1 Introduction
The various differential expression analyses of the data generated in tmrc3_datasets will occur in this document.
1.1 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.
1.2 Define contrasts for DE analyses
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("Tumacofailure", "Tumacocure"),
"cure" = c("Tumacocure", "Calicure"))
## In other cases, we have cure/fail for both places.
clinic_cf_contrasts <- list(
"cali" = c("Califailure", "Calicure"),
"tumaco" = c("Tumacofailure", "Tumacocure"),
"cure" = c("Tumacocure", "Calicure"),
"fail" = c("Tumacofailure", "Califailure"))
cf_contrast <- list(
"outcome" = c("Tumacofailure", "Tumacocure"))
t_cf_contrast <- list(
"outcome" = c("failure", "cure"))
visitcf_contrasts <- list(
"v1cf" = c("v1failure", "v1cure"),
"v2cf" = c("v2failure", "v2cure"),
"v3cf" = c("v3failure", "v3cure"))
visit_contrasts <- list(
"v2v1" = c("c2", "c1"),
"v3v1" = c("c3", "c1"),
"v3v2" = c("c3", "c2"))2 Compare samples by clinic
2.1 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")
table(pData(tc_clinic_type)[["condition"]])##
## Cali Tumaco
## 61 123tc_all_clinic_de_sva <- all_pairwise(tc_clinic_type, model_batch="svaseq", filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali Tumaco
## 61 123## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14290 remaining).## Setting 31271 low elements to zero.## transform_counts: Found 31271 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_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,
rda=glue::glue("rda/tc_all_clinic_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/compare_clinics/tc_all_clinic_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/compare_clinics/tc_all_clinic_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_all_clinic_table_sva-v202207 to rda/tc_all_clinic_table_sva-v202207.rda.tc_all_clinic_sig_sva <- extract_significant_genes(
tc_all_clinic_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/compare_clinics/tc_clinic_type_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/compare_clinics/tc_clinic_type_sig_sva-v202207.xlsx before writing the tables.2.2 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, model_batch="svaseq", filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Tumaco_cure Tumaco_failure
## 4 9 5## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (13608 remaining).## Setting 290 low elements to zero.## transform_counts: Found 290 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_biopsies_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_biopsies_clinic_table_sva <- combine_de_tables(
tc_biopsies_clinic_de_sva, keepers=tc_cf_contrasts,
rda=glue::glue("rda/tc_biopsies_clinic_table_sva-v{ver}.xlsx"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_biopsies_clinic_table_sva-v202207.xlsx to rda/tc_biopsies_clinic_table_sva-v202207.xlsx.tc_biopsies_clinic_sig_sva <- extract_significant_genes(
tc_biopsies_clinic_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_sig_sva-v202207.xlsx before writing the tables.2.3 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, model_batch=FALSE, filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Tumaco_cure Tumaco_failure
## 15 17 9## This will pre-filter the input data using normalize_expt's: TRUE argument.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_eosinophils_clinic_de_nobatch[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_eosinophils_clinic_table_nobatch <- combine_de_tables(
tc_eosinophils_clinic_de_nobatch, keepers=tc_cf_contrasts,
rda=glue::glue("rda/tc_eosinophils_clinic_table_nobatch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v202207.xlsx before writing the tables.## Saving de result as tc_eosinophils_clinic_table_nobatch-v202207 to rda/tc_eosinophils_clinic_table_nobatch-v202207.rda.tc_eosinophils_clinic_sig_nobatch <- extract_significant_genes(
tc_eosinophils_clinic_table_nobatch,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v202207.xlsx before writing the tables.tc_eosinophils_clinic_de_sva <- all_pairwise(tc_eosinophils, model_batch="svaseq", filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Tumaco_cure Tumaco_failure
## 15 17 9## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10864 remaining).## Setting 1043 low elements to zero.## transform_counts: Found 1043 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_eosinophils_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_eosinophils_clinic_table_sva <- combine_de_tables(
tc_eosinophils_clinic_de_sva, keepers=tc_cf_contrasts,
rda=glue::glue("rda/tc_eosinophils_clinic_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_eosinophils_clinic_table_sva-v202207 to rda/tc_eosinophils_clinic_table_sva-v202207.rda.tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
tc_eosinophils_clinic_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v202207.xlsx before writing the tables.2.4 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.
2.4.1 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.
tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch=FALSE, filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 21 21## This will pre-filter the input data using normalize_expt's: TRUE argument.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_monocytes_table_nobatch <- combine_de_tables(
tc_monocytes_de_nobatch, keepers=clinic_cf_contrasts,
rda=glue::glue("rda/tc_monocytes_clinic_table_nobatch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_nobatch-v202207.xlsx before writing the tables.## Saving de result as tc_monocytes_clinic_table_nobatch-v202207 to rda/tc_monocytes_clinic_table_nobatch-v202207.rda.tc_monocytes_sig_nobatch <- extract_significant_genes(
tc_monocytes_table_nobatch,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_nobatch-v202207.xlsx before writing the tables.2.4.2 DE: Compare clinics, monocytes with svaseq
In contrast, the following comparison should give a view of the data corresponding to the svaseq PCA plot above. In the best case scenario, we should therefore be able to see some significane differences between the Tumaco cure and fail samples.
tc_monocytes_de_sva <- all_pairwise(tc_monocytes, model_batch="svaseq", filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 21 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11104 remaining).## Setting 1447 low elements to zero.## transform_counts: Found 1447 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_monocytes_table_sva <- combine_de_tables(
tc_monocytes_de_sva, keepers=clinic_cf_contrasts,
rda=glue::glue("rda/tc_monocytes_clinic_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_monocytes_clinic_table_sva-v202207 to rda/tc_monocytes_clinic_table_sva-v202207.rda.tc_monocytes_sig_sva <- extract_significant_genes(
tc_monocytes_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_sva-v202207.xlsx before writing the tables.2.4.3 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_merged <- merge(cali_table, table, by="row.names")
cor.test(cali_merged[, "deseq_logfc.x"], cali_merged[, "deseq_logfc.y"])##
## Pearson's product-moment correlation
##
## data: cali_merged[, "deseq_logfc.x"] and cali_merged[, "deseq_logfc.y"]
## t = 0.92, df = 11102, p-value = 0.4
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.009917 0.027280
## sample estimates:
## cor
## 0.008685cali_aucc <- calculate_aucc(cali_table, table, px="deseq_adjp", py="deseq_adjp",
lx="deseq_logfc", ly="deseq_logfc")
cali_aucc$plot
cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]
cali_merged_sva <- merge(cali_table_sva, tumaco_table_sva, by="row.names")
cor.test(cali_merged_sva[, "deseq_logfc.x"], cali_merged_sva[, "deseq_logfc.y"])##
## Pearson's product-moment correlation
##
## data: cali_merged_sva[, "deseq_logfc.x"] and cali_merged_sva[, "deseq_logfc.y"]
## t = 16, df = 11102, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1356 0.1720
## sample estimates:
## cor
## 0.1539cali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px="deseq_adjp", py="deseq_adjp",
lx="deseq_logfc", ly="deseq_logfc")
cali_aucc_sva$plot
2.5 DE: Compare clinics, neutrophil samples
tc_neutrophils_de_nobatch <- all_pairwise(tc_neutrophils, model_batch=FALSE, filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 20 21## This will pre-filter the input data using normalize_expt's: TRUE argument.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_neutrophils_table_nobatch <- combine_de_tables(
tc_neutrophils_de_nobatch, keepers=clinic_cf_contrasts,
rda=glue::glue("rda/tc_neutrophils_clinic_table_nobatch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_nobatch-v202207.xlsx before writing the tables.## Saving de result as tc_neutrophils_clinic_table_nobatch-v202207 to rda/tc_neutrophils_clinic_table_nobatch-v202207.rda.tc_neutrophils_sig_nobatch <- extract_significant_genes(
tc_neutrophils_table_nobatch,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_nobatch-v202207.xlsx before writing the tables.tc_neutrophils_de_sva <- all_pairwise(tc_neutrophils, model_batch="svaseq", filter=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 20 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9242 remaining).## Setting 1541 low elements to zero.## transform_counts: Found 1541 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
tc_neutrophils_table_sva <- combine_de_tables(
tc_neutrophils_de_sva, keepers=clinic_cf_contrasts,
rda=glue::glue("rda/tc_neutrophils_clinic_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_neutrophils_clinic_table_sva-v202207 to rda/tc_neutrophils_clinic_table_sva-v202207.rda.tc_neutrophils_sig_sva <- extract_significant_genes(
tc_neutrophils_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_sva-v202207.xlsx before writing the tables.3 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 = 9240, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1800 -0.1403
## sample estimates:
## cor
## -0.1602cali_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
3.1 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)3.2 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.
3.2.1 GSEA: Compare clinics, all samples
clinic_gp <- simple_gprofiler(clinic_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 2065 against hsapiens.## GO search found 711 hits.## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the plotly package.
## Please report the issue at <https://github.com/plotly/plotly.R/issues>.## Performing gProfiler KEGG search of 2065 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 2065 against hsapiens.## REAC search found 11 hits.## Performing gProfiler WP search of 2065 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 2065 against hsapiens.## TF search found 254 hits.## Performing gProfiler MIRNA search of 2065 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 2065 against hsapiens.## HPA search found 13 hits.## Performing gProfiler CORUM search of 2065 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 2065 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"clinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp <- simple_gprofiler(clinic_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 2065 against hsapiens.## GO search found 711 hits.## Performing gProfiler KEGG search of 2065 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 2065 against hsapiens.## REAC search found 11 hits.## Performing gProfiler WP search of 2065 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 2065 against hsapiens.## TF search found 254 hits.## Performing gProfiler MIRNA search of 2065 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 2065 against hsapiens.## HPA search found 13 hits.## Performing gProfiler CORUM search of 2065 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 2065 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"clinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp <- simple_gprofiler(clinic_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 2065 against hsapiens.## GO search found 711 hits.## Performing gProfiler KEGG search of 2065 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 2065 against hsapiens.## REAC search found 11 hits.## Performing gProfiler WP search of 2065 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 2065 against hsapiens.## TF search found 254 hits.## Performing gProfiler MIRNA search of 2065 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 2065 against hsapiens.## HPA search found 13 hits.## Performing gProfiler CORUM search of 2065 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 2065 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"clinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundbroad_c7 <- load_gmt_signatures(signatures = "reference/msigdb/c7.all.v7.5.1.entrez.gmt",
signature_category = "c7")
broad_c2 <- load_gmt_signatures(signatures = "reference/msigdb/c2.all.v7.5.1.entrez.gmt",
signature_category = "c2")
broad_h <- load_gmt_signatures(signatures = "reference/msigdb/h.all.v7.5.1.entrez.gmt",
signature_category = "h")
clinic_gsea_msig_c2 <- goseq_msigdb(
clinic_sigenes, signatures = broad_c2,
signature_category = "c7")## Error in .testForValidKeys(x, keys, keytype, fks): 'keys' must be a character vector3.2.2 GSEA: Compare clinics, Eosinophil samples
tc_eosinophils_gp <- simple_gprofiler(tc_eosinophils_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 1584 against hsapiens.## GO search found 401 hits.## Performing gProfiler KEGG search of 1584 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 1584 against hsapiens.## REAC search found 2 hits.## Performing gProfiler WP search of 1584 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 1584 against hsapiens.## TF search found 535 hits.## Performing gProfiler MIRNA search of 1584 against hsapiens.## MIRNA search found 4 hits.## Performing gProfiler HPA search of 1584 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 1584 against hsapiens.## CORUM search found 5 hits.## Performing gProfiler HP search of 1584 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_eosinophils_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_up_gp <- simple_gprofiler(tc_eosinophils_sigenes_up)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 778 against hsapiens.## GO search found 304 hits.## Performing gProfiler KEGG search of 778 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 778 against hsapiens.## REAC search found 2 hits.## Performing gProfiler WP search of 778 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 778 against hsapiens.## TF search found 511 hits.## Performing gProfiler MIRNA search of 778 against hsapiens.## MIRNA search found 3 hits.## Performing gProfiler HPA search of 778 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 778 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 778 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_eosinophils_up_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_up_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_down_gp <- simple_gprofiler(tc_eosinophils_sigenes_down)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 806 against hsapiens.## GO search found 165 hits.## Performing gProfiler KEGG search of 806 against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 806 against hsapiens.## REAC search found 9 hits.## Performing gProfiler WP search of 806 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 806 against hsapiens.## TF search found 90 hits.## Performing gProfiler MIRNA search of 806 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 806 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 806 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 806 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_eosinophils_down_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not foundtc_eosinophils_down_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not found3.2.3 GSEA: Compare clinics, Monocyte samples
tc_monocytes_gp <- simple_gprofiler(tc_monocytes_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 1491 against hsapiens.## GO search found 625 hits.## Performing gProfiler KEGG search of 1491 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 1491 against hsapiens.## REAC search found 6 hits.## Performing gProfiler WP search of 1491 against hsapiens.## WP search found 3 hits.## Performing gProfiler TF search of 1491 against hsapiens.## TF search found 441 hits.## Performing gProfiler MIRNA search of 1491 against hsapiens.## MIRNA search found 1 hits.## Performing gProfiler HPA search of 1491 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 1491 against hsapiens.## CORUM search found 1 hits.## Performing gProfiler HP search of 1491 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_monocytes_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_gp' not foundtc_monocytes_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_gp' not foundtc_monocytes_up_gp <- simple_gprofiler(tc_monocytes_sigenes_up)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 761 against hsapiens.## GO search found 602 hits.## Performing gProfiler KEGG search of 761 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 761 against hsapiens.## REAC search found 6 hits.## Performing gProfiler WP search of 761 against hsapiens.## WP search found 3 hits.## Performing gProfiler TF search of 761 against hsapiens.## TF search found 433 hits.## Performing gProfiler MIRNA search of 761 against hsapiens.## MIRNA search found 1 hits.## Performing gProfiler HPA search of 761 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 761 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 761 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_monocytes_up_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_up_gp' not foundtc_monocytes_up_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_up_gp' not foundtc_monocytes_down_gp <- simple_gprofiler(tc_monocytes_sigenes_down)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 730 against hsapiens.## GO search found 62 hits.## Performing gProfiler KEGG search of 730 against hsapiens.## KEGG search found 1 hits.## Performing gProfiler REAC search of 730 against hsapiens.## REAC search found 1 hits.## Performing gProfiler WP search of 730 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 730 against hsapiens.## TF search found 281 hits.## Performing gProfiler MIRNA search of 730 against hsapiens.## MIRNA search found 1 hits.## Performing gProfiler HPA search of 730 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 730 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 730 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_monocytes_down_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_down_gp' not foundtc_monocytes_down_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_monocytes_down_gp' not found3.2.4 GSEA: Compare clinics, Neutrophil samples
