1 Changelog
- 202309: Disabled GSVA analyses until/unless we get permission to include the mSigDB 7.5.1 release. I will simplify the filenames so that one may easily drop in a downloaded copy of the data and run those blocks. Until then, I guess you will have to trust me when I say those blocks all work?
- 202309: Moved all GSEA analyses to 04lrt_gsea_gsva.Rmd
- 202309 next day: Moving GSEA back because it adds too much complexity to save/reload the DE results for gProfiler and friends.
- Still hunting for messed up colors, changed input data to match new version.
2 Introduction
The various differential expression analyses of the data generated in tmrc3_datasets will occur in this document.
2.1 Naming conventions
I am going to try to standardize how I name the various data structures created in this document. Most of the large data created are either sets of differential expression analyses, their combined results, or the set of results deemed ‘significant’.
Hopefully by now they all follow these guidelines:
{clinic(s)}sample-subset}{primary-question(s)}{datatype}{batch-method}
- {clinic}: This is either tc or t for Tumaco and Cali, or just Tumaco.
- {sample-subset}: Things like ‘all’ or ‘monocytes’.
- {primary-question}: Shorthand name for the primary contrasts performed, thus ‘clinics’ would suggest a comparison of Tumaco vs. Cali. ‘visits’ would compare v2/v1, etc.
- {datatype}: de, table, sig
- {batch-type}: nobatch, batch{factor}, sva. {factor} in this instance should be a column from the metadata.
With this in mind, ‘tc_biopsies_clinic_de_sva’ should be the Tumaco+Cali biopsy data after performing the differential expression analyses comparing the clinics using sva.
I suspect there remain some exceptions and/or errors.
2.2 Define contrasts for DE analyses
Each of the following lists describes the set of contrasts that I think are interesting for the various ways one might consider the TMRC3 dataset. The variables are named according to the assumed data with which they will be used, thus tc_cf_contrasts is expected to be used for the Tumaco+Cali data and provide a series of cure/fail comparisons which (to the extent possible) across both locations. In every case, the name of the list element will be used as the contrast name, and will thus be seen as the sheet name in the output xlsx file(s); the two pieces of the character vector value are the numerator and denominator of the associated contrast.
2.3 GSEA
The GSEA analyses will follow each DE analysis during this document.
Most (all?) of the GSEA analyses used in this paper were done via gProfiler rather than goseq/clusterProfiler/topGO/GOstats. Primarily because it is so easy to invoke gprofiler.
clinic_contrasts <- list(
"clinics" = c("cali", "tumaco"))
## In some cases we have no Cali failure samples, so there remain only 2
## contrasts that are likely of interest
tc_cf_contrasts <- list(
"tumaco" = c("tumaco_failure", "tumaco_cure"),
"cure" = c("tumaco_cure", "cali_cure"))
## In other cases, we have cure/fail for both places.
clinic_cf_contrasts <- list(
"cali" = c("cali_failure", "cali_cure"),
"tumaco" = c("tumaco_failure", "tumaco_cure"),
"cure" = c("tumaco_cure", "cali_cure"),
"fail" = c("tumaco_failure", "cali_failure"))
cf_contrast <- list(
"outcome" = c("tumaco_failure", "tumaco_cure"))
t_cf_contrast <- list(
"outcome" = c("failure", "cure"))
visitcf_contrasts <- list(
"v1cf" = c("v1_failure", "v1_cure"),
"v2cf" = c("v2_failure", "v2_cure"),
"v3cf" = c("v3_failure", "v3_cure"))
visit_contrasts <- list(
"v2v1" = c("c2", "c1"),
"v3v1" = c("c3", "c1"),
"v3v2" = c("c3", "c2"))
visit_v1later <- list(
"later_vs_first" = c("later", "first"))
celltypes <- list(
"eo_mono" = c("eosinophils", "monocytes"),
"ne_mono" = c("neutrophils", "monocytes"),
"eo_ne" = c("eosinophils", "neutrophils"))
ethnicity_contrasts <- list(
"mestizo_indigenous" = c("mestiza", "indigena"),
"mestizo_afrocol" = c("mestiza", "afrocol"),
"indigenous_afrocol" = c("indigena", "afrocol"))3 Compare samples by clinic
3.1 DE: Compare clinics, all samples
Perform a svaseq-guided comparison of the two clinics. Ideally this will give some clue about just how strong the clinic-based batch effect really is and what its causes are.
tc_clinic_type <- tc_valid %>%
set_expt_conditions(fact = "clinic") %>%
set_expt_batches(fact = "typeofcells")## The numbers of samples by condition are:##
## cali tumaco
## 61 123## The number of samples by batch are:##
## biopsy eosinophils monocytes neutrophils
## 18 41 63 62table(pData(tc_clinic_type)[["condition"]])##
## cali tumaco
## 61 123tc_all_clinic_de_sva <- all_pairwise(tc_clinic_type, model_batch = "svaseq",
filter = TRUE, methods = methods)##
## cali tumaco
## 61 123## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_all_clinic_de_sva## 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 21 comparisons.## The logFC agreement among the methods follows:## tumc_vs_cl
## basic_vs_deseq 0.8086
## basic_vs_dream 0.9211
## basic_vs_ebseq 0.7548
## basic_vs_edger 0.8735
## basic_vs_limma 0.9693
## basic_vs_noiseq 0.9114
## deseq_vs_dream 0.8806
## deseq_vs_ebseq 0.8504
## deseq_vs_edger 0.9377
## deseq_vs_limma 0.7998
## deseq_vs_noiseq 0.8770
## dream_vs_ebseq 0.8550
## dream_vs_edger 0.9438
## dream_vs_limma 0.9373
## dream_vs_noiseq 0.9347
## ebseq_vs_edger 0.8618
## ebseq_vs_limma 0.7772
## ebseq_vs_noiseq 0.8382
## edger_vs_limma 0.8621
## edger_vs_noiseq 0.9363
## limma_vs_noiseq 0.8895tc_all_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "tumaco_vs_cali"tc_all_clinic_table_sva <- combine_de_tables(
tc_all_clinic_de_sva, keepers = clinic_contrasts,
excel = glue("{clinic_prefix}/tc_all_clinic_table_sva-v{ver}.xlsx"))
tc_all_clinic_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 tumaco_vs_cali-inverted 273 1799 323 1667
## limma_sigup limma_sigdown
## 1 392 606## `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.
tc_all_clinic_sig_sva <- extract_significant_genes(
tc_all_clinic_table_sva,
excel = glue("{clinic_prefix}/compare_clinics/tc_clinic_type_sig_sva-v{ver}.xlsx"))
tc_all_clinic_sig_sva## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## clinics 392 606 323 1667 273 1799 224
## ebseq_down basic_up basic_down
## clinics 424 0 5914
3.1.1 GSEA: comparing the clinics
increased_tumaco_categories_up <- simple_gprofiler(
tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]],
excel = glue("{gsea_prefix}/tumaco_cateogies_up-v{ver}.xlsx"))
increased_tumaco_categories_up## A set of ontologies produced by gprofiler using 273
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 17 MF
## 12 BP
## 1 KEGG
## 1 REAC
## 0 WP
## 100 TF
## 0 MIRNA
## 0 HPA
## 0 CORUM
## 0 HP hits.increased_tumaco_categories_up[["pvalue_plots"]][["BP"]]## NULLincreased_cali_categories <- simple_gprofiler(
tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]],
excel = glue("{gsea_prefix}/cali_cateogies_up-v{ver}.xlsx"))
increased_cali_categories## A set of ontologies produced by gprofiler using 1799
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 59 MF
## 686 BP
## 2 KEGG
## 20 REAC
## 7 WP
## 333 TF
## 2 MIRNA
## 16 HPA
## 0 CORUM
## 14 HP hits.increased_cali_categories[["pvalue_plots"]][["BP"]]## NULL3.1.2 Visualize clinic differences
Let us take a quick look at the results of the comparison of Tumaco/Cali
Note: I keep re-introducing an error which causes these (volcano and MA) plots to be reversed with respect to the logFC values. Pay careful attention to these and make sure that they agree with the numbers of genes observed in the contrast.
## Check that up is up
summary(tc_all_clinic_table_sva[["data"]][["clinics"]][["deseq_logfc"]])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -20.280 -0.584 -0.155 -0.255 0.172 3.514## I think we can assume that most genes are down when considering Tumaco/Cali.
sum(tc_all_clinic_table_sva$data$clinics$deseq_logfc < -1.0 &
tc_all_clinic_table_sva$data$clinics$deseq_adjp < 0.05)## [1] 1795tc_all_clinic_table_sva[["plots"]][["clinics"]][["deseq_vol_plots"]]
## Ok, so it says 1794 up, but that is clearly the down side... Something is definitely messed up.
## The points are on the correct sides of the plot, but the categories of up/down are reversed.
## Theresa noted that she colors differently, and I think better: left side gets called
## 'increased in denominator', right side gets called 'increased in numerator';
## these two groups are colored according to their condition colors, and everything else is gray.
## I am checking out Theresa's helper_functions.R to get a sense of how she handles this, I think
## I can use a variant of her idea pretty easily:
## 1. Add a column 'Significance', which is a factor, and contains either 'Not enriched',
## 'Enriched in x', or 'Enriched in y' according to the logfc/adjp.
## 2. use the significance column for the geom_point color/fill in the volcano plot.
## My change to this idea would be to extract the colors from the input expressionset.There appear to be many more genes which are increased in the Tumaco samples with respect to the Cali samples.
3.2 DE: Compare clinics, eosinophil samples
The remaining cell types all have pretty strong clinic-based variance; but I am not certain if it is consistent across cell types.
table(pData(tc_eosinophils)[["condition"]])##
## cali_cure tumaco_cure tumaco_failure
## 15 17 9tc_eosinophils_clinic_de_nobatch <- all_pairwise(tc_eosinophils, parallel = parallel,
model_batch = FALSE, filter = TRUE,
methods = methods)##
## cali_cure tumaco_cure tumaco_failure
## 15 17 9## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_eosinophils_clinic_de_nobatch## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: none.## The primary analysis performed 21 comparisons.tc_eosinophils_clinic_de_nobatch[["deseq"]][["contrasts_performed"]]## [1] "tumaco_failure_vs_tumaco_cure" "tumaco_failure_vs_cali_cure"
## [3] "tumaco_cure_vs_cali_cure"tc_eosinophils_clinic_table_nobatch <- combine_de_tables(
tc_eosinophils_clinic_de_nobatch, keepers = tc_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))
tc_eosinophils_clinic_table_nobatch## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 tumaco_failure_vs_tumaco_cure 102 35 114
## 2 tumaco_cure_vs_cali_cure 834 814 856
## edger_sigdown limma_sigup limma_sigdown
## 1 32 62 17
## 2 817 712 705## Plot describing unique/shared genes in a differential expression table.
tc_eosinophils_clinic_sig_nobatch <- extract_significant_genes(
tc_eosinophils_clinic_table_nobatch,
excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_nobatch## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## tumaco 62 17 114 32 102 35 9
## cure 712 705 856 817 834 814 698
## ebseq_down basic_up basic_down
## tumaco 36 8 0
## cure 599 5550 0
tc_eosinophils_clinic_de_sva <- all_pairwise(tc_eosinophils, model_batch = "svaseq",
filter = TRUE, methods = methods)##
## cali_cure tumaco_cure tumaco_failure
## 15 17 9## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_eosinophils_clinic_de_sva## 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 21 comparisons.tc_eosinophils_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "tumaco_failure_vs_tumaco_cure" "tumaco_failure_vs_cali_cure"
## [3] "tumaco_cure_vs_cali_cure"tc_eosinophils_clinic_table_sva <- combine_de_tables(
tc_eosinophils_clinic_de_sva, keepers = tc_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 tumaco_failure_vs_tumaco_cure 89 57 90
## 2 tumaco_cure_vs_cali_cure 777 808 781
## edger_sigdown limma_sigup limma_sigdown
## 1 41 77 30
## 2 806 723 710## Plot describing unique/shared genes in a differential expression table.
tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
tc_eosinophils_clinic_table_sva,
excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_sva## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## tumaco 77 30 90 41 89 57 9
## cure 723 710 781 806 777 808 698
## ebseq_down basic_up basic_down
## tumaco 36 8 0
## cure 599 5550 0
3.3 DE: Compare clinics, biopsy samples
Interestingly to me, the biopsy samples appear to have the least location-based variance. But we can perform an explicit DE and see how well that hypothesis holds up.
Note that these data include cure and fail samples for
table(pData(tc_biopsies)[["condition"]])##
## cali_cure tumaco_cure tumaco_failure
## 4 9 5tc_biopsies_clinic_de_sva <- all_pairwise(tc_biopsies, parallel = parallel,
model_batch = "svaseq", filter = TRUE,
methods = methods)##
## cali_cure tumaco_cure tumaco_failure
## 4 9 5## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_biopsies_clinic_de_sva## 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 21 comparisons.tc_biopsies_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "tumaco_failure_vs_tumaco_cure" "tumaco_failure_vs_cali_cure"
## [3] "tumaco_cure_vs_cali_cure"tc_biopsies_clinic_table_sva <- combine_de_tables(
tc_biopsies_clinic_de_sva, keepers = tc_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Biopsies/tc_biopsies_clinic_table_sva-v{ver}.xlsx"))
