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
Most (all?) of the overenrichment/GSEA analyses used in this paper were done via gProfiler and clusterProfiler rather than goseq/topGO/GOstats. Primarily because it is so easy to invoke gprofiler and because clusterProfiler makes it very easy to invoke the GSEA analyses. One fun thing I did relatively recently is coerce the results from all methods into the clusterProfiler enrichment object type, thus you may pass any result directly to the various enrichplot functions.
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 0.7977
## basic_vs_dream 0.9210
## basic_vs_ebseq 0.7545
## basic_vs_edger 0.8733
## basic_vs_limma 0.9696
## basic_vs_noiseq 0.9114
## deseq_vs_dream 0.8696
## deseq_vs_ebseq 0.8431
## deseq_vs_edger 0.9262
## deseq_vs_limma 0.7895
## deseq_vs_noiseq 0.8657
## dream_vs_ebseq 0.8550
## dream_vs_edger 0.9442
## dream_vs_limma 0.9372
## dream_vs_noiseq 0.9353
## ebseq_vs_edger 0.8612
## ebseq_vs_limma 0.7772
## ebseq_vs_noiseq 0.8376
## edger_vs_limma 0.8623
## edger_vs_noiseq 0.9364
## limma_vs_noiseq 0.8900tc_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 270 1788 322 1660
## limma_sigup limma_sigdown
## 1 388 604## `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 388 604 322 1660 270 1788 222
## ebseq_down basic_up basic_down
## clinics 420 0 5902
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 270
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 17 MF
## 5 BP
## 1 CC
## 1 KEGG
## 1 REAC
## 0 WP
## 105 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 1788
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 59 MF
## 689 BP
## 89 CC
## 2 KEGG
## 20 REAC
## 8 WP
## 356 TF
## 2 MIRNA
## 18 HPA
## 0 CORUM
## 15 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.
I eventually took some code from Theresa which more intelligently colors the sides of MA/volcano plots to be the same colors as their corresponding numerator/denominator.
## Check that up is up
summary(tc_all_clinic_table_sva[["data"]][["clinics"]][["deseq_logfc"]])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -25.270 -0.583 -0.156 -0.255 0.171 3.483## 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] 1788tc_all_clinic_table_sva[["plots"]][["clinics"]][["deseq_vol_plots"]]
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 828 811 778
## edger_sigdown limma_sigup limma_sigdown
## 1 31 61 17
## 2 885 711 700## 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 61 17 114 31 102 35 7
## cure 711 700 778 885 828 811 694
## ebseq_down basic_up basic_down
## tumaco 36 10 0
## cure 596 5543 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 111 61 114
## 2 tumaco_cure_vs_cali_cure 785 861 716
## edger_sigdown limma_sigup limma_sigdown
## 1 36 74 35
## 2 925 734 672## 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 74 35 114 36 111 61 7
## cure 734 672 716 925 785 861 694
## ebseq_down basic_up basic_down
## tumaco 36 10 0
## cure 596 5543 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 27
## 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## 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 48 120 60
## 3 tumaco_cure_vs_cali_cure 781 724 773
## 4 tumaco_failure_vs_cali_failure 633 488 515
## edger_sigdown limma_sigup limma_sigdown
## 1 13 38 5
## 2 138 23 37
## 3 779 644 713
## 4 535 395 564## 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 23 37 60 138 48 120 0
## cure 644 713 773 779 781 724 642
## fail 395 564 515 535 633 488 166
## ebseq_down basic_up basic_down
## cali 23 0 0
## tumaco 23 339 0
## cure 660 6378 0
## fail 525 2160 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 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## 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 27 36 40
## 2 tumaco_failure_vs_tumaco_cure 34 88 29
## 3 tumaco_cure_vs_cali_cure 763 728 711
## 4 tumaco_failure_vs_cali_failure 684 583 576
## edger_sigdown limma_sigup limma_sigdown
## 1 17 51 7
## 2 71 15 57
## 3 758 640 663
## 4 615 430 553## 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 51 7 40 17 27 36 92
## tumaco 15 57 29 71 34 88 0
## cure 640 663 711 758 763 728 642
## fail 430 553 576 615 684 583 166
## ebseq_down basic_up basic_down
## cali 23 0 0
## tumaco 23 339 0
## cure 660 6378 0
## fail 525 2160 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: 0.0659989114452595 and correlation:##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 1.3, df = 11084, p-value = 0.2
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.005944 0.031280
