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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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 <- 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"]]## Error in eval(expr, envir, enclos): object 'tc_eosinophils_clinic_de_nobatch' not foundtc_eosinophils_clinic_table_nobatch <- combine_de_tables(
tc_eosinophils_clinic_de_nobatch, keepers=tc_cf_contrasts,
excel=glue::glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))## Error in combine_de_tables(tc_eosinophils_clinic_de_nobatch, keepers = tc_cf_contrasts, : object 'tc_eosinophils_clinic_de_nobatch' not foundtc_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"))## Error in extract_significant_genes(tc_eosinophils_clinic_table_nobatch, : object 'tc_eosinophils_clinic_table_nobatch' not found2.4 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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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$plot
cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]
cali_merged_sva <- merge(cali_table_sva, tumaco_table_sva, by="row.names")
cor.test(cali_merged_sva[, "deseq_logfc.x"], cali_sva_merged[, "deseq_logfc.y"])## Error in cor.test.default(cali_merged_sva[, "deseq_logfc.x"], cali_sva_merged[, : object 'cali_sva_merged' not foundcali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px="deseq_adjp", py="deseq_adjp",
lx="deseq_logfc", ly="deseq_logfc")
cali_aucc_sva$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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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,
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.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.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.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_clinic_type_sig_sva[["deseq"]][["ups"]][["clinics"]])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'tc_clinic_type_sig_sva' not foundclinic_sigenes_down <- rownames(tc_clinic_type_sig_sva[["deseq"]][["downs"]][["clinics"]])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'tc_clinic_type_sig_sva' not foundclinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)## Error in eval(expr, envir, enclos): object 'clinic_sigenes_up' not foundtc_eosinophils_sigenes_up <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["ups"]][["cure"]])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'tc_eosinophils_clinic_sig_sva' not foundtc_eosinophils_sigenes_down <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["downs"]][["cure"]])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'tc_eosinophils_clinic_sig_sva' not foundtc_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)## Error in eval(expr, envir, enclos): object 'tc_eosinophils_sigenes_up' not foundtc_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)## Error in simple_gprofiler(clinic_sigenes): object 'clinic_sigenes' not foundclinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp <- simple_gprofiler(clinic_sigenes)## Error in simple_gprofiler(clinic_sigenes): object 'clinic_sigenes' not foundclinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp <- simple_gprofiler(clinic_sigenes)## Error in simple_gprofiler(clinic_sigenes): object 'clinic_sigenes' not foundclinic_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not foundclinic_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'clinic_gp' not found3.2.2 GSEA: Compare clinics, Eosinophil samples
tc_eosinophils_gp <- simple_gprofiler(tc_eosinophils_sigenes)## Error in simple_gprofiler(tc_eosinophils_sigenes): object 'tc_eosinophils_sigenes' not foundtc_eosinophils_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_up_gp <- simple_gprofiler(tc_eosinophils_sigenes_up)## Error in simple_gprofiler(tc_eosinophils_sigenes_up): object 'tc_eosinophils_sigenes_up' not foundtc_eosinophils_up_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_up_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_down_gp <- simple_gprofiler(tc_eosinophils_sigenes_down)## Error in simple_gprofiler(tc_eosinophils_sigenes_down): object 'tc_eosinophils_sigenes_down' not foundtc_eosinophils_down_gp$pvalue_plots$kegg_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not foundtc_eosinophils_down_gp$pvalue_plots$reactome_plot_over## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not found3.2.3 GSEA: Compare clinics, Monocyte samples
tc_monocytes_gp <- simple_gprofiler(tc_monocytes_sigenes)## Performing gProfiler GO search of 1491 genes against hsapiens.## GO search found 539 hits.## Performing gProfiler KEGG search of 1491 genes against hsapiens.## KEGG search found 30 hits.## Performing gProfiler REAC search of 1491 genes against hsapiens.## REAC search found 14 hits.## Performing gProfiler MI search of 1491 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 1491 genes against hsapiens.## TF search found 396 hits.## Performing gProfiler CORUM search of 1491 genes against hsapiens.## CORUM search found 7 hits.## Performing gProfiler HP search of 1491 genes against hsapiens.## HP search found 26 hits.tc_monocytes_gp$pvalue_plots$kegg_plot_over
tc_monocytes_gp$pvalue_plots$reactome_plot_over
tc_monocytes_up_gp <- simple_gprofiler(tc_monocytes_sigenes_up)## Performing gProfiler GO search of 761 genes against hsapiens.## GO search found 473 hits.## Performing gProfiler KEGG search of 761 genes against hsapiens.## KEGG search found 28 hits.## Performing gProfiler REAC search of 761 genes against hsapiens.## REAC search found 11 hits.## Performing gProfiler MI search of 761 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 761 genes against hsapiens.## TF search found 393 hits.## Performing gProfiler CORUM search of 761 genes against hsapiens.## CORUM search found 5 hits.## Performing gProfiler HP search of 761 genes against hsapiens.## HP search found 22 hits.tc_monocytes_up_gp$pvalue_plots$kegg_plot_over