tc_biopsies_clinic_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 tumaco_failure_vs_tumaco_cure 14 11 18
## 2 tumaco_cure_vs_cali_cure 1 0 0
## edger_sigdown limma_sigup limma_sigdown
## 1 6 0 0
## 2 0 0 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.
tc_biopsies_clinic_sig_sva <- extract_significant_genes(
tc_biopsies_clinic_table_sva,
excel = glue("{clinic_cf_prefix}/Biopsies/tc_biopsies_clinic_sig_sva-v{ver}.xlsx"))
tc_biopsies_clinic_sig_sva## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## tumaco 0 0 18 6 14 11 11
## cure 0 0 0 0 1 0 28
## ebseq_down basic_up basic_down
## tumaco 60 0 0
## cure 1 0 0
3.4 DE: Compare clinics, monocyte samples
At least for the moment, I am only looking at the differences between no-batch vs. sva across clinics for the monocyte samples. This was chosen mostly arbitrarily.
3.4.1 DE: Compare clinics, monocytes without batch estimation
Our baseline is the comparison of the monocytes samples without batch in the model or surrogate estimation. In theory at least, this should correspond to the PCA plot above when no batch estimation was performed.
table(pData(tc_monocytes)[["condition"]])##
## cali_cure cali_failure tumaco_cure tumaco_failure
## 18 3 21 21tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch = FALSE,
filter = TRUE,
methods = methods)##
## cali_cure cali_failure tumaco_cure tumaco_failure
## 18 3 21 21## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_monocytes_de_nobatch## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: none.## The primary analysis performed 21 comparisons.tc_monocytes_table_nobatch <- combine_de_tables(
tc_monocytes_de_nobatch, keepers = clinic_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_table_nobatch-v{ver}.xlsx"))
tc_monocytes_table_nobatch## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 cali_failure_vs_cali_cure 16 20 32
## 2 tumaco_failure_vs_tumaco_cure 48 121 60
## 3 tumaco_cure_vs_cali_cure 786 729 778
## 4 tumaco_failure_vs_cali_failure 638 492 518
## edger_sigdown limma_sigup limma_sigdown
## 1 13 38 5
## 2 139 24 37
## 3 784 646 716
## 4 540 395 570## Plot describing unique/shared genes in a differential expression table.
tc_monocytes_sig_nobatch <- extract_significant_genes(
tc_monocytes_table_nobatch,
excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_sig_nobatch-v{ver}.xlsx"))
tc_monocytes_sig_nobatch## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## cali 38 5 32 13 16 20 92
## tumaco 24 37 60 139 48 121 0
## cure 646 716 778 784 786 729 646
## fail 395 570 518 540 638 492 165
## ebseq_down basic_up basic_down
## cali 23 0 0
## tumaco 23 336 0
## cure 664 6383 0
## fail 529 2167 0
3.4.2 DE: Compare clinics, monocytes with svaseq
In contrast, the following comparison should give a view of the data corresponding to the svaseq PCA plot above. In the best case scenario, we should therefore be able to see some significane differences between the Tumaco cure and fail samples.
tc_monocytes_de_sva <- all_pairwise(tc_monocytes, model_batch = "svaseq",
filter = TRUE,
methods = methods)##
## cali_cure cali_failure tumaco_cure tumaco_failure
## 18 3 21 21## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_monocytes_de_sva## 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 21 comparisons.tc_monocytes_table_sva <- combine_de_tables(
tc_monocytes_de_sva, keepers = clinic_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_table_sva-v{ver}.xlsx"))
tc_monocytes_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 cali_failure_vs_cali_cure 28 36 40
## 2 tumaco_failure_vs_tumaco_cure 34 86 29
## 3 tumaco_cure_vs_cali_cure 761 732 713
## 4 tumaco_failure_vs_cali_failure 684 584 583
## edger_sigdown limma_sigup limma_sigdown
## 1 17 52 7
## 2 70 14 57
## 3 762 640 663
## 4 623 434 567## Plot describing unique/shared genes in a differential expression table.
tc_monocytes_sig_sva <- extract_significant_genes(
tc_monocytes_table_sva,
excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_sig_sva-v{ver}.xlsx"))
tc_monocytes_sig_sva## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## cali 52 7 40 17 28 36 92
## tumaco 14 57 29 70 34 86 0
## cure 640 663 713 762 761 732 646
## fail 434 567 583 623 684 584 165
## ebseq_down basic_up basic_down
## cali 23 0 0
## tumaco 23 336 0
## cure 664 6383 0
## fail 529 2167 0
3.4.3 DE Compare: How similar are the no-batch vs. SVA results?
The following block shows that these two results are exceedingly different, sugesting that the Cali cure/fail and Tumaco cure/fail cannot easily be considered in the same analysis. I did some playing around with my calculate_aucc function in this block and found that it is in some important way broken, at least if one expands the top-n genes to more than 20% of the number of genes in the data.
cali_table <- tc_monocytes_table_nobatch[["data"]][["cali"]]
table <- tc_monocytes_table_nobatch[["data"]][["tumaco"]]
cali_aucc <- calculate_aucc(cali_table, table, px = "deseq_adjp", py = "deseq_adjp",
lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc## These two tables have an aucc value of: 0.0659267365585479 and correlation:##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 1.2, df = 11106, p-value = 0.2
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.006843 0.030345
## sample estimates:
## cor
## 0.01175
cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]
cali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px = "deseq_adjp",
py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_sva## These two tables have an aucc value of: 0.0842668799254026 and correlation:##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 16, df = 11106, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1356 0.1719
## sample estimates:
## cor
## 0.1538
3.5 DE: Compare clinics, neutrophil samples
tc_neutrophils_de_nobatch <- all_pairwise(tc_neutrophils, parallel = parallel,
model_batch = FALSE, filter = TRUE,
methods = methods)##
## cali_cure cali_failure tumaco_cure tumaco_failure
## 18 3 20 21## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_neutrophils_de_nobatch## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: none.## The primary analysis performed 21 comparisons.tc_neutrophils_table_nobatch <- combine_de_tables(
tc_neutrophils_de_nobatch, keepers = clinic_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_table_nobatch-v{ver}.xlsx"))
tc_neutrophils_table_nobatch## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 cali_failure_vs_cali_cure 33 83 42
## 2 tumaco_failure_vs_tumaco_cure 95 50 112
## 3 tumaco_cure_vs_cali_cure 910 337 934
## 4 tumaco_failure_vs_cali_failure 984 257 808
## edger_sigdown limma_sigup limma_sigdown
## 1 32 37 10
## 2 57 7 12
## 3 342 630 520
## 4 283 380 462## Plot describing unique/shared genes in a differential expression table.
tc_neutrophils_sig_nobatch <- extract_significant_genes(
tc_neutrophils_table_nobatch,
excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_sig_nobatch-v{ver}.xlsx"))
tc_neutrophils_sig_nobatch## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## cali 37 10 42 32 33 83 91
## tumaco 7 12 112 57 95 50 7
## cure 630 520 934 342 910 337 687
## fail 380 462 808 283 984 257 113
## ebseq_down basic_up basic_down
## cali 38 0 0
## tumaco 7 7 0
## cure 299 4601 0
## fail 312 1651 0
tc_neutrophils_de_sva <- all_pairwise(tc_neutrophils, parallel = parallel,
model_batch = "svaseq", filter = TRUE,
methods = methods)##
## cali_cure cali_failure tumaco_cure tumaco_failure
## 18 3 20 21## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_neutrophils_de_sva## 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 21 comparisons.tc_neutrophils_table_sva <- combine_de_tables(
tc_neutrophils_de_sva, keepers = clinic_cf_contrasts,
excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_table_sva-v{ver}.xlsx"))
tc_neutrophils_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup
## 1 cali_failure_vs_cali_cure 91 181 103
## 2 tumaco_failure_vs_tumaco_cure 86 38 72
## 3 tumaco_cure_vs_cali_cure 844 379 843
## 4 tumaco_failure_vs_cali_failure 696 197 608
## edger_sigdown limma_sigup limma_sigdown
## 1 122 75 50
## 2 23 37 47
## 3 367 645 481
## 4 214 310 325## Plot describing unique/shared genes in a differential expression table.
tc_neutrophils_sig_sva <- extract_significant_genes(
tc_neutrophils_table_sva,
excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_sig_sva-v{ver}.xlsx"))
tc_neutrophils_sig_sva## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up
## cali 75 50 103 122 91 181 91
## tumaco 37 47 72 23 86 38 7
## cure 645 481 843 367 844 379 687
## fail 310 325 608 214 696 197 113
## ebseq_down basic_up basic_down
## cali 38 0 0
## tumaco 7 7 0
## cure 299 4601 0
## fail 312 1651 0
3.6 GSEA: Extract clinic-specific genes
Given the above comparisons, we can extract some gene sets which resulted from those DE analyses and eventually perform some ontology/KEGG/reactome/etc searches. This reminds me, I want to make my extract_significant_ functions to return gene-set data structures and my various ontology searches to take them as inputs. This should help avoid potential errors when extracting up/down genes.
clinic_sigenes_up <- rownames(tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]])
clinic_sigenes_down <- rownames(tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]])
clinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)
tc_eosinophils_sigenes_up <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_eosinophils_sigenes_down <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_monocytes_sigenes_up <- rownames(tc_monocytes_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_monocytes_sigenes_down <- rownames(tc_monocytes_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_neutrophils_sigenes_up <- rownames(tc_neutrophils_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_neutrophils_sigenes_down <- rownames(tc_neutrophils_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_eosinophils_sigenes <- c(tc_eosinophils_sigenes_up,
tc_eosinophils_sigenes_down)
tc_monocytes_sigenes <- c(tc_monocytes_sigenes_up,
tc_monocytes_sigenes_down)
tc_neutrophils_sigenes <- c(tc_neutrophils_sigenes_up,
tc_neutrophils_sigenes_down)3.7 GSEA: gProfiler of genes deemed up/down when comparing Cali and Tumaco
I was curious to try to understand why the two clinics appear to be so different vis a vis their PCA/DE; so I thought that gProfiler might help boil those results down to something more digestible.
3.7.1 GSEA: Compare clinics, all samples
Note that in the following block I used the function simple_gprofiler(), but later in this document I will use all_gprofiler(). The first invocation limits the search to a single table, while the second will iterate over every result in a pairwise differential expression analysis.
In this instance, we are looking at the vector of gene IDs deemed significantly different between the two clinics in either the up or down direction.
One other thing worth noting, the new version of gProfiler provides some fun interactive plots. I will add an example here.
tc_eosinophil_gprofiler <- simple_gprofiler(
tc_eosinophils_sigenes_up,
excel = glue("{gsea_prefix}/eosinophil_clinics_tumaco_up-v{ver}.xlsx"))
tc_eosinophil_gprofiler## A set of ontologies produced by gprofiler using 777
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 20 MF
## 218 BP
## 0 KEGG
## 2 REAC
## 0 WP
## 540 TF
## 6 MIRNA
## 0 HPA
## 2 CORUM
## 0 HP hits.clinic_gp <- simple_gprofiler(
clinic_sigenes,
excel = glue("{gsea_prefix}/both_clinics_cali_up-v{ver}.xlsx"))
clinic_gp$pvalue_plots$REAC
clinic_gp$pvalue_plots$BP## NULLclinic_gp$pvalue_plots$TF
clinic_gp$interactive_plots$GO## NULL3.7.2 GSEA: Compare clinics, Eosinophil samples
In the following block, I am looking at the gProfiler over represented groups observed across clinics in only the Eosinophils. First I do so for all genes(up or down), followed by only the up and down groups. Each of the following will include only the Reactome and GO:BP plots. These searches did not have too many other hits, excepting the transcription factor database.
tc_eosinophils_gp <- simple_gprofiler(
tc_eosinophils_sigenes,
excel = glue("{gsea_prefix}/eosinophil_clinics-v{ver}.xlsx"))
tc_eosinophils_gp## A set of ontologies produced by gprofiler using 1585
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 39 MF
## 276 BP
## 0 KEGG
## 5 REAC
## 0 WP
## 563 TF
## 10 MIRNA
## 0 HPA
## 5 CORUM
## 0 HP hits.tc_eosinophils_gp$pvalue_plots$REAC
tc_eosinophils_gp$pvalue_plots$BP## NULLtc_eosinophils_up_gp <- simple_gprofiler(