## sample estimates:
## cor
## 0.01267
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: 0.0846505658047588 and correlation:##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 17, df = 11084, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1396 0.1759
## sample estimates:
## cor
## 0.1578
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
## 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 85 42
## 2 tumaco_failure_vs_tumaco_cure 95 49 112
## 3 tumaco_cure_vs_cali_cure 905 337 913
## 4 tumaco_failure_vs_cali_failure 983 256 803
## edger_sigdown limma_sigup limma_sigdown
## 1 33 37 10
## 2 55 7 12
## 3 355 627 520
## 4 281 380 460## 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 33 33 85 90
## tumaco 7 12 112 55 95 49 7
## cure 627 520 913 355 905 337 683
## fail 380 460 803 281 983 256 113
## ebseq_down basic_up basic_down
## cali 39 0 0
## tumaco 7 8 0
## cure 299 4589 0
## fail 310 1652 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
## 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 88 183 102
## 2 tumaco_failure_vs_tumaco_cure 92 42 80
## 3 tumaco_cure_vs_cali_cure 853 379 831
## 4 tumaco_failure_vs_cali_failure 704 201 611
## edger_sigdown limma_sigup limma_sigdown
## 1 121 76 49
## 2 24 44 51
## 3 384 650 486
## 4 220 312 332## 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 76 49 102 121 88 183 90
## tumaco 44 51 80 24 92 42 7
## cure 650 486 831 384 853 379 683
## fail 312 332 611 220 704 201 113
## ebseq_down basic_up basic_down
## cali 39 0 0
## tumaco 7 8 0
## cure 299 4589 0
## fail 310 1652 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 785
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 21 MF
## 213 BP
## 16 CC
## 0 KEGG
## 3 REAC
## 0 WP
## 549 TF
## 11 MIRNA
## 0 HPA
## 4 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 1646
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 39 MF
## 281 BP
## 50 CC
## 0 KEGG
## 0 REAC
## 0 WP
## 582 TF
## 13 MIRNA
## 0 HPA
## 5 CORUM
## 0 HP hits.tc_eosinophils_gp$pvalue_plots$REAC## NULLtc_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 785
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 21 MF
## 213 BP
## 16 CC
## 0 KEGG
## 3 REAC
## 0 WP
## 549 TF
## 11 MIRNA
## 0 HPA
## 4 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 861
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 12 MF
## 126 BP
## 70 CC
## 3 KEGG
## 10 REAC
## 3 WP
## 70 TF
## 1 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 1491
## genes against the hsapiens annotations and significance cutoff 0.05.
## There are:
## 57 MF
## 480 BP
## 29 CC
## 0 KEGG
## 5 REAC
## 6 WP
## 545 TF
## 4 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 = 9229, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1821 -0.1423
## sample estimates:
## cor
## -0.1623cali_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$plot
5 Tumaco and Cali, cure vs. fail
In all of the above, we are looking to understand the differences between the two locations. Let us now step back and perform the original question: fail/cure without regard to location.
I performed this query with a few different parameters, notably with(out) sva and again using each cell type, including biopsies. The main reasion I am keeping these comparisons is in the relatively weak hope that there will be sufficient signal in the full dataset that it might be able to overcome the apparently ridiculous batch effect from the two clinics.