tc_neutrophils_gp <- simple_gprofiler(tc_neutrophils_sigenes)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 1221 against hsapiens.## GO search found 199 hits.## Performing gProfiler KEGG search of 1221 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 1221 against hsapiens.## REAC search found 0 hits.## Performing gProfiler WP search of 1221 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 1221 against hsapiens.## TF search found 484 hits.## Performing gProfiler MIRNA search of 1221 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 1221 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 1221 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 1221 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_neutrophils_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_gp' not foundtc_neutrophils_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_gp' not foundtc_neutrophils_up_gp <- simple_gprofiler(tc_neutrophils_sigenes_up)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 843 against hsapiens.## GO search found 165 hits.## Performing gProfiler KEGG search of 843 against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 843 against hsapiens.## REAC search found 0 hits.## Performing gProfiler WP search of 843 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 843 against hsapiens.## TF search found 466 hits.## Performing gProfiler MIRNA search of 843 against hsapiens.## MIRNA search found 0 hits.## Performing gProfiler HPA search of 843 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 843 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 843 against hsapiens.## HP search found 0 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_neutrophils_up_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_up_gp' not foundtc_neutrophils_up_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_up_gp' not foundtc_neutrophils_down_gp <- simple_gprofiler(tc_neutrophils_sigenes_down)## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().## Performing gProfiler GO search of 378 against hsapiens.## GO search found 69 hits.## Performing gProfiler KEGG search of 378 against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 378 against hsapiens.## REAC search found 40 hits.## Performing gProfiler WP search of 378 against hsapiens.## WP search found 0 hits.## Performing gProfiler TF search of 378 against hsapiens.## TF search found 18 hits.## Performing gProfiler MIRNA search of 378 against hsapiens.## MIRNA search found 1 hits.## Performing gProfiler HPA search of 378 against hsapiens.## HPA search found 0 hits.## Performing gProfiler CORUM search of 378 against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 378 against hsapiens.## HP search found 10 hits.## Error in validObject(.Object): invalid class "enrichResult" object: invalid object for slot "gene" in class "enrichResult": got class "NULL", should be or extend class "character"tc_neutrophils_down_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_down_gp' not foundtc_neutrophils_down_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_neutrophils_down_gp' not found4 Tumaco and Cali, cure vs. fail
In all of the above, we are looking to understand the differences between the two location. Let us now step back and perform the original question: fail/cure without regard to location.
tc_all_cf_de_sva <- all_pairwise(tc_valid, filter=TRUE, model_batch="svaseq")## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 122 62## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14290 remaining).## Setting 27033 low elements to zero.## transform_counts: Found 27033 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_all_cf_table_sva <- combine_de_tables(
tc_all_cf_de_sva, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_valid_cf_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_valid_cf_table_sva-v202207 to rda/tc_valid_cf_table_sva-v202207.rda.tc_all_cf_sig_sva <- extract_significant_genes(
tc_all_cf_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_sva-v202207.xlsx before writing the tables.tc_all_cf_de_batch <- all_pairwise(tc_valid, filter=TRUE, model_batch=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 122 62## This analysis will include a batch factor in the model comprised of:##
## 1 2 3
## 83 50 51## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_all_cf_table_batch <- combine_de_tables(
tc_all_cf_de_batch,
keepers=t_cf_contrast,
rda=glue::glue("rda/tc_valid_cf_table_batch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_batch-v202207.xlsx before writing the tables.## Saving de result as tc_valid_cf_table_batch-v202207 to rda/tc_valid_cf_table_batch-v202207.rda.tc_all_cf_sig_batch <- extract_significant_genes(
tc_all_cf_table_batch,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v202207.xlsx before writing the tables.tc_biopsies_cf <- set_expt_conditions(tc_biopsies, fact="finaloutcome")
tc_biopsies_cf_de_sva <- all_pairwise(tc_biopsies_cf, filter=TRUE, model_batch="svaseq")## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 13 5## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (13608 remaining).## Setting 222 low elements to zero.## transform_counts: Found 222 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_biopsies_cf_table_sva <- combine_de_tables(
tc_biopsies_cf_de_sva, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_biopsies_cf_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Biopsies/tc_biopsies_cf_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_biopsies_cf_table_sva-v202207 to rda/tc_biopsies_cf_table_sva-v202207.rda.tc_biopsies_cf_sig_sva <- extract_significant_genes(
tc_biopsies_cf_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_sva-v202207.xlsx before writing the tables.tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter=TRUE, model_batch=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 13 5## This analysis will include a batch factor in the model comprised of:##
## 1
## 18## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_biopsies_cf_table_batch <- combine_de_tables(
tc_biopsies_cf_de_batch, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_biopsies_cf_table_batch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_table_batch-v202207.xlsx before writing the tables.## Saving de result as tc_biopsies_cf_table_batch-v202207 to rda/tc_biopsies_cf_table_batch-v202207.rda.tc_biopsies_cf_sig_batch <- extract_significant_genes(
tc_biopsies_cf_table_batch,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_batch-v202207.xlsx before writing the tables.tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact="finaloutcome")
tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter=TRUE, model_batch="svaseq")## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 32 9## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10864 remaining).## Setting 856 low elements to zero.## transform_counts: Found 856 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_eosinophils_cf_table_sva <- combine_de_tables(
tc_eosinophils_cf_de_sva, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_eosinophils_cf_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/Eosinophils/tc_eosinophils_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Eosinophils/tc_eosinophils_cf_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_eosinophils_cf_table_sva-v202207 to rda/tc_eosinophils_cf_table_sva-v202207.rda.tc_eosinophils_cf_sig_sva <- extract_significant_genes(
tc_eosinophils_cf_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_sva-v202207.xlsx before writing the tables.tc_eosinophils_cf_de_batch <- all_pairwise(tc_eosinophils_cf, filter=TRUE, model_batch=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 32 9## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 13 14 14## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_eosinophils_cf_table_batch <- combine_de_tables(
tc_eosinophils_cf_de_batch, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_eosinophils_cf_table_batch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_table_batch-v202207.xlsx before writing the tables.## Saving de result as tc_eosinophils_cf_table_batch-v202207 to rda/tc_eosinophils_cf_table_batch-v202207.rda.tc_eosinophils_cf_sig_batch <- extract_significant_genes(
tc_eosinophils_cf_table_batch,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v202207.xlsx before writing the tables.tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact="finaloutcome")
tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter=TRUE, model_batch="svaseq")## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 39 24## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11104 remaining).## Setting 1326 low elements to zero.## transform_counts: Found 1326 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_monocytes_cf_table_sva <- combine_de_tables(
tc_monocytes_cf_de_sva, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_monocytes_cf_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Monocytes/tc_monocytes_cf_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_monocytes_cf_table_sva-v202207 to rda/tc_monocytes_cf_table_sva-v202207.rda.tc_monocytes_cf_sig_sva <- extract_significant_genes(
tc_monocytes_cf_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_sva-v202207.xlsx before writing the tables.tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter=TRUE, model_batch=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 39 24## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 19 18 26## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_monocytes_cf_table_batch <- combine_de_tables(
tc_monocytes_cf_de_batch, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_monocytes_cf_table_batch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_table_batch-v202207.xlsx before writing the tables.## Saving de result as tc_monocytes_cf_table_batch-v202207 to rda/tc_monocytes_cf_table_batch-v202207.rda.tc_monocytes_cf_sig_batch <- extract_significant_genes(
tc_monocytes_cf_table_batch,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_batch-v202207.xlsx before writing the tables.tc_neutrophils_cf <- set_expt_conditions(tc_neutrophils, fact="finaloutcome")
tc_neutrophils_cf_de_sva <- all_pairwise(tc_neutrophils_cf, filter=TRUE, model_batch="svaseq")## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 38 24## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9242 remaining).## Setting 1562 low elements to zero.## transform_counts: Found 1562 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_neutrophils_cf_table_sva <- combine_de_tables(
tc_neutrophils_cf_de_sva, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_neutrophils_cf_table_sva-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Neutrophils/tc_neutrophils_cf_table_sva-v202207.xlsx before writing the tables.## Saving de result as tc_neutrophils_cf_table_sva-v202207 to rda/tc_neutrophils_cf_table_sva-v202207.rda.tc_neutrophils_cf_sig_sva <- extract_significant_genes(
tc_neutrophils_cf_table_sva,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_sva-v202207.xlsx before writing the tables.tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter=TRUE, model_batch=TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 38 24## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 19 18 25## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.tc_neutrophils_cf_table_batch <- combine_de_tables(
tc_neutrophils_cf_de_batch, keepers=t_cf_contrast,
rda=glue::glue("rda/tc_neutrophils_cf_table_batch-v{ver}.rda"),
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_table_batch-v202207.xlsx before writing the tables.## Saving de result as tc_neutrophils_cf_table_batch-v202207 to rda/tc_neutrophils_cf_table_batch-v202207.rda.tc_neutrophils_cf_sig_batch <- extract_significant_genes(
tc_neutrophils_cf_table_batch,
excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v202207.xlsx before writing the tables.5 Only Tumaco samples
5.1 Set the xlsx output prefix
xlsx_prefix <- "analyses/4_tumaco/DE_Cure_vs_Fail"5.2 All samples
t_cf_clinical_de_sva <- all_pairwise(t_clinical, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 67 56## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14149 remaining).## Setting 17282 low elements to zero.## transform_counts: Found 17282 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_table_sva <- combine_de_tables(
t_cf_clinical_de_sva, keepers = t_cf_contrast,
rda=glue::glue("rda/t_clinical_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_clinical_cf_table_sva-v202207 to rda/t_clinical_cf_table_sva-v202207.rda.t_cf_clinical_sig_sva <- extract_significant_genes(
t_cf_clinical_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_sig_sva$deseq$ups[[1]])## [1] 93 58dim(t_cf_clinical_sig_sva$deseq$downs[[1]])## [1] 183 585.2.1 All visits, each time point
t_cf_clinical_v1_de_sva <- all_pairwise(tv1_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 30 24## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14016 remaining).## Setting 7615 low elements to zero.## transform_counts: Found 7615 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v1_table_sva <- combine_de_tables(
t_cf_clinical_v1_de_sva, keepers = t_cf_contrast,
rda = glue::glue("rda/t_clinical_v1_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_clinical_v1_cf_table_sva-v202207 to rda/t_clinical_v1_cf_table_sva-v202207.rda.t_cf_clinical_v1_sig_sva <- extract_significant_genes(
t_cf_clinical_v1_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v1_sig_sva$deseq$ups[[1]])## [1] 28 58dim(t_cf_clinical_v1_sig_sva$deseq$downs[[1]])## [1] 74 58t_cf_clinical_v2_de_sva <- all_pairwise(tv2_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 20 15## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11559 remaining).## Setting 2848 low elements to zero.## transform_counts: Found 2848 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v2_table_sva <- combine_de_tables(
t_cf_clinical_v2_de_sva, keepers = t_cf_contrast,
rda=glue::glue("rda/t_clinical_v2_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_clinical_v2_cf_table_sva-v202207 to rda/t_clinical_v2_cf_table_sva-v202207.rda.t_cf_clinical_v2_sig_sva <- extract_significant_genes(
t_cf_clinical_v2_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v2_sig_sva$deseq$ups[[1]])## [1] 51 58dim(t_cf_clinical_v2_sig_sva$deseq$downs[[1]])## [1] 15 58t_cf_clinical_v3_de_sva <- all_pairwise(tv3_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 17 17## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11449 remaining).## Setting 1878 low elements to zero.## transform_counts: Found 1878 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v3_table_sva <- combine_de_tables(
t_cf_clinical_v3_de_sva, keepers = t_cf_contrast,
rda=glue::glue("rda/t_clinical_v3_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_clinical_v3_cf_table_sva-v202207 to rda/t_clinical_v3_cf_table_sva-v202207.rda.t_cf_clinical_v3_sig_sva <- extract_significant_genes(
t_cf_clinical_v3_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v3_sig_sva$deseq$ups[[1]])## [1] 120 58dim(t_cf_clinical_v3_sig_sva$deseq$downs[[1]])## [1] 62 585.2.2 Repeat no biopsies
t_cf_clinical_nobiop_de_sva <- all_pairwise(t_clinical_nobiop,
model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 58 51## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11907 remaining).## Setting 9578 low elements to zero.## transform_counts: Found 9578 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_nobiop_table_sva <- combine_de_tables(
t_cf_clinical_nobiop_de_sva, keepers = t_cf_contrast,
rda=glue::glue("rda/t_clinical_nobiop_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_clinical_nobiop_cf_table_sva-v202207 to rda/t_clinical_nobiop_cf_table_sva-v202207.rda.t_cf_clinical_nobiop_sig_sva <- extract_significant_genes(
t_cf_clinical_nobiop_table_sva,
excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_nobiop_sig_sva$deseq$ups[[1]])## [1] 137 58dim(t_cf_clinical_nobiop_sig_sva$deseq$downs[[1]])## [1] 73 585.2.3 By cell type