tc_eosinophils_sigenes_up,
excel = glue("{gsea_prefix}/eosinophil_clinics_tumaco_up-v{ver}.xlsx"))
tc_eosinophils_up_gp## A set of ontologies produced by gprofiler using 777
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 20 MF
## 218 BP
## 0 KEGG
## 2 REAC
## 0 WP
## 540 TF
## 6 MIRNA
## 0 HPA
## 2 CORUM
## 0 HP hits.tc_eosinophils_up_gp$pvalue_plots$REAC
tc_eosinophils_down_gp <- simple_gprofiler(
tc_eosinophils_sigenes_down,
excel = glue("{gsea_prefix}/eosinophil_clinics_cali_up-v{ver}.xlsx"))
tc_eosinophils_down_gp## A set of ontologies produced by gprofiler using 808
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 14 MF
## 94 BP
## 2 KEGG
## 9 REAC
## 2 WP
## 77 TF
## 0 MIRNA
## 0 HPA
## 0 CORUM
## 0 HP hits.tc_eosinophils_down_gp$pvalue_plots$REAC
3.7.3 GSEA: Compare clinics, Monocyte samples
In the following block I repeated the above query, but this time looking at the monocyte samples.
tc_monocytes_up_gp <- simple_gprofiler(
tc_monocytes_sigenes,
excel = glue("{gsea_prefix}/monocyte_clinics-v{ver}.xlsx"))
tc_monocytes_up_gp## A set of ontologies produced by gprofiler using 1493
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 55 MF
## 476 BP
## 0 KEGG
## 6 REAC
## 4 WP
## 495 TF
## 2 MIRNA
## 0 HPA
## 1 CORUM
## 0 HP hits.tc_monocytes_up_gp$pvalue_plots$REAC
tc_monocytes_up_gp$pvalue_plots$BP## NULLtc_monocytes_down_gp <- simple_gprofiler(
tc_monocytes_sigenes_down,
excel = glue("{gsea_prefix}/monocyte_clinics_cali_up-v{ver}.xlsx"))
tc_monocytes_down_gp$pvalue_plots$REAC
tc_monocytes_down_gp$pvalue_plots$BP## NULL3.7.3.1 GSEA: Compare clinics, Neutrophil samples
Ibid. This time looking at the Neutrophils. Thus the first two images should be a superset of the second and third pairs of images; assuming that the genes in the up/down list do not cause the groups to no longer be significant. Interestingly, the reactome search did not return any hits for the increased search.
tc_neutrophils_gp <- simple_gprofiler(
tc_neutrophils_sigenes,
excel = glue("{gsea_prefix}/neutrophil_clinics-v{ver}.xlsx"))
## tc_neutrophils_gp$pvalue_plots$REAC ## no hits
tc_neutrophils_gp$pvalue_plots$BP## NULLtc_neutrophils_gp$pvalue_plots$TF
tc_neutrophils_up_gp <- simple_gprofiler(
tc_neutrophils_sigenes_up,
excel = glue("{gsea_prefix}/neutrophil_clinics_tumaco_up-v{ver}.xlsx"))
## tc_neutrophils_up_gp$pvalue_plots$REAC ## No hits
tc_neutrophils_up_gp$pvalue_plots$BP## NULLtc_neutrophils_down_gp <- simple_gprofiler(
tc_neutrophils_sigenes_down,
excel = glue("{gsea_prefix}/neutrophil_clinics_cali_up-v{ver}.xlsx"))
tc_neutrophils_down_gp$pvalue_plots$REAC
tc_neutrophils_down_gp$pvalue_plots$BP## NULL4 Compare DE: How similar are Tumaco C/F vs. Cali C/F
The following expands the cross-clinic query above to also test the neutrophils. Once again, I think it will pretty strongly support the hypothesis that the two clinics are not compatible.
We are concerned that the clinic-based batch effect may make our results essentially useless. One way to test this concern is to compare the set of genes observed different between the Cali Cure/Fail vs. the Tumaco Cure/Fail.
cali_table_nobatch <- tc_neutrophils_table_nobatch[["data"]][["cali"]]
tumaco_table_nobatch <- tc_neutrophils_table_nobatch[["data"]][["tumaco"]]
cali_merged_nobatch <- merge(cali_table_nobatch, tumaco_table_nobatch, by="row.names")
cor.test(cali_merged_nobatch[, "deseq_logfc.x"], cali_merged_nobatch[, "deseq_logfc.y"])##
## Pearson's product-moment correlation
##
## data: cali_merged_nobatch[, "deseq_logfc.x"] and cali_merged_nobatch[, "deseq_logfc.y"]
## t = -16, df = 9242, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1798 -0.1401
## sample estimates:
## cor
## -0.16cali_aucc_nobatch <- calculate_aucc(cali_table_nobatch, tumaco_table_nobatch, px = "deseq_adjp",
py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_nobatch$plot
5 Tumaco and Cali, cure vs. fail
In all of the above, we are looking to understand the differences between the two location. Let us now step back and perform the original question: fail/cure without regard to location.
I performed this query with a few different parameters, notably with(out) sva and again using each cell type, including biopsies. The main reasion I am keeping these comparisons is in the relatively weak hope that there will be sufficient signal in the full dataset that it might be able to overcome the apparently ridiculous batch effect from the two clinics.
5.1 All cell types together, with(out) SVA
table(pData(tc_valid)[["condition"]])##
## cure failure
## 122 62tc_all_cf_de_sva <- all_pairwise(tc_valid, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 122 62## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_all_cf_table_sva <- combine_de_tables(
tc_all_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_table_sva-v{ver}.xlsx"))
tc_all_cf_sig_sva <- extract_significant_genes(
tc_all_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_sig_sva-v{ver}.xlsx"))
tc_all_cf_de_batch <- all_pairwise(tc_valid, filter = TRUE, methods = methods,
model_batch = TRUE)##
## cure failure
## 122 62
##
## v3 v2 v1
## 51 50 83## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_all_cf_table_batch <- combine_de_tables(
tc_all_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_table_batch-v{ver}.xlsx"))
tc_all_cf_sig_batch <- extract_significant_genes(
tc_all_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))5.2 All cell types except biopsies
I am not sure if this is the best choice, but I call the set of all samples excluding biopsies ‘clinical’.
table(pData(tc_clinical_nobiop)[["condition"]])##
## cure failure
## 109 57tc_clinical_cf_de_sva <- all_pairwise(tc_clinical_nobiop, filter = TRUE,
model_batch = "svaseq",
methods = methods)##
## cure failure
## 109 57## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_clinical_cf_de_sva## 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 21 comparisons.## The logFC agreement among the methods follows:## falr_vs_cr
## basic_vs_deseq 0.8865
## basic_vs_dream 0.9294
## basic_vs_ebseq 0.7583
## basic_vs_edger 0.9008
## basic_vs_limma 0.9413
## basic_vs_noiseq 0.9206
## deseq_vs_dream 0.9004
## deseq_vs_ebseq 0.8258
## deseq_vs_edger 0.9918
## deseq_vs_limma 0.8939
## deseq_vs_noiseq 0.9403
## dream_vs_ebseq 0.8097
## dream_vs_edger 0.9029
## dream_vs_limma 0.9855
## dream_vs_noiseq 0.8710
## ebseq_vs_edger 0.8175
## ebseq_vs_limma 0.8180
## ebseq_vs_noiseq 0.8311
## edger_vs_limma 0.8974
## edger_vs_noiseq 0.9512
## limma_vs_noiseq 0.8734tc_clinical_cf_table_sva <- combine_de_tables(
tc_clinical_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_table_sva-v{ver}.xlsx"))
tc_clinical_cf_table_sva## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 failure_vs_cure 186 96 214 93
## limma_sigup limma_sigdown
## 1 97 79## `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.
tc_clinical_cf_sig_sva <- extract_significant_genes(
tc_clinical_cf_table_sva, according_to = "deseq",
excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_sig_sva-v{ver}.xlsx"))
tc_clinical_cf_sig_sva## 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
## outcome 186 96
tc_clinical_cf_de_batch <- all_pairwise(tc_clinical_nobiop, filter = TRUE,
model_batch = TRUE,
methods = methods)##
## cure failure
## 109 57
##
## eosinophils monocytes neutrophils
## 41 63 62## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_clinical_cf_de_batch## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: batch in model/limma.## The primary analysis performed 21 comparisons.## The logFC agreement among the methods follows:## falr_vs_cr
## basic_vs_deseq 0.6416
## basic_vs_dream 0.7018
## basic_vs_ebseq 0.7583
## basic_vs_edger 0.6418
## basic_vs_limma 0.7121
## basic_vs_noiseq 0.9206
## deseq_vs_dream 0.8032
## deseq_vs_ebseq 0.7638
## deseq_vs_edger 0.9991
## deseq_vs_limma 0.8070
## deseq_vs_noiseq 0.7624
## dream_vs_ebseq 0.6724
## dream_vs_edger 0.8087
## dream_vs_limma 0.9718
## dream_vs_noiseq 0.6697
## ebseq_vs_edger 0.7635
## ebseq_vs_limma 0.6891
## ebseq_vs_noiseq 0.8311
## edger_vs_limma 0.8115
## edger_vs_noiseq 0.7625
## limma_vs_noiseq 0.6686tc_clinical_cf_table_batch <- combine_de_tables(
tc_clinical_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_table_batch-v{ver}.xlsx"))
tc_clinical_cf_table_batch## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 failure_vs_cure 106 68 116 74
## limma_sigup limma_sigdown
## 1 83 45## `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.
tc_clinical_cf_sig_batch <- extract_significant_genes(
tc_clinical_cf_table_batch, according_to = "deseq",
excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_sig_batch-v{ver}.xlsx"))
tc_clinical_cf_sig_batch## 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
## outcome 106 68
5.2.1 A portion of Supplemental Figure 11.
num_color <- color_choices[["cf"]][["cure"]]
den_color <- color_choices[["cf"]][["failure"]]
tc_clinical_cf_table <- tc_clinical_cf_table_sva[["data"]][["outcome"]]
tc_clinical_cf_volcano_top10 <- plot_volcano_condition_de(
tc_clinical_cf_table, "outcome", label = 10,
fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
color_high = num_color, color_low = den_color, label_size = 6)
pp(file = "figures/s11c_tc_clinical_cf_volcano_labeled_top10.svg")
tc_clinical_cf_volcano_top10[["plot"]]
dev.off()## png
## 2tc_clinical_cf_volcano_top10[["plot"]]
5.3 Biopsies, with(out) SVA
In the following block, we repeat the same question, but using only the biopsy samples from both clinics.
tc_biopsies_cf <- set_expt_conditions(tc_biopsies, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 13 5tc_biopsies_cf_de_sva <- all_pairwise(tc_biopsies_cf, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 13 5## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_biopsies_cf_table_sva <- combine_de_tables(
tc_biopsies_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))
tc_biopsies_cf_sig_sva <- extract_significant_genes(
tc_biopsies_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))
tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter = TRUE, methods = methods,
model_batch = TRUE)##
## cure failure
## 13 5
##
## v1
## 18## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## The condition+batch model failed. Does your experimental design support both condition and batch? Using only a conditional model.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## The condition+batch model failed. Does your experimental design support both condition and batch? Using only a conditional model.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## The condition+batch model failed. Does your experimental design support both condition and batch? Using only a conditional model.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## The condition+batch model failed. Does your experimental design support both condition and batch? Using only a conditional model.
## The condition+batch model failed. Does your experimental design support both condition and batch? Using only a conditional model.
tc_biopsies_cf_table_batch <- combine_de_tables(
tc_biopsies_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_table_batch-v{ver}.xlsx"))
tc_biopsies_cf_sig_batch <- extract_significant_genes(
tc_biopsies_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))5.4 Eosinophils, with(out) SVA
In the following block, we repeat the same question, but using only the Eosinophil samples from both clinics.
tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 32 9tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 32 9## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_eosinophils_cf_table_sva <- combine_de_tables(
tc_eosinophils_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Eosinophils/tc_eosinophils_cf_table_sva-v{ver}.xlsx"))
tc_eosinophils_cf_sig_sva <- extract_significant_genes(
tc_eosinophils_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_sig_sva-v{ver}.xlsx"))
tc_eosinophils_cf_de_batch <- all_pairwise(tc_eosinophils_cf, filter = TRUE,
model_batch = TRUE,
methods = methods)##
## cure failure
## 32 9
##
## v3 v2 v1
## 13 14 14## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_eosinophils_cf_table_batch <- combine_de_tables(
tc_eosinophils_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))
tc_eosinophils_cf_sig_batch <- extract_significant_genes(
tc_eosinophils_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))5.5 Monocytes, with(out) SVA
Repeat yet again, this time with the monocyte samples. The idea is to see if there is a cell type which is particularly good (or bad) at discriminating the two clinics.
tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 39 24tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 39 24## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_monocytes_cf_table_sva <- combine_de_tables(