5.1 All cell types together, with(out) SVA
table(pData(tc_valid)[["condition"]])##
## cure failure
## 122 62tc_all_cf_de_sva <- all_pairwise(tc_valid, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 122 62## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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 0.7595
## basic_vs_edger 0.9007
## basic_vs_limma 0.9415
## basic_vs_noiseq 0.9213
## deseq_vs_dream 0.8999
## deseq_vs_ebseq 0.8271
## deseq_vs_edger 0.9919
## deseq_vs_limma 0.8936
## deseq_vs_noiseq 0.9409
## dream_vs_ebseq 0.8102
## dream_vs_edger 0.9024
## dream_vs_limma 0.9855
## dream_vs_noiseq 0.8709
## ebseq_vs_edger 0.8188
## ebseq_vs_limma 0.8184
## ebseq_vs_noiseq 0.8312
## edger_vs_limma 0.8971
## edger_vs_noiseq 0.9517
## limma_vs_noiseq 0.8733tc_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 181 93 209 92
## limma_sigup limma_sigdown
## 1 96 78## `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 181 93
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 0.6444
## basic_vs_dream 0.7017
## basic_vs_ebseq 0.7595
## basic_vs_edger 0.6445
## basic_vs_limma 0.7122
## basic_vs_noiseq 0.9213
## deseq_vs_dream 0.8060
## deseq_vs_ebseq 0.7628
## deseq_vs_edger 0.9991
## deseq_vs_limma 0.8096
## deseq_vs_noiseq 0.7631
## dream_vs_ebseq 0.6730
## dream_vs_edger 0.8114
## dream_vs_limma 0.9716
## dream_vs_noiseq 0.6695
## ebseq_vs_edger 0.7626
## ebseq_vs_limma 0.6897
## ebseq_vs_noiseq 0.8312
## edger_vs_limma 0.8140
## edger_vs_noiseq 0.7631
## limma_vs_noiseq 0.6685tc_clinical_cf_table_batch <- combine_de_tables(
tc_clinical_cf_de_batch, keepers = t_cf_contrast, label_column = "hgnc_symbol",
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 104 68 114 73
## limma_sigup limma_sigdown
## 1 81 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 104 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$best_libsize.tc_biopsies_cf_table_sva <- combine_de_tables(
tc_biopsies_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))
tc_biopsies_cf_sig_sva <- extract_significant_genes(
tc_biopsies_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))
tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter = TRUE, methods = methods,
model_batch = TRUE)##
## cure failure
## 13 5
##
## v1
## 18## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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$best_libsize.tc_biopsies_cf_table_batch <- combine_de_tables(
tc_biopsies_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_table_batch-v{ver}.xlsx"))
tc_biopsies_cf_sig_batch <- extract_significant_genes(
tc_biopsies_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))5.4 Eosinophils, with(out) SVA
In the following block, we repeat the same question, but using only the Eosinophil samples from both clinics.
tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 32 9tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 32 9## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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$best_libsize.tc_eosinophils_cf_table_batch <- combine_de_tables(
tc_eosinophils_cf_de_batch, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))
tc_eosinophils_cf_sig_batch <- extract_significant_genes(
tc_eosinophils_cf_table_batch,
excel = glue("{cf_prefix}/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))5.5 Monocytes, with(out) SVA
Repeat yet again, this time with the monocyte samples. The idea is to see if there is a cell type which is particularly good (or bad) at discriminating the two clinics.
tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact = "finaloutcome")## The numbers of samples by condition are:##
## cure failure
## 39 24tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
model_batch = "svaseq")##
## cure failure
## 39 24## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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$best_libsize.tc_monocytes_cf_table_sva <- combine_de_tables(
tc_monocytes_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))
tc_monocytes_cf_sig_sva <- extract_significant_genes(
tc_monocytes_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))
tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter = TRUE, methods = methods,
model_batch = TRUE)##
## cure failure
## 39 24
##
## v3 v2 v1
## 19 18 26## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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## 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$best_libsize.tc_neutrophils_cf_table_sva <- combine_de_tables(
tc_neutrophils_cf_de_sva, keepers = t_cf_contrast,
excel = glue("{cf_prefix}/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))