5.2.3.1 Cure/Fail, Biopsies
t_cf_biopsy_de_sva <- all_pairwise(t_biopsies, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 9 5## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (13506 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_biopsy_table_sva <- combine_de_tables(
t_cf_biopsy_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_biopsy_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Biopsies/t_biopsy_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_biopsy_cf_table_sva-v202207 to rda/t_biopsy_cf_table_sva-v202207.rda.t_cf_biopsy_sig_sva <- extract_significant_genes(
t_cf_biopsy_table_sva,
excel = glue::glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Biopsies/t_cf_biopsy_sig_sva-v202207.xlsx before writing the tables.dim(t_cf_biopsy_sig_sva$deseq$ups[[1]])## [1] 17 58dim(t_cf_biopsy_sig_sva$deseq$downs[[1]])## [1] 11 585.2.3.2 Cure/Fail, Monocytes
t_cf_monocyte_de_sva <- all_pairwise(t_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 21 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10859 remaining).## Setting 730 low elements to zero.## transform_counts: Found 730 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_tables_sva <- combine_de_tables(
t_cf_monocyte_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_monocyte_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_cf_table_sva-v202207 to rda/t_monocyte_cf_table_sva-v202207.rda.t_cf_monocyte_sig_sva <- extract_significant_genes(
t_cf_monocyte_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_sig_sva$deseq$ups[[1]])## [1] 60 58dim(t_cf_monocyte_sig_sva$deseq$downs[[1]])## [1] 53 58t_cf_monocyte_de_batchvisit <- all_pairwise(t_monocytes, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 21 21## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 13 13 16## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_tables_batchvisit <- combine_de_tables(
t_cf_monocyte_de_batchvisit, keepers = cf_contrast,
rda=glue::glue("rda/t_monocyte_cf_table_batchvisit-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_batchvisit-v{ver}.xlsx"))## Saving de result as t_monocyte_cf_table_batchvisit-v202207 to rda/t_monocyte_cf_table_batchvisit-v202207.rda.t_cf_monocyte_sig_batchvisit <- extract_significant_genes(
t_cf_monocyte_tables_batchvisit,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_batchvisit-v{ver}.xlsx"))
dim(t_cf_monocyte_sig_batchvisit$deseq$ups[[1]])## [1] 43 58dim(t_cf_monocyte_sig_batchvisit$deseq$downs[[1]])## [1] 93 585.2.4 All visits, Monocytes
t_cf_monocyte_v1_de_sva <- all_pairwise(tv1_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 8 8## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10479 remaining).## Setting 187 low elements to zero.## transform_counts: Found 187 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v1_tables_sva <- combine_de_tables(
t_cf_monocyte_v1_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_monocyte_v1_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_v1_cf_table_sva-v202207 to rda/t_monocyte_v1_cf_table_sva-v202207.rda.t_cf_monocyte_v1_sig_sva <- extract_significant_genes(
t_cf_monocyte_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v1_sig_sva$deseq$ups[[1]])## [1] 14 58dim(t_cf_monocyte_v1_sig_sva$deseq$downs[[1]])## [1] 52 58t_cf_monocyte_v2_de_sva <- all_pairwise(tv2_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 7 6## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10520 remaining).## Setting 115 low elements to zero.## transform_counts: Found 115 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v2_tables_sva <- combine_de_tables(
t_cf_monocyte_v2_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_monocyte_v2_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_v2_cf_table_sva-v202207 to rda/t_monocyte_v2_cf_table_sva-v202207.rda.t_cf_monocyte_v2_sig_sva <- extract_significant_genes(
t_cf_monocyte_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v2_sig_sva$deseq$ups[[1]])## [1] 0 58dim(t_cf_monocyte_v2_sig_sva$deseq$downs[[1]])## [1] 1 58t_cf_monocyte_v3_de_sva <- all_pairwise(tv3_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10374 remaining).## Setting 55 low elements to zero.## transform_counts: Found 55 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v3_tables_sva <- combine_de_tables(
t_cf_monocyte_v3_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_monocyte_v3_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_v3_cf_table_sva-v202207 to rda/t_monocyte_v3_cf_table_sva-v202207.rda.t_cf_monocyte_v3_sig_sva <- extract_significant_genes(
t_cf_monocyte_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v3_sig_sva$deseq$ups[[1]])## [1] 0 58dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 585.2.4.1 Monocytes: Compare sva to batch-in-model
sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][[1]],
tbl2=t_cf_monocyte_tables_batchvisit[["data"]][[1]],
py="deseq_adjp", ly="deseq_logfc")
sva_aucc## $aucc
## [1] 0.6943
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 180, df = 10857, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8611 0.8705
## sample estimates:
## cor
## 0.8659
##
##
## $plot
shared_ids <- rownames(t_cf_monocyte_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_monocyte_tables_batchvisit[["data"]][[1]])
first <- t_cf_monocyte_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_monocyte_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 180, df = 10857, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8611 0.8705
## sample estimates:
## cor
## 0.86595.2.4.2 Neutrophil samples
t_cf_neutrophil_de_sva <- all_pairwise(t_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 20 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 750 low elements to zero.## transform_counts: Found 750 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_tables_sva <- combine_de_tables(
t_cf_neutrophil_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_neutrophil_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_cf_table_sva-v202207 to rda/t_neutrophil_cf_table_sva-v202207.rda.t_cf_neutrophil_sig_sva <- extract_significant_genes(
t_cf_neutrophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_sig_sva$deseq$ups[[1]])## [1] 84 58dim(t_cf_neutrophil_sig_sva$deseq$downs[[1]])## [1] 29 58t_cf_neutrophil_de_batchvisit <- all_pairwise(t_neutrophils, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 20 21## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 12 13 16## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_tables_batchvisit <- combine_de_tables(
t_cf_neutrophil_de_batchvisit, keepers = cf_contrast,
rda=glue::glue("rda/t_neutrophil_cf_table_batchvisit-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_batchvisit-v{ver}.xlsx"))## Saving de result as t_neutrophil_cf_table_batchvisit-v202207 to rda/t_neutrophil_cf_table_batchvisit-v202207.rda.t_cf_neutrophil_sig_batchvisit <- extract_significant_genes(
t_cf_neutrophil_tables_batchvisit,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_batchvisit-v{ver}.xlsx"))
dim(t_cf_neutrophil_sig_batchvisit$deseq$ups[[1]])## [1] 92 58dim(t_cf_neutrophil_sig_batchvisit$deseq$downs[[1]])## [1] 47 585.2.5 Neutrophils by time
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]],
pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)
t_cf_neutrophil_visits_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 8 8 7 6 5 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 686 low elements to zero.## transform_counts: Found 686 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_cf_neutrophil_visits_tables_sva <- combine_de_tables(
t_cf_neutrophil_visits_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_visitcf_table_sva-v202207 to rda/t_neutrophil_visitcf_table_sva-v202207.rda.t_cf_neutrophil_visits_sig_sva <- extract_significant_genes(
t_cf_neutrophil_visits_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_visits_sig_sva$deseq$ups[[1]])## [1] 12 47dim(t_cf_neutrophil_visits_sig_sva$deseq$downs[[1]])## [1] 6 47t_cf_neutrophil_v1_de_sva <- all_pairwise(tv1_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 8 8## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8715 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v1_tables_sva <- combine_de_tables(
t_cf_neutrophil_v1_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_neutrophil_v1_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_v1_cf_table_sva-v202207 to rda/t_neutrophil_v1_cf_table_sva-v202207.rda.t_cf_neutrophil_v1_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v1_sig_sva$deseq$ups[[1]])## [1] 5 58dim(t_cf_neutrophil_v1_sig_sva$deseq$downs[[1]])## [1] 8 58t_cf_neutrophil_v2_de_sva <- all_pairwise(tv2_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 7 6## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8450 remaining).## Setting 78 low elements to zero.## transform_counts: Found 78 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v2_tables_sva <- combine_de_tables(
t_cf_neutrophil_v2_de_sva,
keepers = cf_contrast,
rda=glue::glue("rda/t_neutrophil_v2_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_v2_cf_table_sva-v202207 to rda/t_neutrophil_v2_cf_table_sva-v202207.rda.t_cf_neutrophil_v2_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 58dim(t_cf_neutrophil_v2_sig_sva$deseq$downs[[1]])## [1] 3 58t_cf_neutrophil_v3_de_sva <- all_pairwise(tv3_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 5 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8503 remaining).## Setting 83 low elements to zero.## transform_counts: Found 83 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v3_tables_sva <- combine_de_tables(
t_cf_neutrophil_v3_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_neutrophil_v3_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_v3_cf_table_sva-v202207 to rda/t_neutrophil_v3_cf_table_sva-v202207.rda.t_cf_neutrophil_v3_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v3_sig_sva$deseq$ups[[1]])## [1] 5 58dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 585.2.5.1 Neutrophils: Compare sva to batch-in-model
sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][[1]],
tbl2=t_cf_neutrophil_tables_batchvisit[["data"]][[1]],
py="deseq_adjp", ly="deseq_logfc")
sva_aucc## $aucc
## [1] 0.611
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 192, df = 9097, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8915 0.8996
## sample estimates:
## cor
## 0.8956
##
##
## $plot
shared_ids <- rownames(t_cf_neutrophil_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_neutrophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_neutrophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_neutrophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 192, df = 9097, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8915 0.8996
## sample estimates:
## cor
## 0.89565.2.5.2 Eosinophils
t_cf_eosinophil_de_sva <- all_pairwise(t_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 17 9## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 325 low elements to zero.## transform_counts: Found 325 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_tables_sva <- combine_de_tables(
t_cf_eosinophil_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_eosinophil_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_cf_tables_sva-v202207.xlsx before writing the tables.## Saving de result as t_eosinophil_cf_table_sva-v202207 to rda/t_eosinophil_cf_table_sva-v202207.rda.t_cf_eosinophil_sig_sva <- extract_significant_genes(
t_cf_eosinophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_sig_sva$deseq$ups[[1]])## [1] 116 58dim(t_cf_eosinophil_sig_sva$deseq$downs[[1]])## [1] 74 58t_cf_eosinophil_de_batchvisit <- all_pairwise(t_eosinophils, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 17 9## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 9 9 8## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_tables_batchvisit <- combine_de_tables(
t_cf_eosinophil_de_batchvisit, keepers = cf_contrast,
rda=glue::glue("rda/t_eosinophil_cf_table_batchvisit-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_batchvisit-v{ver}.xlsx"))## Saving de result as t_eosinophil_cf_table_batchvisit-v202207 to rda/t_eosinophil_cf_table_batchvisit-v202207.rda.t_cf_eosinophil_sig_batchvisit <- extract_significant_genes(
t_cf_eosinophil_tables_batchvisit,
excel = glue::glue("excel/t_eosinophil_cf_sig_batchvisit-v{ver}.xlsx"))
dim(t_cf_eosinophil_sig_batchvisit$deseq$ups[[1]])## [1] 99 58dim(t_cf_eosinophil_sig_batchvisit$deseq$downs[[1]])## [1] 35 58visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]],
pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact=visitcf_factor)
t_cf_eosinophil_visits_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 5 3 6 3 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 374 low elements to zero.## transform_counts: Found 374 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_cf_eosinophil_visits_tables_sva <- combine_de_tables(
t_cf_eosinophil_visits_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))## Saving de result as t_eosinophil_visitcf_table_sva-v202207 to rda/t_eosinophil_visitcf_table_sva-v202207.rda.t_cf_eosinophil_visits_sig_sva <- extract_significant_genes(
t_cf_eosinophil_visits_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_visits_sig_sva$deseq$ups[[1]])## [1] 9 47dim(t_cf_eosinophil_visits_sig_sva$deseq$downs[[1]])## [1] 11 475.2.6 Eosinophil time comparisons
t_cf_eosinophil_v1_de_sva <- all_pairwise(tv1_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 5 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9977 remaining).## Setting 57 low elements to zero.## transform_counts: Found 57 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v1_tables_sva <- combine_de_tables(
t_cf_eosinophil_v1_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_eosinophil_v1_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_eosinophil_v1_cf_table_sva-v202207 to rda/t_eosinophil_v1_cf_table_sva-v202207.rda.t_cf_eosinophil_v1_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v1_sig_sva$deseq$ups[[1]])## [1] 13 58dim(t_cf_eosinophil_v1_sig_sva$deseq$downs[[1]])## [1] 19 58t_cf_eosinophil_v2_de_sva <- all_pairwise(tv2_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10115 remaining).## Setting 90 low elements to zero.## transform_counts: Found 90 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v2_tables_sva <- combine_de_tables(
t_cf_eosinophil_v2_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_eosinophil_v2_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_eosinophil_v2_cf_table_sva-v202207 to rda/t_eosinophil_v2_cf_table_sva-v202207.rda.t_cf_eosinophil_v2_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 58dim(t_cf_eosinophil_v2_sig_sva$deseq$downs[[1]])## [1] 4 58t_cf_eosinophil_v3_de_sva <- all_pairwise(tv3_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10078 remaining).## Setting 48 low elements to zero.## transform_counts: Found 48 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v3_tables_sva <- combine_de_tables(
t_cf_eosinophil_v3_de_sva, keepers = cf_contrast,
rda=glue::glue("rda/t_eosinophil_v3_cf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_tables_sva-v{ver}.xlsx"))## Saving de result as t_eosinophil_v3_cf_table_sva-v202207 to rda/t_eosinophil_v3_cf_table_sva-v202207.rda.t_cf_eosinophil_v3_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v3_sig_sva$deseq$ups[[1]])## [1] 68 58dim(t_cf_eosinophil_v3_sig_sva$deseq$downs[[1]])## [1] 29 585.2.6.1 Eosinophils: Compare sva to batch-in-visit
sva_aucc <- calculate_aucc(t_cf_eosinophil_tables_sva[["data"]][[1]],
tbl2=t_cf_eosinophil_tables_batchvisit[["data"]][[1]],
py="deseq_adjp", ly="deseq_logfc")
sva_aucc## $aucc
## [1] 0.5764
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 152, df = 10528, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8232 0.8352
## sample estimates:
## cor
## 0.8293
##
##
## $plot
shared_ids <- rownames(t_cf_eosinophil_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_eosinophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_eosinophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_eosinophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 152, df = 10528, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8232 0.8352
## sample estimates:
## cor
## 0.82935.2.6.2 Compare monocyte CF, neutrophil CF, eosinophil CF
t_mono_neut_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
tbl2=t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
py="deseq_adjp", ly="deseq_logfc")
t_mono_neut_sva_aucc## $aucc
## [1] 0.2058
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 43, df = 8575, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4033 0.4381
## sample estimates:
## cor
## 0.4209
##
##
## $plot
t_mono_eo_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
tbl2=t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
py="deseq_adjp", ly="deseq_logfc")
t_mono_eo_sva_aucc## $aucc
## [1] 0.09657
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 22, df = 9763, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2028 0.2405
## sample estimates:
## cor
## 0.2217
##
##
## $plot
t_neut_eo_sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
tbl2=t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
py="deseq_adjp", ly="deseq_logfc")
t_neut_eo_sva_aucc## $aucc