tc_monocytes_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))
tc_monocytes_cf_sig_sva <- extract_significant_genes(
tc_monocytes_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))
tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
model_batch = TRUE)##
## cure failure
## 39 24
##
## v3 v2 v1
## 19 18 26## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_monocytes_cf_table_batch <- combine_de_tables(
tc_monocytes_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_table_batch-v{ver}.xlsx"))
tc_monocytes_cf_sig_batch <- extract_significant_genes(
tc_monocytes_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_sig_batch-v{ver}.xlsx"))5.6 Neutrophils, with(out) SVA
Last try, this time using the Neutrophil samples.
tc_neutrophils_cf <- set_expt_conditions(tc_neutrophils, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 38 24tc_neutrophils_cf_de_sva <- all_pairwise(tc_neutrophils_cf, parallel = parallel,
filter = TRUE, model_batch = "svaseq",
methods = methods)##
## cure failure
## 38 24## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_neutrophils_cf_table_sva <- combine_de_tables(
tc_neutrophils_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))
tc_neutrophils_cf_sig_sva <- extract_significant_genes(
tc_neutrophils_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_sig_sva-v{ver}.xlsx"))
tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter = TRUE,
model_batch = TRUE,
methods = methods)##
## cure failure
## 38 24
##
## v3 v2 v1
## 19 18 25## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_neutrophils_cf_table_batch <- combine_de_tables(
tc_neutrophils_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_table_batch-v{ver}.xlsx"))
tc_neutrophils_cf_sig_batch <- extract_significant_genes(
tc_neutrophils_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))6 Visit comparisons
Later in this document I do a bunch of visit/cf comparisons. In this block I want to explicitly only compare v1 to other visits. This is something I did quite a lot in the 2019 datasets, but never actually moved to this document.
v1_vs_later <- all_pairwise(tc_v1vs, model_batch = "svaseq", methods = methods,
filter = TRUE)##
## first later
## 65 101## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
v1_vs_later_table <- combine_de_tables(
v1_vs_later, keepers = visit_v1later,
excel = glue("{visit_prefix}/v1_vs_later_tables-v{ver}.xlsx"))
v1_vs_later_sig <- extract_significant_genes(
v1_vs_later_table,
excel = glue("{visit_prefix}/v1_vs_later_sig-v{ver}.xlsx"))6.0.0.1 GSEA: V1 vs other visits.
v1later_gp <- all_gprofiler(v1_vs_later_sig)
v1later_gp[[1]]$pvalue_plots$REAC
v1later_gp[[2]]$pvalue_plots$REAC
7 Sex comparison
tc_sex_de <- all_pairwise(tc_sex, model_batch = "svaseq", methods = methods,
filter = TRUE)##
## female male
## 28 156## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_sex_table <- combine_de_tables(
tc_sex_de, excel = glue("{sex_prefix}/tc_sex_table-v{ver}.xlsx"))
tc_sex_sig <- extract_significant_genes(
tc_sex_table, excel = glue("{sex_prefix}/tc_sex_sig-v{ver}.xlsx"))
tc_sex_gp <- all_gprofiler(tc_sex_sig)tc_sex_cure <- subset_expt(tc_sex, subset = "finaloutcome=='cure'")## subset_expt(): There were 184, now there are 122 samples.tc_sex_cure_de <- all_pairwise(tc_sex_cure, model_batch = "svaseq",
filter = TRUE,
methods = methods)##
## female male
## 19 103## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_sex_cure_de## 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 21 comparisons.## The logFC agreement among the methods follows:## mal_vs_fml
## basic_vs_deseq 0.6740
## basic_vs_dream 0.9121
## basic_vs_ebseq 0.5398
## basic_vs_edger 0.8003
## basic_vs_limma 0.9099
## basic_vs_noiseq 0.8159
## deseq_vs_dream 0.6763
## deseq_vs_ebseq 0.6490
## deseq_vs_edger 0.8641
## deseq_vs_limma 0.6481
## deseq_vs_noiseq 0.7503
## dream_vs_ebseq 0.6292
## dream_vs_edger 0.8070
## dream_vs_limma 0.9653
## dream_vs_noiseq 0.7585
## ebseq_vs_edger 0.6887
## ebseq_vs_limma 0.5899
## ebseq_vs_noiseq 0.6346
## edger_vs_limma 0.7769
## edger_vs_noiseq 0.8670
## limma_vs_noiseq 0.7196tc_sex_cure_table <- combine_de_tables(
tc_sex_cure_de, excel = glue("{sex_prefix}/tc_sex_cure_table-v{ver}.xlsx"))
tc_sex_cure_table## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 male_vs_female 70 74 64 80
## limma_sigup limma_sigdown
## 1 40 74## `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.
tc_sex_cure_sig <- extract_significant_genes(
tc_sex_cure_table, excel = glue("{sex_prefix}/tc_sex_cure_sig-v{ver}.xlsx"))
tc_sex_cure_sig## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down
## male_vs_female 40 74 64 80 70 74
## ebseq_up ebseq_down basic_up basic_down
## male_vs_female 10 8 13 0
7.0.0.1 GSEA: Sex comparisons both clinics
tc_sex_cure_gp <- all_gprofiler(tc_sex_cure_sig)
tc_sex_cure_gp## Running gProfiler on every set of significant genes found:## BP CORUM HP HPA KEGG MIRNA MF REAC TF WP
## male_vs_female_up 3 0 1 0 1 0 1 0 3 0
## male_vs_female_down 3 0 0 0 0 0 0 1 0 0tc_sex_cure_gp[[1]][["pvalue_plots"]][["BP"]]## NULLtc_sex_cure_gp[[2]][["pvalue_plots"]][["BP"]]## NULL8 Ethnicity comparisons
tc_ethnicity_de <- all_pairwise(tc_etnia_expt, model_batch = "svaseq",
filter = TRUE,
methods = methods)##
## afrocol indigena mestiza
## 91 46 47## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
tc_ethnicity_de## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 21 comparisons.tc_ethnicity_table <- combine_de_tables(
tc_ethnicity_de, keepers = ethnicity_contrasts,
excel = glue("{eth_prefix}/tc_ethnicity_table-v{ver}.xlsx"))
tc_ethnicity_table## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 mestiza_vs_indigena 48 22 53 23
## 2 mestiza_vs_afrocol 51 171 58 180
## 3 indigena_vs_afrocol 67 269 72 280
## limma_sigup limma_sigdown
## 1 24 14
## 2 44 90
## 3 78 144## Plot describing unique/shared genes in a differential expression table.
tc_ethnicity_table[["plots"]][["mestizo_indigenous"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["mestizo_afrocol"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["indigenous_afrocol"]][["deseq_ma_plots"]]
tc_ethnicity_sig <- extract_significant_genes(
tc_ethnicity_table, excel = glue("{eth_prefix}/tc_ethnicity_sig-v{ver}.xlsx"))
ethnicity_cure <- subset_expt(tc_etnia_expt, subset = "finaloutcome=='cure'")## subset_expt(): There were 184, now there are 122 samples.ethnicity_cure_de <- all_pairwise(ethnicity_cure, model_batch = "svaseq",
filter = TRUE,
methods = methods)##
## afrocol indigena mestiza
## 39 36 47## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Warning in createContrastL(objFlt$formula, objFlt$data, L): Contrasts with only
## a single non-zero term are already evaluated by default.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.
ethnicity_cure_table <- combine_de_tables(
ethnicity_cure_de, keepers = ethnicity_contrasts,
excel = glue("{eth_prefix}/ethnicity_cure_table-v{ver}.xlsx"))
ethnicity_cure_table## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 mestiza_vs_indigena 64 24 60 26
## 2 mestiza_vs_afrocol 68 168 78 167
## 3 indigena_vs_afrocol 87 352 97 340
## limma_sigup limma_sigdown
## 1 36 15
## 2 63 100
## 3 109 180## Plot describing unique/shared genes in a differential expression table.
ethnicity_cure_table[["plots"]][["mestizo_indigenous"]][["deseq_ma_plots"]]
ethnicity_cure_table[["plots"]][["mestizo_afrocol"]][["deseq_ma_plots"]]
ethnicity_cure_table[["plots"]][["indigenous_afrocol"]][["deseq_ma_plots"]]
ethnicity_cure_sig <- extract_significant_genes(
ethnicity_cure_table, excel = glue("{eth_prefix}/ethnicity_cure_sig-v{ver}.xlsx"))
ethnicity_cure_sig## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down edger_up edger_down deseq_up deseq_down
## mestizo_indigenous 36 15 60 26 64 24
## mestizo_afrocol 63 100 78 167 68 168
## indigenous_afrocol 109 180 97 340 87 352
## ebseq_up ebseq_down basic_up basic_down
## mestizo_indigenous 11 0 6 0
## mestizo_afrocol 6 16 41 0
## indigenous_afrocol 10 38 439 0
8.0.0.1 GSEA: Ethnicity differences
Performed once with both clinics and again with only Tumaco.
tc_ethnicity_gp <- all_gprofiler(tc_ethnicity_sig)pander::pander(sessionInfo())R version 4.4.1 (2024-06-14)
Platform: x86_64-conda-linux-gnu
locale: C
attached base packages: stats4, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: edgeR(v.4.0.16), ruv(v.0.9.7.1), DOSE(v.3.28.2), forcats(v.1.0.0), dplyr(v.1.1.4), hpgltools(v.1.0), Matrix(v.1.6-5), glue(v.1.7.0), SummarizedExperiment(v.1.32.0), GenomicRanges(v.1.54.1), GenomeInfoDb(v.1.38.6), IRanges(v.2.36.0), S4Vectors(v.0.40.2), MatrixGenerics(v.1.14.0), matrixStats(v.1.2.0), Biobase(v.2.62.0) and BiocGenerics(v.0.48.1)
loaded via a namespace (and not attached): fs(v.1.6.3), bitops(v.1.0-7), enrichplot(v.1.22.0), blockmodeling(v.1.1.5), HDO.db(v.0.99.1), httr(v.1.4.7), RColorBrewer(v.1.1-3), numDeriv(v.2016.8-1.1), tools(v.4.4.1), backports(v.1.4.1), utf8(v.1.2.4), R6(v.2.5.1), lazyeval(v.0.2.2), mgcv(v.1.9-1), withr(v.3.0.0), gridExtra(v.2.3), preprocessCore(v.1.64.0), fdrtool(v.1.2.18), cli(v.3.6.2), scatterpie(v.0.2.1), labeling(v.0.4.3), slam(v.0.1-53), EBSeq(v.2.0.0), sass(v.0.4.8), mvtnorm(v.1.2-4), robustbase(v.0.99-4), genefilter(v.1.84.0), yulab.utils(v.0.1.7), ggupset(v.0.4.0), gson(v.0.1.0), R.utils(v.2.12.3), limma(v.3.58.1), RSQLite(v.2.3.5), gridGraphics(v.0.5-1), generics(v.0.1.3), gtools(v.3.9.5), crosstalk(v.1.2.1), zip(v.2.3.1), GO.db(v.3.18.0), fansi(v.1.0.6), abind(v.1.4-5), R.methodsS3(v.1.8.2), lifecycle(v.1.0.4), yaml(v.2.3.8), gplots(v.3.1.3.1), qvalue(v.2.34.0), SparseArray(v.1.2.4), grid(v.4.4.1), blob(v.1.2.4), promises(v.1.2.1), crayon(v.1.5.2), lattice(v.0.22-5), cowplot(v.1.1.3), annotate(v.1.80.0), KEGGREST(v.1.42.0), pillar(v.1.9.0), knitr(v.1.45), varhandle(v.2.0.6), fgsea(v.1.28.0), boot(v.1.3-29), corpcor(v.1.6.10), codetools(v.0.2-19), fastmatch(v.1.1-4), ggfun(v.0.1.8), data.table(v.1.15.0), Vennerable(v.3.1.0.9000), treeio(v.1.29.1), vctrs(v.0.6.5), png(v.0.1-8), Rdpack(v.2.6), testthat(v.3.2.1), gtable(v.0.3.4), cachem(v.1.0.8), xfun(v.0.42), openxlsx(v.4.2.5.2), rbibutils(v.2.2.16), S4Arrays(v.1.2.0), mime(v.0.12), RcppEigen(v.0.3.3.9.4), tidygraph(v.1.3.1), survival(v.3.5-8), iterators(v.1.0.14), NOISeq(v.2.46.0), statmod(v.1.5.0), ellipsis(v.0.3.2), nlme(v.3.1-164), pbkrtest(v.0.5.2), ggtree(v.3.15.0), bit64(v.4.0.5), EnvStats(v.2.8.1), UpSetR(v.1.4.0), rprojroot(v.2.0.4), bslib(v.0.6.1), KernSmooth(v.2.23-22), colorspace(v.2.1-0), DBI(v.1.2.2), DESeq2(v.1.42.0), tidyselect(v.1.2.0), bit(v.4.0.5), compiler(v.4.4.1), graph(v.1.80.0), desc(v.1.4.3), DelayedArray(v.0.28.0), plotly(v.4.10.4), shadowtext(v.0.1.3), scales(v.1.3.0), caTools(v.1.18.2), DEoptimR(v.1.1-3), remaCor(v.0.0.18), RBGL(v.1.78.0), stringr(v.1.5.1), digest(v.0.6.34), minqa(v.1.2.6), variancePartition(v.1.32.5), rmarkdown(v.2.25), aod(v.1.3.3), XVector(v.0.42.0), RhpcBLASctl(v.0.23-42), htmltools(v.0.5.7), pkgconfig(v.2.0.3), lme4(v.1.1-35.1), lpsymphony(v.1.30.0), highr(v.0.10), fastmap(v.1.1.1), rlang(v.1.1.3), htmlwidgets(v.1.6.4), shiny(v.1.8.0), farver(v.2.1.1), jquerylib(v.0.1.4), IHW(v.1.30.0), jsonlite(v.1.8.8), BiocParallel(v.1.36.0), GOSemSim(v.2.28.1), R.oo(v.1.26.0), RCurl(v.1.98-1.14), magrittr(v.2.0.3), GenomeInfoDbData(v.1.2.11), ggplotify(v.0.1.2), patchwork(v.1.2.0), munsell(v.0.5.0), Rcpp(v.1.0.12), ape(v.5.8), viridis(v.0.6.5), stringi(v.1.8.3), ggraph(v.2.1.0), brio(v.1.1.4), zlibbioc(v.1.48.0), MASS(v.7.3-60.0.1), plyr(v.1.8.9), parallel(v.4.4.1), ggrepel(v.0.9.5), Biostrings(v.2.70.2), graphlayouts(v.1.1.0), splines(v.4.4.1), pander(v.0.6.5), locfit(v.1.5-9.8), igraph(v.2.0.2), reshape2(v.1.4.4), pkgload(v.1.3.4), gprofiler2(v.0.2.3), XML(v.3.99-0.16.1), evaluate(v.0.23), BiocManager(v.1.30.25), nloptr(v.2.0.3), foreach(v.1.5.2), tweenr(v.2.0.2), httpuv(v.1.6.14), tidyr(v.1.3.1), purrr(v.1.0.2), polyclip(v.1.10-6), ggplot2(v.3.5.0), ggforce(v.0.4.2), broom(v.1.0.5), xtable(v.1.8-4), tidytree(v.0.4.6), fANCOVA(v.0.6-1), later(v.1.3.2), viridisLite(v.0.4.2), tibble(v.3.2.1), lmerTest(v.3.1-3), clusterProfiler(v.4.10.1), aplot(v.0.2.2), memoise(v.2.0.1), AnnotationDbi(v.1.64.1), sva(v.3.50.0) and GSEABase(v.1.64.0)
message("This is hpgltools commit: ", get_git_commit())## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset ea2dd309f4d81efb8df7002085a68665ee192918## This is hpgltools commit: Fri Jan 3 16:53:12 2025 -0500: ea2dd309f4d81efb8df7002085a68665ee192918message("Saving to ", savefile)## Saving to 03differential_expression_both.rda.xz# tmp <- sm(saveme(filename = savefile))tmp <- loadme(filename = savefile)---
title: "TMRC3 `r Sys.getenv('VERSION')`: Differential Expression analyses"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
runtime: shiny
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
---

