "eqtl/covariates.tsv")

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

# Genotype file name
SNP_file_name = "eqtl/snps.tsv"
snps_location_file_name = "eqtl/snpsloc.tsv"
expression_file_name = "eqtl/ge.tsv"
gene_location_file_name = "eqtl/geneloc.tsv"
covariates_file_name = "eqtl/covariates.tsv"
# Output file name
output_file_name_cis = tempfile()
output_file_name_tra = tempfile()
# Only associations significant at this level will be saved
pvOutputThreshold_cis = 0.1
pvOutputThreshold_tra = 0.1
# Error covariance matrix
# Set to numeric() for identity.
errorCovariance = numeric()
# errorCovariance = read.table("Sample_Data/errorCovariance.txt");
# Distance for local gene-SNP pairs
cisDist = 1e6
## Load genotype data
snps = SlicedData$new()
snps$fileDelimiter = "\t"      # the TAB character
snps$fileOmitCharacters = "NA" # denote missing values;
snps$fileSkipRows = 1          # one row of column labels
snps$fileSkipColumns = 1       # one column of row labels
snps$fileSliceSize = 2000      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name)
## Load gene expression data
gene = SlicedData$new()
gene$fileDelimiter = "\t"      # the TAB character
gene$fileOmitCharacters = "NA" # denote missing values;
gene$fileSkipRows = 1          # one row of column labels
gene$fileSkipColumns = 1       # one column of row labels
gene$fileSliceSize = 2000      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name)
## Load covariates
cvrt = SlicedData$new()
cvrt$fileDelimiter = "\t"      # the TAB character
cvrt$fileOmitCharacters = "NA" # denote missing values;
cvrt$fileSkipRows = 1          # one row of column labels
cvrt$fileSkipColumns = 1       # one column of row labels
if(length(covariates_file_name) > 0) {
  cvrt$LoadFile(covariates_file_name)
}
## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE)
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE)

me = Matrix_eQTL_main(
    snps = snps,
    gene = gene,
    cvrt = cvrt,
    output_file_name = output_file_name_tra,
    pvOutputThreshold = pvOutputThreshold_tra,
    useModel = useModel,
    errorCovariance = errorCovariance,
    verbose = TRUE,
    output_file_name.cis = output_file_name_cis,
    pvOutputThreshold.cis = pvOutputThreshold_cis,
    snpspos = snpspos,
    genepos = genepos,
    cisDist = cisDist,
    pvalue.hist = "qqplot",
    min.pv.by.genesnp = FALSE,
    noFDRsaveMemory = FALSE);

if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message("This is hpgltools commit: ", get_git_commit())
  message("Saving to ", savefile)
  ## tmp <- sm(saveme(filename = savefile))
}

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset

## This is hpgltools commit:

## Saving to tmrc2_post_visualization_202405.rda.xz

tmp <- loadme(filename = savefile)