## [1] 0.1583
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 36, df = 8569, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3467 0.3834
## sample estimates:
## cor
## 0.3652
##
##
## $plot
5.2.7 By visit
For these contrasts, we want to see fail_v1 vs. cure_v1, fail_v2 vs. cure_v2 etc. As a result, we will need to juggle the data slightly and add another set of contrasts.
5.2.7.1 Cure/Fail by visits, all cell types
t_visit_cf_all_de_sva <- all_pairwise(t_visitcf, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 30 24 20 15 17 17## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14149 remaining).## Setting 17117 low elements to zero.## transform_counts: Found 17117 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_cf_all_tables_sva <- combine_de_tables(
t_visit_cf_all_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_all_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_tables_sva-v{ver}.xlsx"))## Saving de result as t_all_visitcf_table_sva-v202207 to rda/t_all_visitcf_table_sva-v202207.rda.t_visit_cf_all_sig_sva <- extract_significant_genes(
t_visit_cf_all_tables_sva,
excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_sig_sva-v{ver}.xlsx"))5.2.7.2 Cure/Fail by visit, Monocytes
visitcf_factor <- paste0("v", pData(t_monocytes)[["visitnumber"]], "_",
pData(t_monocytes)[["finaloutcome"]])
t_monocytes_visitcf <- set_expt_conditions(t_monocytes, fact=visitcf_factor)
t_visit_cf_monocyte_de_sva <- all_pairwise(t_monocytes_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 6 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10859 remaining).## Setting 688 low elements to zero.## transform_counts: Found 688 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_cf_monocyte_tables_sva <- combine_de_tables(
t_visit_cf_monocyte_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_monocyte_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_visitcf_table_sva-v202207 to rda/t_monocyte_visitcf_table_sva-v202207.rda.t_visit_cf_monocyte_sig_sva <- extract_significant_genes(
t_visit_cf_monocyte_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_sig_sva-v{ver}.xlsx"))
t_v1fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v1cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v1_maplot.png")
t_v1fc_deseq_ma
closed <- dev.off()
t_v1fc_deseq_ma
t_v2fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v2cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v2_maplot.png")
t_v2fc_deseq_ma
closed <- dev.off()
t_v2fc_deseq_ma
t_v3fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v3cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v3_maplot.png")
t_v3fc_deseq_ma
closed <- dev.off()
t_v3fc_deseq_ma
One query from Alejandro is to look at the genes shared up/down across visits. I am not entirely certain we have enough samples for this to work, but let us find out.
I am thinking this is a good place to use the AUCC curves I learned about thanks to Julie Cridland.
Note that the following is all monocyte samples, this should therefore potentially be moved up and a version of this with only the Tumaco samples put here?
v1cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v1cf"]]
v2cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v2cf"]]
v3cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v3cf"]]
v1_sig <- c(
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v1cf"]]),
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v1cf"]]))
length(v1_sig)## [1] 25v2_sig <- c(
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v2cf"]]),
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v2cf"]]))
length(v2_sig)## [1] 0v3_sig <- c(
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v2cf"]]),
rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v2cf"]]))
length(v3_sig)## [1] 0t_monocyte_visit_aucc_v2v1 <- calculate_aucc(v1cf, tbl2=v2cf, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v2v1_aucc.png")
t_monocyte_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
t_monocyte_visit_aucc_v2v1[["plot"]]
t_monocyte_visit_aucc_v3v1 <- calculate_aucc(v1cf, tbl2=v3cf, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v3v1_aucc.png")
t_monocyte_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
t_monocyte_visit_aucc_v3v1[["plot"]]
5.2.7.3 Cure/Fail by visit, Neutrophils
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]], "_",
pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)
t_visit_cf_neutrophil_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 5 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 686 low elements to zero.## transform_counts: Found 686 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_cf_neutrophil_tables_sva <- combine_de_tables(
t_visit_cf_neutrophil_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_tables_sva-v202207.xlsx before writing the tables.## Saving de result as t_neutrophil_visitcf_table_sva-v202207 to rda/t_neutrophil_visitcf_table_sva-v202207.rda.t_visit_cf_neutrophil_sig_sva <- extract_significant_genes(
t_visit_cf_neutrophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_sig_sva-v202207.xlsx before writing the tables.5.2.7.4 Cure/Fail by visit, Eosinophils
visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]], "_",
pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact=visitcf_factor)
t_visit_cf_eosinophil_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 5 3 6 3 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 374 low elements to zero.## transform_counts: Found 374 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_cf_eosinophil_tables_sva <- combine_de_tables(
t_visit_cf_eosinophil_de_sva, keepers = visitcf_contrasts,
rda=glue::glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_tables_sva-v202207.xlsx before writing the tables.## Saving de result as t_eosinophil_visitcf_table_sva-v202207 to rda/t_eosinophil_visitcf_table_sva-v202207.rda.t_visit_cf_eosinophil_sig_sva <- extract_significant_genes(
t_visit_cf_eosinophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_sig_sva-v202207.xlsx before writing the tables.5.3 Persistence in visit 3
Having put some SL read mapping information in the sample sheet, Maria Adelaida added a new column using it with the putative persistence state on a per-sample basis. One question which arised from that: what differences are observable between the persistent yes vs. no samples on a per-cell-type basis among the visit 3 samples.
5.3.1 Setting up
First things first, create the datasets.
persistence_expt <- subset_expt(t_clinical, subset = "persistence=='Y'|persistence=='N'") %>%
subset_expt(subset = 'visitnumber==3') %>%
set_expt_conditions(fact = 'persistence')## subset_expt(): There were 123, now there are 97 samples.## subset_expt(): There were 97, now there are 30 samples.## persistence_biopsy <- subset_expt(persistence_expt, subset = "typeofcells=='biopsy'")
persistence_monocyte <- subset_expt(persistence_expt, subset = "typeofcells=='monocytes'")## subset_expt(): There were 30, now there are 12 samples.persistence_neutrophil <- subset_expt(persistence_expt, subset = "typeofcells=='neutrophils'")## subset_expt(): There were 30, now there are 10 samples.persistence_eosinophil <- subset_expt(persistence_expt, subset = "typeofcells=='eosinophils'")## subset_expt(): There were 30, now there are 8 samples.5.3.2 Take a look
See if there are any patterns which look usable.
## All
persistence_norm <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 8537 low-count genes (11386 remaining).## transform_counts: Found 15 values equal to 0, adding 1 to the matrix.plot_pca(persistence_norm)$plot## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
persistence_nb <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 8537 low-count genes (11386 remaining).## Setting 1538 low elements to zero.## transform_counts: Found 1538 values equal to 0, adding 1 to the matrix.plot_pca(persistence_nb)$plot
## Biopsies
##persistence_biopsy_norm <- normalize_expt(persistence_biopsy, transform = "log2", convert = "cpm",
## norm = "quant", filter = TRUE)
##plot_pca(persistence_biopsy_norm)$plot
## Insufficient data
## Monocytes
persistence_monocyte_norm <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 9597 low-count genes (10326 remaining).## transform_counts: Found 1 values equal to 0, adding 1 to the matrix.plot_pca(persistence_monocyte_norm)$plot
persistence_monocyte_nb <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 9597 low-count genes (10326 remaining).## Setting 46 low elements to zero.## transform_counts: Found 46 values equal to 0, adding 1 to the matrix.plot_pca(persistence_monocyte_nb)$plot
## Neutrophils
persistence_neutrophil_norm <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 11531 low-count genes (8392 remaining).## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.plot_pca(persistence_neutrophil_norm)$plot
persistence_neutrophil_nb <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 11531 low-count genes (8392 remaining).## Setting 46 low elements to zero.## transform_counts: Found 46 values equal to 0, adding 1 to the matrix.plot_pca(persistence_neutrophil_nb)$plot
## Eosinophils
persistence_eosinophil_norm <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 9895 low-count genes (10028 remaining).## transform_counts: Found 1 values equal to 0, adding 1 to the matrix.plot_pca(persistence_eosinophil_norm)$plot
persistence_eosinophil_nb <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 9895 low-count genes (10028 remaining).## Setting 25 low elements to zero.## transform_counts: Found 25 values equal to 0, adding 1 to the matrix.plot_pca(persistence_eosinophil_nb)$plot
5.3.3 persistence DE
persistence_de_sva <- all_pairwise(persistence_expt, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## N Y
## 6 24## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11386 remaining).## Setting 1538 low elements to zero.## transform_counts: Found 1538 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_table_sva <- combine_de_tables(
persistence_de_sva,
excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_all_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_all_de_sva-v202207.xlsx before writing the tables.persistence_monocyte_de_sva <- all_pairwise(persistence_monocyte, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## N Y
## 2 10## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10326 remaining).## Setting 46 low elements to zero.## transform_counts: Found 46 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_monocyte_table_sva <- combine_de_tables(
persistence_monocyte_de_sva,
excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_monocyte_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_monocyte_de_sva-v202207.xlsx before writing the tables.persistence_neutrophil_de_sva <- all_pairwise(persistence_neutrophil, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## N Y
## 3 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8392 remaining).## Setting 46 low elements to zero.## transform_counts: Found 46 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_neutrophil_table_sva <- combine_de_tables(
persistence_neutrophil_de_sva,
excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_neutrophil_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_neutrophil_de_sva-v202207.xlsx before writing the tables.persistence_eosinophil_de_sva <- all_pairwise(persistence_eosinophil, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## N Y
## 1 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10028 remaining).## Setting 25 low elements to zero.## transform_counts: Found 25 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.persistence_eosinophil_table_sva <- combine_de_tables(
persistence_eosinophil_de_sva,
excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_eosinophil_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_eosinophil_de_sva-v202207.xlsx before writing the tables.5.4 Comparing visits without regard to cure/fail
5.4.1 All cell types
t_visit_all_de_sva <- all_pairwise(t_visit, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## 3 2 1
## 34 35 40## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11907 remaining).## Setting 9614 low elements to zero.## transform_counts: Found 9614 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_all_table_sva <- combine_de_tables(
t_visit_all_de_sva, keepers = visit_contrasts,
rda=glue::glue("rda/t_all_visit_table_sva-v{ver}.rda"),
excel = glue::glue("analyses/4_tumaco/DE_Visits/t_all_visit_tables_sva-v{ver}.xlsx"))## Saving de result as t_all_visit_table_sva-v202207 to rda/t_all_visit_table_sva-v202207.rda.t_visit_all_sig_sva <- extract_significant_genes(
t_visit_all_table_sva,
excel = glue::glue("analyses/4_tumaco/DE_Visits/t_all_visit_sig_sva-v{ver}.xlsx"))5.4.2 Monocyte samples
t_visit_monocytes <- set_expt_conditions(t_monocytes, fact="visitnumber")
t_visit_monocyte_de_sva <- all_pairwise(t_visit_monocytes, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## 3 2 1
## 13 13 16## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10859 remaining).## Setting 648 low elements to zero.## transform_counts: Found 648 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_monocyte_table_sva <- combine_de_tables(
t_visit_monocyte_de_sva, keepers = visit_contrasts,
rda=glue::glue("rda/t_monocyte_visit_table_sva-v{ver}.rda"),
excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_tables_sva-v{ver}.xlsx"))## Saving de result as t_monocyte_visit_table_sva-v202207 to rda/t_monocyte_visit_table_sva-v202207.rda.t_visit_monocyte_sig_sva <- extract_significant_genes(
t_visit_monocyte_table_sva,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_sig_sva-v{ver}.xlsx"))5.4.3 Neutrophil samples
t_visit_neutrophils <- set_expt_conditions(t_neutrophils, fact="visitnumber")
t_visit_neutrophil_de_sva <- all_pairwise(t_visit_neutrophils, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## 3 2 1
## 12 13 16## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 589 low elements to zero.## transform_counts: Found 589 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_neutrophil_table_sva <- combine_de_tables(
t_visit_neutrophil_de_sva, keepers = visit_contrasts,
rda=glue::glue("rda/t_neutrophil_visit_table_sva-v{ver}.rda"),
excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_table_sva-v{ver}.xlsx"))## Saving de result as t_neutrophil_visit_table_sva-v202207 to rda/t_neutrophil_visit_table_sva-v202207.rda.t_visit_neutrophil_sig_sva <- extract_significant_genes(
t_visit_neutrophil_table_sva,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_sig_sva-v{ver}.xlsx"))5.4.4 Eosinophil samples
t_visit_eosinophils <- set_expt_conditions(t_eosinophils, fact="visitnumber")
t_visit_eosinophil_de <- all_pairwise(t_visit_eosinophils, filter = TRUE, model_batch = "svaseq")## This DE analysis will perform all pairwise comparisons among:##
## 3 2 1
## 9 9 8## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 272 low elements to zero.## transform_counts: Found 272 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_visit_eosinophil_table <- combine_de_tables(
t_visit_eosinophil_de, keepers = visit_contrasts,
rda=glue::glue("rda/t_eosinophil_visit_table_sva-v{ver}.rda"),
excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_table_sva-v{ver}.xlsx"))## Saving de result as t_eosinophil_visit_table_sva-v202207 to rda/t_eosinophil_visit_table_sva-v202207.rda.t_visit_eosinophil_sig <- extract_significant_genes(
t_visit_eosinophil_table,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_sig_sva-v{ver}.xlsx"))6 Explore ROC
Alejandro showed some ROC curves for eosinophil data showing sensitivity vs. specificity of a couple genes which were observed in v1 eosinophils vs. all-times eosinophils across cure/fail. I am curious to better understand how this was done and what utility it might have in other contexts.
To that end, I want to try something similar myself. In order to properly perform the analysis with these various tools, I need to reconfigure the data in a pretty specific format:
- Single df with 1 row per set of observations (sample in this case I think)
- The outcome column(s) need to be 1 (or more?) metadata factor(s) (cure/fail or a paste0 of relevant queries (eo_v1_cure, eo_v123_cure, etc)
- The predictor column(s) are the measurements (rpkm of 1 or more genes), 1 column each gene.
If I intend to use this for our tx data, I will likely need a utility function to create the properly formatted input df.
For the purposes of my playing, I will choose three genes from the eosinophil C/F table, one which is significant, one which is not, and an arbitrary.
The input genes will therefore be chosen from the data structure: t_cf_eosinophil_tables_sva:
ENSG00000198178, ENSG00000179344, ENSG00000182628
eo_rpkm <- normalize_expt(tv1_eosinophils, convert="rpkm", column="cds_length")if (!isTRUE(get0("skip_load"))) {
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename=savefile))
}## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset b0dcbf17fefc74206e61386580411962bf6beb4f## This is hpgltools commit: Fri Oct 7 14:32:20 2022 -0400: b0dcbf17fefc74206e61386580411962bf6beb4f## Saving to tmrc3_differential_expression_202207.rda.xztmp <- loadme(filename=savefile)---
title: "L. panamensis 202209: Differential Expression analyses"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: default
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
---