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

```{r options, include=FALSE}
library(hpgltools)
library(dplyr)
library(forcats)
library(glue)
library(DOSE)

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 <- Sys.getenv("VERSION")
parallel <- toupper(Sys.getenv("PARALLEL"))
if (parallel == "" || parallel == "TRUE") {
  parallel <- TRUE
} else {
  parallel <- FALSE
}
rundate <- format(Sys.Date(), format = "%Y%m%d")

rmd_file <- glue("03differential_expression_both.Rmd")
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
loaded <- load(file = glue("rda/tmrc3_data_structures-v{ver}.rda"))
xlsx_prefix <- "analyses/3_cali_and_tumaco"
clinic_prefix <- glue("{xlsx_prefix}/DE_Clinic")
clinic_cf_prefix <- glue("{xlsx_prefix}/DE_Clinic_Cure_Fail")
cf_prefix <- glue("{xlsx_prefix}/DE_Cure_Fail")
visit_prefix <- glue("{xlsx_prefix}/DE_Visits")
sex_prefix <- glue("{xlsx_prefix}/Sex")
eth_prefix <- glue("{xlsx_prefix}/Ethnicity")
gsea_prefix <- glue("{xlsx_prefix}/GSEA")
```

# Changelog

* 202309: Disabled GSVA analyses until/unless we get permission to
  include the mSigDB 7.5.1 release.  I will simplify the filenames so
  that one may easily drop in a downloaded copy of the data and run
  those blocks.  Until then, I guess you will have to trust me when I
  say those blocks all work?
* 202309: Moved all GSEA analyses to 04lrt_gsea_gsva.Rmd
* 202309 next day: Moving GSEA back because it adds too much
  complexity to save/reload the DE results for gProfiler and friends.
* Still hunting for messed up colors, changed input data to match new version.