tc_neutrophils_cf_sig_sva <- extract_significant_genes(
tc_neutrophils_cf_table_sva,
excel = glue("{cf_prefix}/All_Samples/tc_neutrophils_cf_sig_sva-v{ver}.xlsx"))
tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter = TRUE,
model_batch = TRUE,
methods = methods)##
## cure failure
## 38 24
##
## v3 v2 v1
## 19 18 25## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = as.numeric(xdata[j, ]), y =
## as.numeric(ydata[j, : cannot compute exact p-value with ties## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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 0.6829
## basic_vs_dream 0.9094
## basic_vs_ebseq 0.5372
## basic_vs_edger 0.7974
## basic_vs_limma 0.9066
## basic_vs_noiseq 0.8141
## deseq_vs_dream 0.6847
## deseq_vs_ebseq 0.6539
## deseq_vs_edger 0.8760
## deseq_vs_limma 0.6556
## deseq_vs_noiseq 0.7614
## dream_vs_ebseq 0.6272
## dream_vs_edger 0.8040
## dream_vs_limma 0.9643
## dream_vs_noiseq 0.7544
## ebseq_vs_edger 0.6871
## ebseq_vs_limma 0.5873
## ebseq_vs_noiseq 0.6317
## edger_vs_limma 0.7733
## edger_vs_noiseq 0.8654
## limma_vs_noiseq 0.7144tc_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 68 74 62 80
## limma_sigup limma_sigdown
## 1 37 73## `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 37 73 62 80 68 74
## ebseq_up ebseq_down basic_up basic_down
## male_vs_female 9 8 11 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 CC CORUM HP HPA KEGG MIRNA MF REAC TF WP
## male_vs_female_up 3 2 0 1 0 1 0 1 0 4 1
## male_vs_female_down 3 0 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$best_libsize.tc_ethnicity_de## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 21 comparisons.tc_ethnicity_table <- combine_de_tables(
tc_ethnicity_de, keepers = ethnicity_contrasts,
excel = glue("{eth_prefix}/tc_ethnicity_table-v{ver}.xlsx"))
tc_ethnicity_table## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown
## 1 mestiza_vs_indigena 47 22 54 23
## 2 mestiza_vs_afrocol 53 165 53 180
## 3 indigena_vs_afrocol 66 269 71 279
## limma_sigup limma_sigdown
## 1 23 14
## 2 41 90
## 3 75 143## Plot describing unique/shared genes in a differential expression table.
tc_ethnicity_table[["plots"]][["mestizo_indigenous"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["mestizo_afrocol"]][["deseq_ma_plots"]]
tc_ethnicity_table[["plots"]][["indigenous_afrocol"]][["deseq_ma_plots"]]
tc_ethnicity_sig <- extract_significant_genes(
tc_ethnicity_table, excel = glue("{eth_prefix}/tc_ethnicity_sig-v{ver}.xlsx"))
ethnicity_cure <- subset_expt(tc_etnia_expt, subset = "finaloutcome=='cure'")## subset_expt(): There were 184, now there are 122 samples.ethnicity_cure_de <- all_pairwise(ethnicity_cure, model_batch = "svaseq",
filter = TRUE,
methods = methods)##
## afrocol indigena mestiza
## 39 36 47## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$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 63 24 59 26
## 2 mestiza_vs_afrocol 66 167 76 165
## 3 indigena_vs_afrocol 86 350 94 340
## limma_sigup limma_sigdown
## 1 36 16
## 2 61 101
## 3 108 177## 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 16 59 26 63 24
## mestizo_afrocol 61 101 76 165 66 167
## indigenous_afrocol 108 177 94 340 86 350
## ebseq_up ebseq_down basic_up basic_down
## mestizo_indigenous 10 0 6 0
## mestizo_afrocol 5 16 44 0
## indigenous_afrocol 9 38 433 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 4664b0b9440028c89307a154494579648c639ae8the## This is hpgltools commit: Fri Mar 7 10:26:11 2025 -0500: 4664b0b9440028c89307a154494579648c639ae8Fri Mar 7 10:26:11 2025 -0500: themessage("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

Most (all?) of the overenrichment/GSEA analyses used in this paper
were done via gProfiler and clusterProfiler rather than
goseq/topGO/GOstats.  Primarily because it is so easy to invoke
gprofiler and because clusterProfiler makes it very easy to invoke the
GSEA analyses.  One fun thing I did relatively recently is coerce the
results from all methods into the clusterProfiler enrichment object
type, thus you may pass any result directly to the various enrichplot
functions.

```{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.

I eventually took some code from Theresa which more intelligently
colors the sides of MA/volcano plots to be the same colors as their
corresponding numerator/denominator.

```{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"]]
```

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 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 locations. 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, label_column = "hgnc_symbol",
  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)
```