<style>
  body .main-container {
  max-width: 1600px;
}
</style>

```{r options, include=FALSE}
library(hpgltools)
library(dplyr)
library(forcats)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress=TRUE,
                     verbose=TRUE,
                     width=90,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      fig.width=8,
                      fig.height=8,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=12))
ver <- "202207"
previous_file <- ""
rundate <- format(Sys.Date(), format="%Y%m%d")

##tmp <- try(sm(loadme(filename=gsub(pattern="\\.Rmd", replace="\\.rda\\.xz", x=previous_file))))
rmd_file <- glue::glue("tmrc3_differential_expression_{ver}.Rmd")
savefile <- gsub(pattern="\\.Rmd", replace="\\.rda\\.xz", x=rmd_file)
loaded <- load(file=glue::glue("rda/tmrc3_data_structures-v{ver}.rda"))
```

# 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

```{r setup_contrasts}
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("Tumacofailure", "Tumacocure"),
    "cure" = c("Tumacocure", "Calicure"))
## In other cases, we have cure/fail for both places.
clinic_cf_contrasts <- list(
    "cali" = c("Califailure", "Calicure"),
    "tumaco" = c("Tumacofailure", "Tumacocure"),
    "cure" = c("Tumacocure", "Calicure"),
    "fail" = c("Tumacofailure", "Califailure"))
cf_contrast <- list(
    "outcome" = c("Tumacofailure", "Tumacocure"))
t_cf_contrast <- list(
    "outcome" = c("failure", "cure"))
visitcf_contrasts <- list(
    "v1cf" = c("v1failure", "v1cure"),
    "v2cf" = c("v2failure", "v2cure"),
    "v3cf" = c("v3failure", "v3cure"))
visit_contrasts <- list(
    "v2v1" = c("c2", "c1"),
    "v3v1" = c("c3", "c1"),
    "v3v2" = c("c3", "c2"))
```