# Introduction

The various differential expression analyses of the data generated in tmrc3_datasets
will occur in this document.

## Naming conventions

I am going to try to standardize how I name the various data
structures created in this document.  Most of the large data created
are either sets of differential expression analyses, their combined
results, or the set of results deemed 'significant'.

Hopefully by now they all follow these guidelines:

{clinic(s)}_sample-subset}_{primary-question(s)}_{datatype}_{batch-method}

* {clinic}: This is either tc or t for Tumaco and Cali, or just
Tumaco.
* {sample-subset}: Things like 'all' or 'monocytes'.
* {primary-question}: Shorthand name for the primary contrasts
performed, thus 'clinics' would suggest a comparison of Tumaco
vs. Cali.  'visits' would compare v2/v1, etc.
* {datatype}: de, table, sig
* {batch-type}: nobatch, batch{factor}, sva.  {factor} in this
instance should be a column from the metadata.

With this in mind, 'tc_biopsies_clinic_de_sva' should be the Tumaco+Cali
biopsy data after performing the differential expression analyses
comparing the clinics using sva.

I suspect there remain some exceptions and/or errors.

## Define contrasts for DE analyses

Each of the following lists describes the set of contrasts that I
think are interesting for the various ways one might consider the
TMRC3 dataset.  The variables are named according to the assumed data
with which they will be used, thus tc_cf_contrasts is expected to be
used for the Tumaco+Cali data and provide a series of cure/fail
comparisons which (to the extent possible) across both locations.  In
every case, the name of the list element will be used as the contrast
name, and will thus be seen as the sheet name in the output xlsx
file(s); the two pieces of the character vector value are the
numerator and denominator of the associated contrast.

## GSEA

The GSEA analyses will follow each DE analysis during this document.

Most (all?) of the GSEA analyses used in this paper were done via
gProfiler rather than goseq/clusterProfiler/topGO/GOstats.  Primarily
because it is so easy to invoke gprofiler.

```{r}
clinic_contrasts <- list(
  "clinics" = c("cali", "tumaco"))
## In some cases we have no Cali failure samples, so there remain only 2
## contrasts that are likely of interest
tc_cf_contrasts <- list(
  "tumaco" = c("tumaco_failure", "tumaco_cure"),
  "cure" = c("tumaco_cure", "cali_cure"))
## In other cases, we have cure/fail for both places.
clinic_cf_contrasts <- list(
  "cali" = c("cali_failure", "cali_cure"),
  "tumaco" = c("tumaco_failure", "tumaco_cure"),
  "cure" = c("tumaco_cure", "cali_cure"),
  "fail" = c("tumaco_failure", "cali_failure"))
cf_contrast <- list(
  "outcome" = c("tumaco_failure", "tumaco_cure"))
t_cf_contrast <- list(
  "outcome" = c("failure", "cure"))
visitcf_contrasts <- list(
  "v1cf" = c("v1_failure", "v1_cure"),
  "v2cf" = c("v2_failure", "v2_cure"),
  "v3cf" = c("v3_failure", "v3_cure"))
visit_contrasts <- list(
  "v2v1" = c("c2", "c1"),
  "v3v1" = c("c3", "c1"),
  "v3v2" = c("c3", "c2"))
visit_v1later <- list(
  "later_vs_first" = c("later", "first"))
celltypes <- list(
  "eo_mono" = c("eosinophils", "monocytes"),
  "ne_mono" = c("neutrophils", "monocytes"),
  "eo_ne" = c("eosinophils", "neutrophils"))
ethnicity_contrasts <- list(
  "mestizo_indigenous" = c("mestiza", "indigena"),
  "mestizo_afrocol" = c("mestiza", "afrocol"),
  "indigenous_afrocol" = c("indigena", "afrocol"))
```

# Compare samples by clinic

## DE: Compare clinics, all samples

Perform a svaseq-guided comparison of the two clinics.  Ideally this
will give some clue about just how strong the clinic-based batch
effect really is and what its causes are.

```{r}
tc_clinic_type <- tc_valid %>%
  set_expt_conditions(fact = "clinic") %>%
  set_expt_batches(fact = "typeofcells")

table(pData(tc_clinic_type)[["condition"]])
tc_all_clinic_de_sva <- all_pairwise(tc_clinic_type, model_batch = "svaseq",
                                     filter = TRUE, methods = methods)
tc_all_clinic_de_sva
tc_all_clinic_de_sva[["deseq"]][["contrasts_performed"]]

tc_all_clinic_table_sva <- combine_de_tables(
  tc_all_clinic_de_sva, keepers = clinic_contrasts,
  excel = glue("{clinic_prefix}/tc_all_clinic_table_sva-v{ver}.xlsx"))
tc_all_clinic_table_sva
tc_all_clinic_sig_sva <- extract_significant_genes(
  tc_all_clinic_table_sva,
  excel = glue("{clinic_prefix}/compare_clinics/tc_clinic_type_sig_sva-v{ver}.xlsx"))
tc_all_clinic_sig_sva
```

### GSEA: comparing the clinics

```{r}
increased_tumaco_categories_up <- simple_gprofiler(
  tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]],
  excel = glue("{gsea_prefix}/tumaco_cateogies_up-v{ver}.xlsx"))
increased_tumaco_categories_up
increased_tumaco_categories_up[["pvalue_plots"]][["BP"]]

increased_cali_categories <- simple_gprofiler(
  tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]],
  excel = glue("{gsea_prefix}/cali_cateogies_up-v{ver}.xlsx"))
increased_cali_categories
increased_cali_categories[["pvalue_plots"]][["BP"]]
```

### Visualize clinic differences

Let us take a quick look at the results of the comparison of
Tumaco/Cali

Note: I keep re-introducing an error which causes these (volcano and MA) plots to be
reversed with respect to the logFC values.  Pay careful attention to
these and make sure that they agree with the numbers of genes observed
in the contrast.

```{r}
## Check that up is up
summary(tc_all_clinic_table_sva[["data"]][["clinics"]][["deseq_logfc"]])
## I think we can assume that most genes are down when considering Tumaco/Cali.
sum(tc_all_clinic_table_sva$data$clinics$deseq_logfc < -1.0 &
      tc_all_clinic_table_sva$data$clinics$deseq_adjp < 0.05)
tc_all_clinic_table_sva[["plots"]][["clinics"]][["deseq_vol_plots"]]
## Ok, so it says 1794 up, but that is clearly the down side...  Something is definitely messed up.
## The points are on the correct sides of the plot, but the categories of up/down are reversed.
## Theresa noted that she colors differently, and I think better: left side gets called
## 'increased in denominator', right side gets called 'increased in numerator';
## these two groups are colored according to their condition colors, and everything else is gray.
## I am checking out Theresa's helper_functions.R to get a sense of how she handles this, I think
## I can use a variant of her idea pretty easily:
##  1.  Add a column 'Significance', which is a factor, and contains either 'Not enriched',
##      'Enriched in x', or 'Enriched in y' according to the logfc/adjp.
##  2.  use the significance column for the geom_point color/fill in the volcano plot.
## My change to this idea would be to extract the colors from the input expressionset.
```

There appear to be many more genes which are increased in the Tumaco
samples with respect to the Cali samples.

## DE: Compare clinics, eosinophil samples

The remaining cell types all have pretty strong clinic-based variance;
but I am not certain if it is consistent across cell types.

```{r}
table(pData(tc_eosinophils)[["condition"]])
tc_eosinophils_clinic_de_nobatch <- all_pairwise(tc_eosinophils, parallel = parallel,
                                                 model_batch = FALSE, filter = TRUE,
                                                 methods = methods)
tc_eosinophils_clinic_de_nobatch
tc_eosinophils_clinic_de_nobatch[["deseq"]][["contrasts_performed"]]

tc_eosinophils_clinic_table_nobatch <- combine_de_tables(
  tc_eosinophils_clinic_de_nobatch, keepers = tc_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))
tc_eosinophils_clinic_table_nobatch
tc_eosinophils_clinic_sig_nobatch <- extract_significant_genes(
  tc_eosinophils_clinic_table_nobatch,
  excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_nobatch

tc_eosinophils_clinic_de_sva <- all_pairwise(tc_eosinophils, model_batch = "svaseq",
                                             filter = TRUE, methods = methods)
tc_eosinophils_clinic_de_sva
tc_eosinophils_clinic_de_sva[["deseq"]][["contrasts_performed"]]

tc_eosinophils_clinic_table_sva <- combine_de_tables(
  tc_eosinophils_clinic_de_sva, keepers = tc_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_table_sva
tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
  tc_eosinophils_clinic_table_sva,
  excel = glue("{clinic_cf_prefix}/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_sva
```

## DE: Compare clinics, biopsy samples

Interestingly to me, the biopsy samples appear to have the least
location-based variance.  But we can perform an explicit DE and see
how well that hypothesis holds up.

Note that these data include cure and fail samples for

```{r}
table(pData(tc_biopsies)[["condition"]])
tc_biopsies_clinic_de_sva <- all_pairwise(tc_biopsies, parallel = parallel,
                                          model_batch = "svaseq", filter = TRUE,
                                          methods = methods)
tc_biopsies_clinic_de_sva
tc_biopsies_clinic_de_sva[["deseq"]][["contrasts_performed"]]

tc_biopsies_clinic_table_sva <- combine_de_tables(
  tc_biopsies_clinic_de_sva, keepers = tc_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Biopsies/tc_biopsies_clinic_table_sva-v{ver}.xlsx"))
tc_biopsies_clinic_table_sva
tc_biopsies_clinic_sig_sva <- extract_significant_genes(
  tc_biopsies_clinic_table_sva,
  excel = glue("{clinic_cf_prefix}/Biopsies/tc_biopsies_clinic_sig_sva-v{ver}.xlsx"))
tc_biopsies_clinic_sig_sva
```

## DE: Compare clinics, monocyte samples

At least for the moment, I am only looking at the differences between
no-batch vs. sva across clinics for the monocyte samples.  This
was chosen mostly arbitrarily.