tc_monocytes_up_gp$pvalue_plots$reactome_plot_over
tc_monocytes_down_gp <- simple_gprofiler(tc_monocytes_sigenes_down)## Performing gProfiler GO search of 730 genes against hsapiens.## GO search found 65 hits.## Performing gProfiler KEGG search of 730 genes against hsapiens.## KEGG search found 3 hits.## Performing gProfiler REAC search of 730 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 730 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 730 genes against hsapiens.## TF search found 280 hits.## Performing gProfiler CORUM search of 730 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 730 genes against hsapiens.## HP search found 10 hits.tc_monocytes_down_gp$pvalue_plots$kegg_plot_over
tc_monocytes_down_gp$pvalue_plots$reactome_plot_over
3.2.4 GSEA: Compare clinics, Neutrophil samples
tc_neutrophils_gp <- simple_gprofiler(tc_neutrophils_sigenes)## Performing gProfiler GO search of 1221 genes against hsapiens.## GO search found 150 hits.## Performing gProfiler KEGG search of 1221 genes against hsapiens.## KEGG search found 15 hits.## Performing gProfiler REAC search of 1221 genes against hsapiens.## REAC search found 11 hits.## Performing gProfiler MI search of 1221 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 1221 genes against hsapiens.## TF search found 398 hits.## Performing gProfiler CORUM search of 1221 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 1221 genes against hsapiens.## HP search found 2 hits.tc_neutrophils_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_gp$pvalue_plots$reactome_plot_over
tc_neutrophils_up_gp <- simple_gprofiler(tc_neutrophils_sigenes_up)## Performing gProfiler GO search of 843 genes against hsapiens.## GO search found 97 hits.## Performing gProfiler KEGG search of 843 genes against hsapiens.## KEGG search found 14 hits.## Performing gProfiler REAC search of 843 genes against hsapiens.## REAC search found 6 hits.## Performing gProfiler MI search of 843 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 843 genes against hsapiens.## TF search found 385 hits.## Performing gProfiler CORUM search of 843 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 843 genes against hsapiens.## HP search found 2 hits.tc_neutrophils_up_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_up_gp$pvalue_plots$reactome_plot_over
tc_neutrophils_down_gp <- simple_gprofiler(tc_neutrophils_sigenes_down)## Performing gProfiler GO search of 378 genes against hsapiens.## GO search found 63 hits.## Performing gProfiler KEGG search of 378 genes against hsapiens.## KEGG search found 13 hits.## Performing gProfiler REAC search of 378 genes against hsapiens.## REAC search found 69 hits.## Performing gProfiler MI search of 378 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 378 genes against hsapiens.## TF search found 13 hits.## Performing gProfiler CORUM search of 378 genes against hsapiens.## CORUM search found 3 hits.## Performing gProfiler HP search of 378 genes against hsapiens.## HP search found 13 hits.tc_neutrophils_down_gp$pvalue_plots$kegg_plot_over
tc_neutrophils_down_gp$pvalue_plots$reactome_plot_over
4 Only Tumaco samples
4.1 Set the xlsx output prefix
xlsx_prefix <- "analyses/4_tumaco/DE_Cure_vs_Fail"4.2 All samples
t_cf_clinical_de_sva <- all_pairwise(t_clinical, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 67 56## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14149 remaining).## Setting 17282 low elements to zero.## transform_counts: Found 17282 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_table_sva <- combine_de_tables(
t_cf_clinical_de_sva,
keepers = t_cf_contrast,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_cf_clinical_tables_sva-v202207.xlsx before writing the tables.t_cf_clinical_sig_sva <- extract_significant_genes(
t_cf_clinical_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_cf_clinical_sig_sva-v202207.xlsx before writing the tables.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_clinical_sig$deseq$ups[[1]])## Error in eval(expr, envir, enclos): object 't_cf_clinical_sig' not founddim(t_cf_clinical_sig$deseq$downs[[1]])## Error in eval(expr, envir, enclos): object 't_cf_clinical_sig' not found4.2.1 All visits, each time point
t_cf_clinical_v1_de_sva <- all_pairwise(tv1_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 30 24## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (14016 remaining).## Setting 7615 low elements to zero.## transform_counts: Found 7615 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v1_table_sva <- combine_de_tables(
t_cf_clinical_v1_de_sva,
keepers = t_cf_contrast,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v1_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v1_sig_sva <- extract_significant_genes(
t_cf_clinical_v1_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v1_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_clinical_v1_sig$deseq$ups[[1]])## Error in eval(expr, envir, enclos): object 't_cf_clinical_v1_sig' not founddim(t_cf_clinical_v1_sig$deseq$downs[[1]])## Error in eval(expr, envir, enclos): object 't_cf_clinical_v1_sig' not foundt_cf_clinical_v2_de_sva <- all_pairwise(tv2_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 20 15## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11559 remaining).## Setting 2848 low elements to zero.## transform_counts: Found 2848 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v2_table_sva <- combine_de_tables(
t_cf_clinical_v2_de_sva,
keepers = t_cf_contrast,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v2_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v2_sig_sva <- extract_significant_genes(