# 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 clinic_comparisons_all}
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)
tc_all_clinic_de_sva[["deseq"]][["contrasts_performed"]]

tc_all_clinic_table_sva <- combine_de_tables(
    tc_all_clinic_de_sva, keepers=clinic_contrasts,
    rda=glue::glue("rda/tc_all_clinic_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/compare_clinics/tc_all_clinic_table_sva-v{ver}.xlsx"))
tc_all_clinic_sig_sva <- extract_significant_genes(
    tc_all_clinic_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/compare_clinics/tc_clinic_type_sig_sva-v{ver}.xlsx"))
```

## 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 tc_biopsies_de}
table(pData(tc_biopsies)[["condition"]])
tc_biopsies_clinic_de_sva <- all_pairwise(tc_biopsies, model_batch="svaseq", filter=TRUE)
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,
    rda=glue::glue("rda/tc_biopsies_clinic_table_sva-v{ver}.xlsx"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_table_sva-v{ver}.xlsx"))
tc_biopsies_clinic_sig_sva <- extract_significant_genes(
    tc_biopsies_clinic_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_sig_sva-v{ver}.xlsx"))
```

## 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 tc_eosinophils_de}
table(pData(tc_eosinophils)[["condition"]])
tc_eosinophils_clinic_de_nobatch <- all_pairwise(tc_eosinophils, model_batch=FALSE, filter=TRUE)
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,
    rda=glue::glue("rda/tc_eosinophils_clinic_table_nobatch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_nobatch <- extract_significant_genes(
    tc_eosinophils_clinic_table_nobatch,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))

tc_eosinophils_clinic_de_sva <- all_pairwise(tc_eosinophils, model_batch="svaseq", filter=TRUE)
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,
    rda=glue::glue("rda/tc_eosinophils_clinic_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
    tc_eosinophils_clinic_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))
```

## 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 tc_monocytes_de}
tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch=FALSE, filter=TRUE)

tc_monocytes_table_nobatch <- combine_de_tables(
    tc_monocytes_de_nobatch, keepers=clinic_cf_contrasts,
    rda=glue::glue("rda/tc_monocytes_clinic_table_nobatch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_nobatch-v{ver}.xlsx"))
tc_monocytes_sig_nobatch <- extract_significant_genes(
    tc_monocytes_table_nobatch,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_nobatch-v{ver}.xlsx"))
```

### DE: Compare clinics, monocytes with svaseq

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

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

tc_monocytes_table_sva <- combine_de_tables(
    tc_monocytes_de_sva, keepers=clinic_cf_contrasts,
    rda=glue::glue("rda/tc_monocytes_clinic_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_table_sva-v{ver}.xlsx"))
tc_monocytes_sig_sva <- extract_significant_genes(
    tc_monocytes_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Monocytes/tc_monocytes_clinic_sig_sva-v{ver}.xlsx"))
```

### 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 vs_cali_monocyte}
cali_table <- tc_monocytes_table_nobatch[["data"]][["cali"]]
table <- tc_monocytes_table_nobatch[["data"]][["tumaco"]]

cali_merged <- merge(cali_table, table, by="row.names")
cor.test(cali_merged[, "deseq_logfc.x"], cali_merged[, "deseq_logfc.y"])
cali_aucc <- calculate_aucc(cali_table, table, px="deseq_adjp", py="deseq_adjp",
                            lx="deseq_logfc", ly="deseq_logfc")
cali_aucc$plot

cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]

cali_merged_sva <- merge(cali_table_sva, tumaco_table_sva, by="row.names")
cor.test(cali_merged_sva[, "deseq_logfc.x"], cali_merged_sva[, "deseq_logfc.y"])
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$plot
```

## DE: Compare clinics, neutrophil samples

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

tc_neutrophils_table_nobatch <- combine_de_tables(
    tc_neutrophils_de_nobatch, keepers=clinic_cf_contrasts,
    rda=glue::glue("rda/tc_neutrophils_clinic_table_nobatch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_nobatch-v{ver}.xlsx"))
tc_neutrophils_sig_nobatch <- extract_significant_genes(
    tc_neutrophils_table_nobatch,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_nobatch-v{ver}.xlsx"))

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

tc_neutrophils_table_sva <- combine_de_tables(
    tc_neutrophils_de_sva, keepers=clinic_cf_contrasts,
    rda=glue::glue("rda/tc_neutrophils_clinic_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_table_sva-v{ver}.xlsx"))
tc_neutrophils_sig_sva <- extract_significant_genes(
    tc_neutrophils_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Neutrophils/tc_neutrophils_sig_sva-v{ver}.xlsx"))
```

# 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 vs_cali_neutrophils}
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
```

## 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 compare_clinic_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)
```

## 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

```{r gsea_clinic_gprofiler}
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$kegg_plot_over
clinic_gp$pvalue_plots$reactome_plot_over
```

```{r gsea_msig}
broad_c7 <- load_gmt_signatures(signatures = "reference/msigdb/c7.all.v7.5.1.entrez.gmt",
                                signature_category = "c7")
broad_c2 <- load_gmt_signatures(signatures = "reference/msigdb/c2.all.v7.5.1.entrez.gmt",
                                signature_category = "c2")
broad_h <- load_gmt_signatures(signatures = "reference/msigdb/h.all.v7.5.1.entrez.gmt",
                               signature_category = "h")


clinic_gsea_msig_c2 <- goseq_msigdb(
    clinic_sigenes, signatures = broad_c2,
    signature_category = "c7")
```

### GSEA: Compare clinics, Eosinophil samples

```{r gsea_clinic_eo}
tc_eosinophils_gp <- simple_gprofiler(tc_eosinophils_sigenes)
tc_eosinophils_gp$pvalue_plots$kegg_plot_over
tc_eosinophils_gp$pvalue_plots$reactome_plot_over

tc_eosinophils_up_gp <- simple_gprofiler(tc_eosinophils_sigenes_up)
tc_eosinophils_up_gp$pvalue_plots$kegg_plot_over
tc_eosinophils_up_gp$pvalue_plots$reactome_plot_over

tc_eosinophils_down_gp <- simple_gprofiler(tc_eosinophils_sigenes_down)
tc_eosinophils_down_gp$pvalue_plots$kegg_plot_over
tc_eosinophils_down_gp$pvalue_plots$reactome_plot_over
```

### GSEA: Compare clinics, Monocyte samples

```{r gsea_clinic_mnocyte}
tc_monocytes_gp <- simple_gprofiler(tc_monocytes_sigenes)
tc_monocytes_gp$pvalue_plots$kegg_plot_over
tc_monocytes_gp$pvalue_plots$reactome_plot_over

tc_monocytes_up_gp <- simple_gprofiler(tc_monocytes_sigenes_up)
tc_monocytes_up_gp$pvalue_plots$kegg_plot_over
tc_monocytes_up_gp$pvalue_plots$reactome_plot_over

tc_monocytes_down_gp <- simple_gprofiler(tc_monocytes_sigenes_down)
tc_monocytes_down_gp$pvalue_plots$kegg_plot_over
tc_monocytes_down_gp$pvalue_plots$reactome_plot_over
```

### GSEA: Compare clinics, Neutrophil samples

```{r gsea_clinic_neutrophils}
tc_neutrophils_gp <- simple_gprofiler(tc_neutrophils_sigenes)
tc_neutrophils_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_gp$pvalue_plots$reactome_plot_over

tc_neutrophils_up_gp <- simple_gprofiler(tc_neutrophils_sigenes_up)
tc_neutrophils_up_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_up_gp$pvalue_plots$reactome_plot_over

tc_neutrophils_down_gp <- simple_gprofiler(tc_neutrophils_sigenes_down)
tc_neutrophils_down_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_down_gp$pvalue_plots$reactome_plot_over
```

# Tumaco and Cali, cure vs. fail

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

```{r cf_tumaco_cali}
tc_all_cf_de_sva <- all_pairwise(tc_valid, filter=TRUE, model_batch="svaseq")
tc_all_cf_table_sva <- combine_de_tables(
    tc_all_cf_de_sva, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_valid_cf_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_sva-v{ver}.xlsx"))

tc_all_cf_de_batch <- all_pairwise(tc_valid, filter=TRUE, model_batch=TRUE)
tc_all_cf_table_batch <- combine_de_tables(
    tc_all_cf_de_batch,
    keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_valid_cf_table_batch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))
tc_biopsies_cf <- set_expt_conditions(tc_biopsies, fact="finaloutcome")
tc_biopsies_cf_de_sva <- all_pairwise(tc_biopsies_cf, filter=TRUE, model_batch="svaseq")
tc_biopsies_cf_table_sva <- combine_de_tables(
    tc_biopsies_cf_de_sva, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_biopsies_cf_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))
tc_biopsies_cf_sig_sva <- extract_significant_genes(
    tc_biopsies_cf_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))

tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter=TRUE, model_batch=TRUE)
tc_biopsies_cf_table_batch <- combine_de_tables(
    tc_biopsies_cf_de_batch, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_biopsies_cf_table_batch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))

tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact="finaloutcome")
tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter=TRUE, model_batch="svaseq")
tc_eosinophils_cf_table_sva <- combine_de_tables(
    tc_eosinophils_cf_de_sva, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_eosinophils_cf_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/Eosinophils/tc_eosinophils_cf_table_sva-v{ver}.xlsx"))
tc_eosinophils_cf_sig_sva <- extract_significant_genes(
    tc_eosinophils_cf_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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)
tc_eosinophils_cf_table_batch <- combine_de_tables(
    tc_eosinophils_cf_de_batch, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_eosinophils_cf_table_batch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))

tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact="finaloutcome")
tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter=TRUE, model_batch="svaseq")
tc_monocytes_cf_table_sva <- combine_de_tables(
    tc_monocytes_cf_de_sva, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_monocytes_cf_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))
tc_monocytes_cf_sig_sva <- extract_significant_genes(
    tc_monocytes_cf_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))

tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter=TRUE, model_batch=TRUE)
tc_monocytes_cf_table_batch <- combine_de_tables(
    tc_monocytes_cf_de_batch, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_monocytes_cf_table_batch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_batch-v{ver}.xlsx"))

tc_neutrophils_cf <- set_expt_conditions(tc_neutrophils, fact="finaloutcome")
tc_neutrophils_cf_de_sva <- all_pairwise(tc_neutrophils_cf, filter=TRUE, model_batch="svaseq")
tc_neutrophils_cf_table_sva <- combine_de_tables(
    tc_neutrophils_cf_de_sva, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_neutrophils_cf_table_sva-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))
tc_neutrophils_cf_sig_sva <- extract_significant_genes(
    tc_neutrophils_cf_table_sva,
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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)
tc_neutrophils_cf_table_batch <- combine_de_tables(
    tc_neutrophils_cf_de_batch, keepers=t_cf_contrast,
    rda=glue::glue("rda/tc_neutrophils_cf_table_batch-v{ver}.rda"),
    excel=glue::glue("analyses/3_cali_and_tumaco/cf/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::glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))
```

# Only Tumaco samples

## Set the xlsx output prefix

```{r xlsx_prefix}
xlsx_prefix <- "analyses/4_tumaco/DE_Cure_vs_Fail"
```

## All samples

```{r cf_all_de}
t_cf_clinical_de_sva <- all_pairwise(t_clinical, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_table_sva <- combine_de_tables(
    t_cf_clinical_de_sva, keepers = t_cf_contrast,
    rda=glue::glue("rda/t_clinical_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_sig_sva <- extract_significant_genes(
    t_cf_clinical_table_sva,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_cf_sig_sva-v{ver}.xlsx"))

dim(t_cf_clinical_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_sig_sva$deseq$downs[[1]])
```

### All visits, each time point

```{r all_timepoints}
t_cf_clinical_v1_de_sva <- all_pairwise(tv1_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v1_table_sva <- combine_de_tables(
    t_cf_clinical_v1_de_sva, keepers = t_cf_contrast,
    rda = glue::glue("rda/t_clinical_v1_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v1_sig_sva <- extract_significant_genes(
    t_cf_clinical_v1_table_sva,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v1_sig_sva$deseq$downs[[1]])

t_cf_clinical_v2_de_sva <- all_pairwise(tv2_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v2_table_sva <- combine_de_tables(
    t_cf_clinical_v2_de_sva, keepers = t_cf_contrast,
    rda=glue::glue("rda/t_clinical_v2_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v2_sig_sva <- extract_significant_genes(
    t_cf_clinical_v2_table_sva,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v2_sig_sva$deseq$downs[[1]])

t_cf_clinical_v3_de_sva <- all_pairwise(tv3_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v3_table_sva <- combine_de_tables(
    t_cf_clinical_v3_de_sva, keepers = t_cf_contrast,
    rda=glue::glue("rda/t_clinical_v3_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v3_sig_sva <- extract_significant_genes(
    t_cf_clinical_v3_table_sva,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v3_sig_sva$deseq$downs[[1]])
```

### Repeat no biopsies

```{r cf_all_de_nobiop}
t_cf_clinical_nobiop_de_sva <- all_pairwise(t_clinical_nobiop,
                                            model_batch = "svaseq", filter = TRUE)
t_cf_clinical_nobiop_table_sva <- combine_de_tables(
    t_cf_clinical_nobiop_de_sva, keepers = t_cf_contrast,
    rda=glue::glue("rda/t_clinical_nobiop_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_nobiop_sig_sva <- extract_significant_genes(
    t_cf_clinical_nobiop_table_sva,
    excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_sig_sva-v{ver}.xlsx"))

dim(t_cf_clinical_nobiop_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_nobiop_sig_sva$deseq$downs[[1]])
```

### By cell type

#### Cure/Fail, Biopsies