### DE: Compare clinics, monocytes without batch estimation

Our baseline is the comparison of the monocytes samples without batch
in the model or surrogate estimation.  In theory at least, this should
correspond to the PCA plot above when no batch estimation was performed.

```{r}
table(pData(tc_monocytes)[["condition"]])
tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch = FALSE,
                                        filter = TRUE,
                                        methods = methods)
tc_monocytes_de_nobatch

tc_monocytes_table_nobatch <- combine_de_tables(
  tc_monocytes_de_nobatch, keepers = clinic_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_table_nobatch-v{ver}.xlsx"))
tc_monocytes_table_nobatch
tc_monocytes_sig_nobatch <- extract_significant_genes(
  tc_monocytes_table_nobatch,
  excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_sig_nobatch-v{ver}.xlsx"))
tc_monocytes_sig_nobatch
```

### DE: Compare clinics, monocytes with svaseq

In contrast, the following comparison should give a view of the data
corresponding to the svaseq PCA plot above.  In the best case
scenario, we should therefore be able to see some significane
differences between the Tumaco cure and fail samples.

```{r}
tc_monocytes_de_sva <- all_pairwise(tc_monocytes, model_batch = "svaseq",
                                    filter = TRUE,
                                    methods = methods)
tc_monocytes_de_sva

tc_monocytes_table_sva <- combine_de_tables(
  tc_monocytes_de_sva, keepers = clinic_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_table_sva-v{ver}.xlsx"))
tc_monocytes_table_sva
tc_monocytes_sig_sva <- extract_significant_genes(
  tc_monocytes_table_sva,
  excel = glue("{clinic_cf_prefix}/Monocytes/tc_monocytes_clinic_sig_sva-v{ver}.xlsx"))
tc_monocytes_sig_sva
```

### DE Compare: How similar are the no-batch vs. SVA results?

The following block shows that these two results are exceedingly
different, sugesting that the Cali cure/fail and Tumaco cure/fail
cannot easily be considered in the same analysis.  I did some playing
around with my calculate_aucc function in this block and found that it
is in some important way broken, at least if one expands the top-n
genes to more than 20% of the number of genes in the data.

```{r}
cali_table <- tc_monocytes_table_nobatch[["data"]][["cali"]]
table <- tc_monocytes_table_nobatch[["data"]][["tumaco"]]

cali_aucc <- calculate_aucc(cali_table, table, px = "deseq_adjp", py = "deseq_adjp",
                            lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc

cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]
cali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px = "deseq_adjp",
                                py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_sva
```

## DE: Compare clinics, neutrophil samples

```{r}
tc_neutrophils_de_nobatch <- all_pairwise(tc_neutrophils, parallel = parallel,
                                          model_batch = FALSE, filter = TRUE,
                                          methods = methods)
tc_neutrophils_de_nobatch

tc_neutrophils_table_nobatch <- combine_de_tables(
  tc_neutrophils_de_nobatch, keepers = clinic_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_table_nobatch-v{ver}.xlsx"))
tc_neutrophils_table_nobatch
tc_neutrophils_sig_nobatch <- extract_significant_genes(
  tc_neutrophils_table_nobatch,
  excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_sig_nobatch-v{ver}.xlsx"))
tc_neutrophils_sig_nobatch

tc_neutrophils_de_sva <- all_pairwise(tc_neutrophils, parallel = parallel,
                                      model_batch = "svaseq", filter = TRUE,
                                      methods = methods)
tc_neutrophils_de_sva

tc_neutrophils_table_sva <- combine_de_tables(
  tc_neutrophils_de_sva, keepers = clinic_cf_contrasts,
  excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_table_sva-v{ver}.xlsx"))
tc_neutrophils_table_sva
tc_neutrophils_sig_sva <- extract_significant_genes(
  tc_neutrophils_table_sva,
  excel = glue("{clinic_cf_prefix}/Neutrophils/tc_neutrophils_sig_sva-v{ver}.xlsx"))
tc_neutrophils_sig_sva
```

## GSEA: Extract clinic-specific genes

Given the above comparisons, we can extract some gene sets which
resulted from those DE analyses and eventually perform some
ontology/KEGG/reactome/etc searches.  This reminds me, I want to make
my extract_significant_ functions to return gene-set data structures
and my various ontology searches to take them as inputs.  This should
help avoid potential errors when extracting up/down genes.

```{r}
clinic_sigenes_up <- rownames(tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]])
clinic_sigenes_down <- rownames(tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]])
clinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)

tc_eosinophils_sigenes_up <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_eosinophils_sigenes_down <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_monocytes_sigenes_up <- rownames(tc_monocytes_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_monocytes_sigenes_down <- rownames(tc_monocytes_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_neutrophils_sigenes_up <- rownames(tc_neutrophils_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_neutrophils_sigenes_down <- rownames(tc_neutrophils_sig_sva[["deseq"]][["downs"]][["cure"]])

tc_eosinophils_sigenes <- c(tc_eosinophils_sigenes_up,
                            tc_eosinophils_sigenes_down)
tc_monocytes_sigenes <- c(tc_monocytes_sigenes_up,
                          tc_monocytes_sigenes_down)
tc_neutrophils_sigenes <- c(tc_neutrophils_sigenes_up,
                            tc_neutrophils_sigenes_down)
```

## GSEA: gProfiler of genes deemed up/down when comparing Cali and Tumaco

I was curious to try to understand why the two clinics appear to be so
different vis a vis their PCA/DE; so I thought that gProfiler might
help boil those results down to something more digestible.

### GSEA: Compare clinics, all samples

Note that in the following block I used the function
simple_gprofiler(), but later in this document I will use
all_gprofiler().  The first invocation limits the search to a single
table, while the second will iterate over every result in a pairwise
differential expression analysis.

In this instance, we are looking at the vector of gene IDs deemed
significantly different between the two clinics in either the up or
down direction.

One other thing worth noting, the new version of gProfiler provides
some fun interactive plots.  I will add an example here.

```{r}
tc_eosinophil_gprofiler <- simple_gprofiler(
  tc_eosinophils_sigenes_up,
  excel = glue("{gsea_prefix}/eosinophil_clinics_tumaco_up-v{ver}.xlsx"))
tc_eosinophil_gprofiler

clinic_gp <- simple_gprofiler(
  clinic_sigenes,
  excel = glue("{gsea_prefix}/both_clinics_cali_up-v{ver}.xlsx"))
clinic_gp$pvalue_plots$REAC
clinic_gp$pvalue_plots$BP
clinic_gp$pvalue_plots$TF
clinic_gp$interactive_plots$GO
```

### GSEA: Compare clinics, Eosinophil samples

In the following block, I am looking at the gProfiler over represented
groups observed across clinics in only the Eosinophils.  First I do so
for all genes(up or down), followed by only the up and down groups.
Each of the following will include only the Reactome and GO:BP plots.
These searches did not have too many other hits, excepting the
transcription factor database.

```{r}
tc_eosinophils_gp <- simple_gprofiler(
  tc_eosinophils_sigenes,
  excel = glue("{gsea_prefix}/eosinophil_clinics-v{ver}.xlsx"))
tc_eosinophils_gp
tc_eosinophils_gp$pvalue_plots$REAC
tc_eosinophils_gp$pvalue_plots$BP

tc_eosinophils_up_gp <- simple_gprofiler(
  tc_eosinophils_sigenes_up,
  excel = glue("{gsea_prefix}/eosinophil_clinics_tumaco_up-v{ver}.xlsx"))
tc_eosinophils_up_gp
tc_eosinophils_up_gp$pvalue_plots$REAC

tc_eosinophils_down_gp <- simple_gprofiler(
  tc_eosinophils_sigenes_down,
  excel = glue("{gsea_prefix}/eosinophil_clinics_cali_up-v{ver}.xlsx"))
tc_eosinophils_down_gp
tc_eosinophils_down_gp$pvalue_plots$REAC
```

### GSEA: Compare clinics, Monocyte samples

In the following block I repeated the above query, but this time
looking at the monocyte samples.

```{r}
tc_monocytes_up_gp <- simple_gprofiler(
  tc_monocytes_sigenes,
  excel = glue("{gsea_prefix}/monocyte_clinics-v{ver}.xlsx"))
tc_monocytes_up_gp
tc_monocytes_up_gp$pvalue_plots$REAC
tc_monocytes_up_gp$pvalue_plots$BP

tc_monocytes_down_gp <- simple_gprofiler(
  tc_monocytes_sigenes_down,
  excel = glue("{gsea_prefix}/monocyte_clinics_cali_up-v{ver}.xlsx"))
tc_monocytes_down_gp$pvalue_plots$REAC
tc_monocytes_down_gp$pvalue_plots$BP
```

#### GSEA: Compare clinics, Neutrophil samples

Ibid.  This time looking at the Neutrophils.  Thus the first two
images should be a superset of the second and third pairs of images;
assuming that the genes in the up/down list do not cause the groups to
no longer be significant.  Interestingly, the reactome search did not
return any hits for the increased search.

```{r}
tc_neutrophils_gp <- simple_gprofiler(
  tc_neutrophils_sigenes,
  excel = glue("{gsea_prefix}/neutrophil_clinics-v{ver}.xlsx"))
## tc_neutrophils_gp$pvalue_plots$REAC ## no hits
tc_neutrophils_gp$pvalue_plots$BP
tc_neutrophils_gp$pvalue_plots$TF

tc_neutrophils_up_gp <- simple_gprofiler(
  tc_neutrophils_sigenes_up,
  excel = glue("{gsea_prefix}/neutrophil_clinics_tumaco_up-v{ver}.xlsx"))
## tc_neutrophils_up_gp$pvalue_plots$REAC ## No hits
tc_neutrophils_up_gp$pvalue_plots$BP

tc_neutrophils_down_gp <- simple_gprofiler(
  tc_neutrophils_sigenes_down,
  excel = glue("{gsea_prefix}/neutrophil_clinics_cali_up-v{ver}.xlsx"))
tc_neutrophils_down_gp$pvalue_plots$REAC
tc_neutrophils_down_gp$pvalue_plots$BP
```

# Compare DE: How similar are Tumaco C/F vs. Cali C/F

The following expands the cross-clinic query above to also test the
neutrophils.  Once again, I think it will pretty strongly support the
hypothesis that the two clinics are not compatible.

We are concerned that the clinic-based batch effect may make our
results essentially useless.  One way to test this concern is to
compare the set of genes observed different between the Cali Cure/Fail
vs. the Tumaco Cure/Fail.

```{r}
cali_table_nobatch <- tc_neutrophils_table_nobatch[["data"]][["cali"]]
tumaco_table_nobatch <- tc_neutrophils_table_nobatch[["data"]][["tumaco"]]

cali_merged_nobatch <- merge(cali_table_nobatch, tumaco_table_nobatch, by="row.names")
cor.test(cali_merged_nobatch[, "deseq_logfc.x"], cali_merged_nobatch[, "deseq_logfc.y"])
cali_aucc_nobatch <- calculate_aucc(cali_table_nobatch, tumaco_table_nobatch, px = "deseq_adjp",
                                    py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_nobatch$plot
```

# Tumaco and Cali, cure vs. fail

In all of the above, we are looking to understand the differences between the two location.
Let us now step back and perform the original question: fail/cure without regard to location.

I performed this query with a few different parameters, notably with(out)
sva and again using each cell type, including biopsies. The main
reasion I am keeping these comparisons is in the relatively weak hope
that there will be sufficient signal in the full dataset that it might
be able to overcome the apparently ridiculous batch effect from the
two clinics.