```{r cf_biopsy_de}
t_cf_biopsy_de_sva <- all_pairwise(t_biopsies, model_batch = "svaseq", filter = TRUE)
t_cf_biopsy_table_sva <- combine_de_tables(
    t_cf_biopsy_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_biopsy_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Biopsies/t_biopsy_cf_tables_sva-v{ver}.xlsx"))
t_cf_biopsy_sig_sva <- extract_significant_genes(
    t_cf_biopsy_table_sva,
    excel = glue::glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_sig_sva-v{ver}.xlsx"))

dim(t_cf_biopsy_sig_sva$deseq$ups[[1]])
dim(t_cf_biopsy_sig_sva$deseq$downs[[1]])
```

#### Cure/Fail, Monocytes

```{r cf_monocyte_de}
t_cf_monocyte_de_sva <- all_pairwise(t_monocytes, model_batch = "svaseq", filter = TRUE)

t_cf_monocyte_tables_sva <- combine_de_tables(
    t_cf_monocyte_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_monocyte_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_sig_sva <- extract_significant_genes(
    t_cf_monocyte_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_sva-v{ver}.xlsx"))

dim(t_cf_monocyte_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_sig_sva$deseq$downs[[1]])

t_cf_monocyte_de_batchvisit <- all_pairwise(t_monocytes, model_batch = TRUE, filter = TRUE)

t_cf_monocyte_tables_batchvisit <- combine_de_tables(
    t_cf_monocyte_de_batchvisit, keepers = cf_contrast,
    rda=glue::glue("rda/t_monocyte_cf_table_batchvisit-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_batchvisit-v{ver}.xlsx"))
t_cf_monocyte_sig_batchvisit <- extract_significant_genes(
    t_cf_monocyte_tables_batchvisit,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_batchvisit-v{ver}.xlsx"))

dim(t_cf_monocyte_sig_batchvisit$deseq$ups[[1]])
dim(t_cf_monocyte_sig_batchvisit$deseq$downs[[1]])
```

### All visits, Monocytes

```{r cf_monocyte_de_visits}
t_cf_monocyte_v1_de_sva <- all_pairwise(tv1_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v1_tables_sva <- combine_de_tables(
    t_cf_monocyte_v1_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_monocyte_v1_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v1_sig_sva <- extract_significant_genes(
    t_cf_monocyte_v1_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v1_sig_sva$deseq$downs[[1]])

t_cf_monocyte_v2_de_sva <- all_pairwise(tv2_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v2_tables_sva <- combine_de_tables(
    t_cf_monocyte_v2_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_monocyte_v2_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v2_sig_sva <- extract_significant_genes(
    t_cf_monocyte_v2_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v2_sig_sva$deseq$downs[[1]])

t_cf_monocyte_v3_de_sva <- all_pairwise(tv3_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v3_tables_sva <- combine_de_tables(
    t_cf_monocyte_v3_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_monocyte_v3_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v3_sig_sva <- extract_significant_genes(
    t_cf_monocyte_v3_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])
```

#### Monocytes: Compare sva to batch-in-model

```{r aucc_monocyte}
sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][[1]],
                           tbl2=t_cf_monocyte_tables_batchvisit[["data"]][[1]],
                           py="deseq_adjp", ly="deseq_logfc")
sva_aucc

shared_ids <- rownames(t_cf_monocyte_tables_sva[["data"]][[1]]) %in%
  rownames(t_cf_monocyte_tables_batchvisit[["data"]][[1]])
first <- t_cf_monocyte_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_monocyte_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])
```

#### Neutrophil samples

```{r neutrophil_only}
t_cf_neutrophil_de_sva <- all_pairwise(t_neutrophils, model_batch = "svaseq", filter = TRUE)

t_cf_neutrophil_tables_sva <- combine_de_tables(
    t_cf_neutrophil_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_neutrophil_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_sig_sva <- extract_significant_genes(
    t_cf_neutrophil_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_sva-v{ver}.xlsx"))

dim(t_cf_neutrophil_sig_sva$deseq$ups[[1]])
dim(t_cf_neutrophil_sig_sva$deseq$downs[[1]])

t_cf_neutrophil_de_batchvisit <- all_pairwise(t_neutrophils, model_batch = TRUE, filter = TRUE)

t_cf_neutrophil_tables_batchvisit <- combine_de_tables(
    t_cf_neutrophil_de_batchvisit, keepers = cf_contrast,
    rda=glue::glue("rda/t_neutrophil_cf_table_batchvisit-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_batchvisit-v{ver}.xlsx"))
t_cf_neutrophil_sig_batchvisit <- extract_significant_genes(
    t_cf_neutrophil_tables_batchvisit,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_batchvisit-v{ver}.xlsx"))

dim(t_cf_neutrophil_sig_batchvisit$deseq$ups[[1]])
dim(t_cf_neutrophil_sig_batchvisit$deseq$downs[[1]])
```

### Neutrophils by time

```{r neutrophil_visits}
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]],
                         pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)

t_cf_neutrophil_visits_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
                                              filter = TRUE)

t_cf_neutrophil_visits_tables_sva <- combine_de_tables(
    t_cf_neutrophil_visits_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_visits_sig_sva <- extract_significant_genes(
    t_cf_neutrophil_visits_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_visits_sig_sva$deseq$ups[[1]])
dim(t_cf_neutrophil_visits_sig_sva$deseq$downs[[1]])

t_cf_neutrophil_v1_de_sva <- all_pairwise(tv1_neutrophils, model_batch = "svaseq", filter = TRUE)
t_cf_neutrophil_v1_tables_sva <- combine_de_tables(
    t_cf_neutrophil_v1_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_neutrophil_v1_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v1_sig_sva <- extract_significant_genes(
    t_cf_neutrophil_v1_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_neutrophil_v1_sig_sva$deseq$downs[[1]])

t_cf_neutrophil_v2_de_sva <- all_pairwise(tv2_neutrophils, model_batch = "svaseq", filter = TRUE)
t_cf_neutrophil_v2_tables_sva <- combine_de_tables(
    t_cf_neutrophil_v2_de_sva,
    keepers = cf_contrast,
    rda=glue::glue("rda/t_neutrophil_v2_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v2_sig_sva <- extract_significant_genes(
    t_cf_neutrophil_v2_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_neutrophil_v2_sig_sva$deseq$downs[[1]])

t_cf_neutrophil_v3_de_sva <- all_pairwise(tv3_neutrophils, model_batch = "svaseq", filter = TRUE)
t_cf_neutrophil_v3_tables_sva <- combine_de_tables(
    t_cf_neutrophil_v3_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_neutrophil_v3_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v3_sig_sva <- extract_significant_genes(
    t_cf_neutrophil_v3_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_neutrophil_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])
```

#### Neutrophils: Compare sva to batch-in-model

```{r compare_neutrophil_aucc}
sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][[1]],
                           tbl2=t_cf_neutrophil_tables_batchvisit[["data"]][[1]],
                           py="deseq_adjp", ly="deseq_logfc")
sva_aucc

shared_ids <- rownames(t_cf_neutrophil_tables_sva[["data"]][[1]]) %in%
  rownames(t_cf_neutrophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_neutrophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_neutrophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])
```

#### Eosinophils

```{r eosinophil_only}
t_cf_eosinophil_de_sva <- all_pairwise(t_eosinophils, model_batch = "svaseq", filter = TRUE)

t_cf_eosinophil_tables_sva <- combine_de_tables(
    t_cf_eosinophil_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_eosinophil_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_sig_sva <- extract_significant_genes(
    t_cf_eosinophil_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_sig_sva-v{ver}.xlsx"))

dim(t_cf_eosinophil_sig_sva$deseq$ups[[1]])
dim(t_cf_eosinophil_sig_sva$deseq$downs[[1]])

t_cf_eosinophil_de_batchvisit <- all_pairwise(t_eosinophils, model_batch = TRUE, filter = TRUE)
t_cf_eosinophil_tables_batchvisit <- combine_de_tables(
    t_cf_eosinophil_de_batchvisit, keepers = cf_contrast,
    rda=glue::glue("rda/t_eosinophil_cf_table_batchvisit-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_batchvisit-v{ver}.xlsx"))
t_cf_eosinophil_sig_batchvisit <- extract_significant_genes(
    t_cf_eosinophil_tables_batchvisit,
    excel = glue::glue("excel/t_eosinophil_cf_sig_batchvisit-v{ver}.xlsx"))

dim(t_cf_eosinophil_sig_batchvisit$deseq$ups[[1]])
dim(t_cf_eosinophil_sig_batchvisit$deseq$downs[[1]])

visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]],
                         pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact=visitcf_factor)
t_cf_eosinophil_visits_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
                                              filter = TRUE)

t_cf_eosinophil_visits_tables_sva <- combine_de_tables(
    t_cf_eosinophil_visits_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_visits_sig_sva <- extract_significant_genes(
    t_cf_eosinophil_visits_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))

dim(t_cf_eosinophil_visits_sig_sva$deseq$ups[[1]])
dim(t_cf_eosinophil_visits_sig_sva$deseq$downs[[1]])
```

### Eosinophil time comparisons

```{r eosinophil_visits}
t_cf_eosinophil_v1_de_sva <- all_pairwise(tv1_eosinophils, model_batch = "svaseq", filter = TRUE)
t_cf_eosinophil_v1_tables_sva <- combine_de_tables(
    t_cf_eosinophil_v1_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_eosinophil_v1_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v1_sig_sva <- extract_significant_genes(
    t_cf_eosinophil_v1_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_eosinophil_v1_sig_sva$deseq$downs[[1]])

t_cf_eosinophil_v2_de_sva <- all_pairwise(tv2_eosinophils, model_batch = "svaseq", filter = TRUE)
t_cf_eosinophil_v2_tables_sva <- combine_de_tables(
    t_cf_eosinophil_v2_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_eosinophil_v2_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v2_sig_sva <- extract_significant_genes(
    t_cf_eosinophil_v2_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_eosinophil_v2_sig_sva$deseq$downs[[1]])

t_cf_eosinophil_v3_de_sva <- all_pairwise(tv3_eosinophils, model_batch = "svaseq", filter = TRUE)
t_cf_eosinophil_v3_tables_sva <- combine_de_tables(
    t_cf_eosinophil_v3_de_sva, keepers = cf_contrast,
    rda=glue::glue("rda/t_eosinophil_v3_cf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v3_sig_sva <- extract_significant_genes(
    t_cf_eosinophil_v3_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_eosinophil_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_eosinophil_v3_sig_sva$deseq$downs[[1]])
```

#### Eosinophils: Compare sva to batch-in-visit

```{r eosinophil_aucc}
sva_aucc <- calculate_aucc(t_cf_eosinophil_tables_sva[["data"]][[1]],
                           tbl2=t_cf_eosinophil_tables_batchvisit[["data"]][[1]],
                           py="deseq_adjp", ly="deseq_logfc")
sva_aucc

shared_ids <- rownames(t_cf_eosinophil_tables_sva[["data"]][[1]]) %in%
  rownames(t_cf_eosinophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_eosinophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_eosinophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])
```

#### Compare monocyte CF, neutrophil CF, eosinophil CF

```{r compare_mono_neut_eo}
t_mono_neut_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
                                       tbl2=t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
                                       py="deseq_adjp", ly="deseq_logfc")
t_mono_neut_sva_aucc

t_mono_eo_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
                                     tbl2=t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
                                     py="deseq_adjp", ly="deseq_logfc")
t_mono_eo_sva_aucc

t_neut_eo_sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
                                     tbl2=t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
                                     py="deseq_adjp", ly="deseq_logfc")
t_neut_eo_sva_aucc
```

### By visit

For these contrasts, we want to see fail_v1 vs. cure_v1, fail_v2
vs. cure_v2 etc.  As a result, we will need to juggle the data
slightly and add another set of contrasts.

#### Cure/Fail by visits, all cell types

```{r visit_cf_all_de}
t_visit_cf_all_de_sva <- all_pairwise(t_visitcf, model_batch = "svaseq", filter = TRUE)

t_visit_cf_all_tables_sva <- combine_de_tables(
    t_visit_cf_all_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_all_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_tables_sva-v{ver}.xlsx"))
t_visit_cf_all_sig_sva <- extract_significant_genes(
    t_visit_cf_all_tables_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_sig_sva-v{ver}.xlsx"))
```

#### Cure/Fail by visit, Monocytes