## All cell types together, with(out) SVA

```{r}
table(pData(tc_valid)[["condition"]])
tc_all_cf_de_sva <- all_pairwise(tc_valid, filter = TRUE, methods = methods,
                                 model_batch = "svaseq")
tc_all_cf_table_sva <- combine_de_tables(
  tc_all_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_table_sva-v{ver}.xlsx"))
tc_all_cf_sig_sva <- extract_significant_genes(
  tc_all_cf_table_sva,
  excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_sig_sva-v{ver}.xlsx"))

tc_all_cf_de_batch <- all_pairwise(tc_valid, filter = TRUE, methods = methods,
                                   model_batch = TRUE)
tc_all_cf_table_batch <- combine_de_tables(
  tc_all_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_table_batch-v{ver}.xlsx"))
tc_all_cf_sig_batch <- extract_significant_genes(
  tc_all_cf_table_batch,
  excel = glue("{cf_prefix}/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))
```

## All cell types except biopsies

I am not sure if this is the best choice, but I call the set of all
samples excluding biopsies 'clinical'.

```{r}
table(pData(tc_clinical_nobiop)[["condition"]])
tc_clinical_cf_de_sva <- all_pairwise(tc_clinical_nobiop, filter = TRUE,
                                      model_batch = "svaseq",
                                      methods = methods)
tc_clinical_cf_de_sva

tc_clinical_cf_table_sva <- combine_de_tables(
  tc_clinical_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_table_sva-v{ver}.xlsx"))
tc_clinical_cf_table_sva

tc_clinical_cf_sig_sva <- extract_significant_genes(
  tc_clinical_cf_table_sva, according_to = "deseq",
  excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_sig_sva-v{ver}.xlsx"))
tc_clinical_cf_sig_sva

tc_clinical_cf_de_batch <- all_pairwise(tc_clinical_nobiop, filter = TRUE,
                                        model_batch = TRUE,
                                        methods = methods)
tc_clinical_cf_de_batch

tc_clinical_cf_table_batch <- combine_de_tables(
  tc_clinical_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_table_batch-v{ver}.xlsx"))
tc_clinical_cf_table_batch

tc_clinical_cf_sig_batch <- extract_significant_genes(
  tc_clinical_cf_table_batch, according_to = "deseq",
  excel = glue("{cf_prefix}/Clinical_Samples/tc_clinical_cf_sig_batch-v{ver}.xlsx"))
tc_clinical_cf_sig_batch
```

### A portion of Supplemental Figure 11.

```{r}
num_color <- color_choices[["cf"]][["cure"]]
den_color <- color_choices[["cf"]][["failure"]]
tc_clinical_cf_table <- tc_clinical_cf_table_sva[["data"]][["outcome"]]
tc_clinical_cf_volcano_top10 <- plot_volcano_condition_de(
  tc_clinical_cf_table, "outcome", label = 10,
  fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
  color_high = num_color, color_low = den_color, label_size = 6)
pp(file = "figures/s11c_tc_clinical_cf_volcano_labeled_top10.svg")
tc_clinical_cf_volcano_top10[["plot"]]
dev.off()
tc_clinical_cf_volcano_top10[["plot"]]
```

## Biopsies, with(out) SVA

In the following block, we repeat the same question, but using only
the biopsy samples from both clinics.

```{r}
tc_biopsies_cf <- set_expt_conditions(tc_biopsies, fact = "finaloutcome")
tc_biopsies_cf_de_sva <- all_pairwise(tc_biopsies_cf, filter = TRUE, methods = methods,
                                      model_batch = "svaseq")
tc_biopsies_cf_table_sva <- combine_de_tables(
  tc_biopsies_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))
tc_biopsies_cf_sig_sva <- extract_significant_genes(
  tc_biopsies_cf_table_sva,
  excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))

tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter = TRUE, methods = methods,
                                        model_batch = TRUE)
tc_biopsies_cf_table_batch <- combine_de_tables(
  tc_biopsies_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_table_batch-v{ver}.xlsx"))
tc_biopsies_cf_sig_batch <- extract_significant_genes(
  tc_biopsies_cf_table_batch,
  excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))
```

## Eosinophils, with(out) SVA

In the following block, we repeat the same question, but using only
the Eosinophil samples from both clinics.

```{r}
tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact = "finaloutcome")
tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter = TRUE, methods = methods,
                                         model_batch = "svaseq")
tc_eosinophils_cf_table_sva <- combine_de_tables(
  tc_eosinophils_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Eosinophils/tc_eosinophils_cf_table_sva-v{ver}.xlsx"))
tc_eosinophils_cf_sig_sva <- extract_significant_genes(
  tc_eosinophils_cf_table_sva,
  excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_sig_sva-v{ver}.xlsx"))

tc_eosinophils_cf_de_batch <- all_pairwise(tc_eosinophils_cf, filter = TRUE,
                                           model_batch = TRUE,
                                           methods = methods)
tc_eosinophils_cf_table_batch <- combine_de_tables(
  tc_eosinophils_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))
tc_eosinophils_cf_sig_batch <- extract_significant_genes(
  tc_eosinophils_cf_table_batch,
  excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))
```

## Monocytes, with(out) SVA

Repeat yet again, this time with the monocyte samples.  The idea is to
see if there is a cell type which is particularly good (or bad) at
discriminating the two clinics.

```{r}
tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact = "finaloutcome")
tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
                                       model_batch = "svaseq")
tc_monocytes_cf_table_sva <- combine_de_tables(
  tc_monocytes_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))
tc_monocytes_cf_sig_sva <- extract_significant_genes(
  tc_monocytes_cf_table_sva,
  excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))

tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
                                         model_batch = TRUE)
tc_monocytes_cf_table_batch <- combine_de_tables(
  tc_monocytes_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_table_batch-v{ver}.xlsx"))
tc_monocytes_cf_sig_batch <- extract_significant_genes(
  tc_monocytes_cf_table_batch,
  excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_sig_batch-v{ver}.xlsx"))
```

## Neutrophils, with(out) SVA

Last try, this time using the Neutrophil samples.

```{r}
tc_neutrophils_cf <- set_expt_conditions(tc_neutrophils, fact = "finaloutcome")
tc_neutrophils_cf_de_sva <- all_pairwise(tc_neutrophils_cf, parallel = parallel,
                                         filter = TRUE, model_batch = "svaseq",
                                         methods = methods)
tc_neutrophils_cf_table_sva <- combine_de_tables(
  tc_neutrophils_cf_de_sva, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))
tc_neutrophils_cf_sig_sva <- extract_significant_genes(
  tc_neutrophils_cf_table_sva,
  excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_sig_sva-v{ver}.xlsx"))

tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter = TRUE,
                                           model_batch = TRUE,
                                           methods = methods)
tc_neutrophils_cf_table_batch <- combine_de_tables(
  tc_neutrophils_cf_de_batch, keepers = t_cf_contrast,
  excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_table_batch-v{ver}.xlsx"))
tc_neutrophils_cf_sig_batch <- extract_significant_genes(
  tc_neutrophils_cf_table_batch,
  excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))
```

# Visit comparisons

Later in this document I do a bunch of visit/cf comparisons.  In this
block I want to explicitly only compare v1 to other visits.  This is
something I did quite a lot in the 2019 datasets, but never actually
moved to this document.

```{r}
v1_vs_later <- all_pairwise(tc_v1vs, model_batch = "svaseq", methods = methods,
                            filter = TRUE)
v1_vs_later_table <- combine_de_tables(
  v1_vs_later, keepers = visit_v1later,
  excel = glue("{visit_prefix}/v1_vs_later_tables-v{ver}.xlsx"))
v1_vs_later_sig <- extract_significant_genes(
  v1_vs_later_table,
  excel = glue("{visit_prefix}/v1_vs_later_sig-v{ver}.xlsx"))
```

#### GSEA: V1 vs other visits.

```{r}
v1later_gp <- all_gprofiler(v1_vs_later_sig)
v1later_gp[[1]]$pvalue_plots$REAC
v1later_gp[[2]]$pvalue_plots$REAC
```

# Sex comparison

```{r}
tc_sex_de <- all_pairwise(tc_sex, model_batch = "svaseq", methods = methods,
                          filter = TRUE)
tc_sex_table <- combine_de_tables(
  tc_sex_de, excel = glue("{sex_prefix}/tc_sex_table-v{ver}.xlsx"))
tc_sex_sig <- extract_significant_genes(
  tc_sex_table, excel = glue("{sex_prefix}/tc_sex_sig-v{ver}.xlsx"))
tc_sex_gp <- all_gprofiler(tc_sex_sig)
```

```{r}
tc_sex_cure <- subset_expt(tc_sex, subset = "finaloutcome=='cure'")
tc_sex_cure_de <- all_pairwise(tc_sex_cure, model_batch = "svaseq",
                               filter = TRUE,
                               methods = methods)
tc_sex_cure_de
tc_sex_cure_table <- combine_de_tables(
  tc_sex_cure_de, excel = glue("{sex_prefix}/tc_sex_cure_table-v{ver}.xlsx"))
tc_sex_cure_table
tc_sex_cure_sig <- extract_significant_genes(
  tc_sex_cure_table, excel = glue("{sex_prefix}/tc_sex_cure_sig-v{ver}.xlsx"))
tc_sex_cure_sig
```

#### GSEA: Sex comparisons both clinics

```{r}
tc_sex_cure_gp <- all_gprofiler(tc_sex_cure_sig)
tc_sex_cure_gp
tc_sex_cure_gp[[1]][["pvalue_plots"]][["BP"]]
tc_sex_cure_gp[[2]][["pvalue_plots"]][["BP"]]
```

# Ethnicity comparisons

```{r}
tc_ethnicity_de <- all_pairwise(tc_etnia_expt, model_batch = "svaseq",
                                filter = TRUE,
                                methods = methods)
tc_ethnicity_de
tc_ethnicity_table <- combine_de_tables(
  tc_ethnicity_de, keepers = ethnicity_contrasts,
  excel = glue("{eth_prefix}/tc_ethnicity_table-v{ver}.xlsx"))
tc_ethnicity_table
tc_ethnicity_table[["plots"]][["mestizo_indigenous"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["mestizo_afrocol"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["indigenous_afrocol"]][["deseq_ma_plots"]]

tc_ethnicity_sig <- extract_significant_genes(
  tc_ethnicity_table, excel = glue("{eth_prefix}/tc_ethnicity_sig-v{ver}.xlsx"))

ethnicity_cure <- subset_expt(tc_etnia_expt, subset = "finaloutcome=='cure'")
ethnicity_cure_de <- all_pairwise(ethnicity_cure, model_batch = "svaseq",
                                  filter = TRUE,
                                  methods = methods)
ethnicity_cure_table <- combine_de_tables(
  ethnicity_cure_de, keepers = ethnicity_contrasts,
  excel = glue("{eth_prefix}/ethnicity_cure_table-v{ver}.xlsx"))
ethnicity_cure_table
ethnicity_cure_table[["plots"]][["mestizo_indigenous"]][["deseq_ma_plots"]]
ethnicity_cure_table[["plots"]][["mestizo_afrocol"]][["deseq_ma_plots"]]
ethnicity_cure_table[["plots"]][["indigenous_afrocol"]][["deseq_ma_plots"]]
ethnicity_cure_sig <- extract_significant_genes(
  ethnicity_cure_table, excel = glue("{eth_prefix}/ethnicity_cure_sig-v{ver}.xlsx"))
ethnicity_cure_sig
```

#### GSEA: Ethnicity differences

Performed once with both clinics and again with only Tumaco.

```{r}
tc_ethnicity_gp <- all_gprofiler(tc_ethnicity_sig)
```

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

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