```{r visit_cf_monocyte_de}
visitcf_factor <- paste0("v", pData(t_monocytes)[["visitnumber"]], "_",
                         pData(t_monocytes)[["finaloutcome"]])
t_monocytes_visitcf <- set_expt_conditions(t_monocytes, fact=visitcf_factor)

t_visit_cf_monocyte_de_sva <- all_pairwise(t_monocytes_visitcf, model_batch = "svaseq",
                                           filter = TRUE)

t_visit_cf_monocyte_tables_sva <- combine_de_tables(
    t_visit_cf_monocyte_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_monocyte_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_tables_sva-v{ver}.xlsx"))
t_visit_cf_monocyte_sig_sva <- extract_significant_genes(
    t_visit_cf_monocyte_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_sig_sva-v{ver}.xlsx"))

t_v1fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v1cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v1_maplot.png")
t_v1fc_deseq_ma
closed <- dev.off()
t_v1fc_deseq_ma

t_v2fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v2cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v2_maplot.png")
t_v2fc_deseq_ma
closed <- dev.off()
t_v2fc_deseq_ma

t_v3fc_deseq_ma <- t_visit_cf_monocyte_tables_sva[["plots"]][["v3cf"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file="images/monocyte_cf_de_v3_maplot.png")
t_v3fc_deseq_ma
closed <- dev.off()
t_v3fc_deseq_ma
```

One query from Alejandro is to look at the genes shared up/down across
visits.  I am not entirely certain we have enough samples for this to
work, but let us find out.

I am thinking this is a good place to use the AUCC curves I learned
about thanks to Julie Cridland.

Note that the following is all monocyte samples, this should therefore
potentially be moved up and a version of this with only the Tumaco
samples put here?

```{r monocyte_shared_de_genes}
v1cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v1cf"]]
v2cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v2cf"]]
v3cf <- t_visit_cf_monocyte_tables_sva[["data"]][["v3cf"]]

v1_sig <- c(
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v1cf"]]),
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v1cf"]]))
length(v1_sig)

v2_sig <- c(
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v2cf"]]),
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v2cf"]]))
length(v2_sig)

v3_sig <- c(
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["ups"]][["v2cf"]]),
    rownames(t_visit_cf_monocyte_sig_sva[["deseq"]][["downs"]][["v2cf"]]))
length(v3_sig)

t_monocyte_visit_aucc_v2v1 <- calculate_aucc(v1cf, tbl2=v2cf, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v2v1_aucc.png")
t_monocyte_visit_aucc_v2v1[["plot"]]
closed <- dev.off()
t_monocyte_visit_aucc_v2v1[["plot"]]

t_monocyte_visit_aucc_v3v1 <- calculate_aucc(v1cf, tbl2=v3cf, py="deseq_adjp", ly="deseq_logfc")
dev <- pp(file="images/monocyte_visit_v3v1_aucc.png")
t_monocyte_visit_aucc_v3v1[["plot"]]
closed <- dev.off()
t_monocyte_visit_aucc_v3v1[["plot"]]
```

#### Cure/Fail by visit, Neutrophils

```{r visit_cf_neutrophil_de}
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]], "_",
                         pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)
t_visit_cf_neutrophil_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
                                             filter = TRUE)

t_visit_cf_neutrophil_tables_sva <- combine_de_tables(
    t_visit_cf_neutrophil_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))
t_visit_cf_neutrophil_sig_sva <- extract_significant_genes(
    t_visit_cf_neutrophil_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))
```

#### Cure/Fail by visit, Eosinophils

```{r visit_cf_eosinophil_de}
visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]], "_",
                         pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact=visitcf_factor)
t_visit_cf_eosinophil_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
                                             filter = TRUE)

t_visit_cf_eosinophil_tables_sva <- combine_de_tables(
    t_visit_cf_eosinophil_de_sva, keepers = visitcf_contrasts,
    rda=glue::glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))
t_visit_cf_eosinophil_sig_sva <- extract_significant_genes(
    t_visit_cf_eosinophil_tables_sva,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))
```

## Persistence in visit 3

Having put some SL read mapping information in the sample sheet, Maria
Adelaida added a new column using it with the putative persistence
state on a per-sample basis.  One question which arised from that:
what differences are observable between the persistent yes vs. no
samples on a per-cell-type basis among the visit 3 samples.

### Setting up

First things first, create the datasets.

```{r persistence_setup}
persistence_expt <- subset_expt(t_clinical, subset = "persistence=='Y'|persistence=='N'") %>%
  subset_expt(subset = 'visitnumber==3') %>%
  set_expt_conditions(fact = 'persistence')

## persistence_biopsy <- subset_expt(persistence_expt, subset = "typeofcells=='biopsy'")
persistence_monocyte <- subset_expt(persistence_expt, subset = "typeofcells=='monocytes'")
persistence_neutrophil <- subset_expt(persistence_expt, subset = "typeofcells=='neutrophils'")
persistence_eosinophil <- subset_expt(persistence_expt, subset = "typeofcells=='eosinophils'")
```

### Take a look

See if there are any patterns which look usable.

```{r persistence_plot}
## All
persistence_norm <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(persistence_norm)$plot
persistence_nb <- normalize_expt(persistence_expt, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = TRUE)
plot_pca(persistence_nb)$plot

## Biopsies
##persistence_biopsy_norm <- normalize_expt(persistence_biopsy, transform = "log2", convert = "cpm",
##                                   norm = "quant", filter = TRUE)
##plot_pca(persistence_biopsy_norm)$plot
## Insufficient data

## Monocytes
persistence_monocyte_norm <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
                                            norm = "quant", filter = TRUE)
plot_pca(persistence_monocyte_norm)$plot
persistence_monocyte_nb <- normalize_expt(persistence_monocyte, transform = "log2", convert = "cpm",
                                          batch = "svaseq", filter = TRUE)
plot_pca(persistence_monocyte_nb)$plot

## Neutrophils
persistence_neutrophil_norm <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
                                              norm = "quant", filter = TRUE)
plot_pca(persistence_neutrophil_norm)$plot
persistence_neutrophil_nb <- normalize_expt(persistence_neutrophil, transform = "log2", convert = "cpm",
                                            batch = "svaseq", filter = TRUE)
plot_pca(persistence_neutrophil_nb)$plot

## Eosinophils
persistence_eosinophil_norm <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
                                              norm = "quant", filter = TRUE)
plot_pca(persistence_eosinophil_norm)$plot
persistence_eosinophil_nb <- normalize_expt(persistence_eosinophil, transform = "log2", convert = "cpm",
                                            batch = "svaseq", filter = TRUE)
plot_pca(persistence_eosinophil_nb)$plot
```

### persistence DE

```{r persistence_de}
persistence_de_sva <- all_pairwise(persistence_expt, filter = TRUE, model_batch = "svaseq")
persistence_table_sva <- combine_de_tables(
    persistence_de_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_all_de_sva-v{ver}.xlsx"))

persistence_monocyte_de_sva <- all_pairwise(persistence_monocyte, filter = TRUE, model_batch = "svaseq")
persistence_monocyte_table_sva <- combine_de_tables(
    persistence_monocyte_de_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_monocyte_de_sva-v{ver}.xlsx"))

persistence_neutrophil_de_sva <- all_pairwise(persistence_neutrophil, filter = TRUE, model_batch = "svaseq")
persistence_neutrophil_table_sva <- combine_de_tables(
    persistence_neutrophil_de_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_neutrophil_de_sva-v{ver}.xlsx"))

persistence_eosinophil_de_sva <- all_pairwise(persistence_eosinophil, filter = TRUE, model_batch = "svaseq")
persistence_eosinophil_table_sva <- combine_de_tables(
    persistence_eosinophil_de_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Persistence/persistence_eosinophil_de_sva-v{ver}.xlsx"))
```

## Comparing visits without regard to cure/fail

### All cell types

```{r de_cf_visit_all}
t_visit_all_de_sva <- all_pairwise(t_visit, filter = TRUE, model_batch = "svaseq")

t_visit_all_table_sva <- combine_de_tables(
    t_visit_all_de_sva, keepers = visit_contrasts,
    rda=glue::glue("rda/t_all_visit_table_sva-v{ver}.rda"),
    excel = glue::glue("analyses/4_tumaco/DE_Visits/t_all_visit_tables_sva-v{ver}.xlsx"))
t_visit_all_sig_sva <- extract_significant_genes(
    t_visit_all_table_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/t_all_visit_sig_sva-v{ver}.xlsx"))
```

### Monocyte samples

```{r de_cf_visit_monocyte}
t_visit_monocytes <- set_expt_conditions(t_monocytes, fact="visitnumber")
t_visit_monocyte_de_sva <- all_pairwise(t_visit_monocytes, filter = TRUE, model_batch = "svaseq")

t_visit_monocyte_table_sva <- combine_de_tables(
    t_visit_monocyte_de_sva, keepers = visit_contrasts,
    rda=glue::glue("rda/t_monocyte_visit_table_sva-v{ver}.rda"),
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_tables_sva-v{ver}.xlsx"))
t_visit_monocyte_sig_sva <- extract_significant_genes(
    t_visit_monocyte_table_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_sig_sva-v{ver}.xlsx"))
```

### Neutrophil samples

```{r de_cf_visit_neutrophil}
t_visit_neutrophils <- set_expt_conditions(t_neutrophils, fact="visitnumber")
t_visit_neutrophil_de_sva <- all_pairwise(t_visit_neutrophils, filter = TRUE, model_batch = "svaseq")

t_visit_neutrophil_table_sva <- combine_de_tables(
    t_visit_neutrophil_de_sva, keepers = visit_contrasts,
    rda=glue::glue("rda/t_neutrophil_visit_table_sva-v{ver}.rda"),
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_table_sva-v{ver}.xlsx"))
t_visit_neutrophil_sig_sva <- extract_significant_genes(
    t_visit_neutrophil_table_sva,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_sig_sva-v{ver}.xlsx"))
```

### Eosinophil samples

```{r de_cf_visit_eosinophil}
t_visit_eosinophils <- set_expt_conditions(t_eosinophils, fact="visitnumber")
t_visit_eosinophil_de <- all_pairwise(t_visit_eosinophils, filter = TRUE, model_batch = "svaseq")

t_visit_eosinophil_table <- combine_de_tables(
    t_visit_eosinophil_de, keepers = visit_contrasts,
    rda=glue::glue("rda/t_eosinophil_visit_table_sva-v{ver}.rda"),
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_table_sva-v{ver}.xlsx"))
t_visit_eosinophil_sig <- extract_significant_genes(
    t_visit_eosinophil_table,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_sig_sva-v{ver}.xlsx"))
```

# Explore ROC

Alejandro showed some ROC curves for eosinophil data showing
sensitivity vs. specificity of a couple genes which were observed in
v1 eosinophils vs. all-times eosinophils across cure/fail.  I am
curious to better understand how this was done and what utility it
might have in other contexts.

To that end, I want to try something similar myself. In order to
properly perform the analysis with these various tools, I need to
reconfigure the data in a pretty specific format:

1.  Single df with 1 row per set of observations (sample in this case
    I think)
2.  The outcome column(s) need to be 1 (or more?) metadata factor(s)
    (cure/fail or a paste0 of relevant queries (eo_v1_cure,
    eo_v123_cure, etc)
3.  The predictor column(s) are the measurements (rpkm of 1 or more
    genes), 1 column each gene.

If I intend to use this for our tx data, I will likely need a utility
function to create the properly formatted input df.

For the purposes of my playing, I will choose three genes from the
eosinophil C/F table, one which is significant, one which is not, and
an arbitrary.

The input genes will therefore be chosen from the data structure:
t_cf_eosinophil_tables_sva:

ENSG00000198178, ENSG00000179344, ENSG00000182628

```{r roc}
eo_rpkm <- normalize_expt(tv1_eosinophils, convert="rpkm", column="cds_length")
```


```{r saveme}
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  message(paste0("Saving to ", savefile))
  tmp <- sm(saveme(filename=savefile))
}
```

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