t_cf_clinical_v2_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v2_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_clinical_v2_sig_sva$deseq$ups[[1]])## [1] 51 58dim(t_cf_clinical_v2_sig_sva$deseq$downs[[1]])## [1] 15 58t_cf_clinical_v3_de_sva <- all_pairwise(tv3_samples, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 17 17## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11449 remaining).## Setting 1878 low elements to zero.## transform_counts: Found 1878 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_v3_table_sva <- combine_de_tables(
t_cf_clinical_v3_de_sva,
keepers = t_cf_contrast,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v3_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v3_sig_sva <- extract_significant_genes(
t_cf_clinical_v3_table_sva,
excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v3_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_clinical_v3_sig_sva$deseq$ups[[1]])## [1] 120 58dim(t_cf_clinical_v3_sig_sva$deseq$downs[[1]])## [1] 62 584.2.2 Repeat no biopsies
t_cf_clinical_nobiop_de_sva <- all_pairwise(t_clinical_nobiop,
model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## cure failure
## 58 51## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (11907 remaining).## Setting 9578 low elements to zero.## transform_counts: Found 9578 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_clinical_nobiop_table_sva <- combine_de_tables(
t_cf_clinical_nobiop_de_sva,
keepers = t_cf_contrast,
excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_cf_clinical_nobiop_tables_sva-v{ver}.xlsx"))
t_cf_clinical_nobiop_sig_sva <- extract_significant_genes(
t_cf_clinical_nobiop_table_sva,
excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_cf_clinical_nobiop_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_clinical_nobiop_sig_sva$deseq$ups[[1]])## [1] 137 58dim(t_cf_clinical_nobiop_sig_sva$deseq$downs[[1]])## [1] 73 584.2.3 By cell type
4.2.3.1 Cure/Fail, Biopsies
t_cf_biopsy_de_sva <- all_pairwise(t_biopsies, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 9 5## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (13506 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_biopsy_table_sva <- combine_de_tables(
t_cf_biopsy_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_tables_sva-v{ver}.xlsx"))
t_cf_biopsy_sig_sva <- extract_significant_genes(
t_cf_biopsy_table_sva,
excel = glue::glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_biopsy_sig_sva$deseq$ups[[1]])## [1] 17 58dim(t_cf_biopsy_sig_sva$deseq$downs[[1]])## [1] 11 584.2.3.2 Cure/Fail, Monocytes
t_cf_monocyte_de_sva <- all_pairwise(t_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 21 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10859 remaining).## Setting 730 low elements to zero.## transform_counts: Found 730 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_tables_sva <- combine_de_tables(
t_cf_monocyte_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_sig_sva <- extract_significant_genes(
t_cf_monocyte_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_monocyte_sig_sva$deseq$ups[[1]])## [1] 60 58dim(t_cf_monocyte_sig_sva$deseq$downs[[1]])## [1] 53 58t_cf_monocyte_de_batchvisit <- all_pairwise(t_monocytes, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 21 21## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 13 13 16## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_tables_batchvisit <- combine_de_tables(
t_cf_monocyte_de_batchvisit,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_tables_batchvisit-v{ver}.xlsx"))
t_cf_monocyte_sig_batchvisit <- extract_significant_genes(
t_cf_monocyte_tables_batchvisit,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_sig_batchvisit-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_monocyte_sig_batchvisit$deseq$ups[[1]])## [1] 43 58dim(t_cf_monocyte_sig_batchvisit$deseq$downs[[1]])## [1] 93 584.2.4 All visits, Monocytes
t_cf_monocyte_v1_de_sva <- all_pairwise(tv1_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 8 8## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10479 remaining).## Setting 187 low elements to zero.## transform_counts: Found 187 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v1_tables_sva <- combine_de_tables(
t_cf_monocyte_v1_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v1_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v1_sig_sva <- extract_significant_genes(
t_cf_monocyte_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v1_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_monocyte_v1_sig_sva$deseq$ups[[1]])## [1] 14 58dim(t_cf_monocyte_v1_sig_sva$deseq$downs[[1]])## [1] 52 58t_cf_monocyte_v2_de_sva <- all_pairwise(tv2_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 7 6## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10520 remaining).## Setting 115 low elements to zero.## transform_counts: Found 115 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v2_tables_sva <- combine_de_tables(
t_cf_monocyte_v2_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v2_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v2_sig_sva <- extract_significant_genes(
t_cf_monocyte_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v2_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_monocyte_v2_sig_sva$deseq$ups[[1]])## [1] 0 58dim(t_cf_monocyte_v2_sig_sva$deseq$downs[[1]])## [1] 1 58t_cf_monocyte_v3_de_sva <- all_pairwise(tv3_monocytes, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10374 remaining).## Setting 55 low elements to zero.## transform_counts: Found 55 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_monocyte_v3_tables_sva <- combine_de_tables(
t_cf_monocyte_v3_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v3_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v3_sig_sva <- extract_significant_genes(
t_cf_monocyte_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v3_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_monocyte_v3_sig_sva$deseq$ups[[1]])## [1] 0 58dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 584.2.4.1 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: tbl3[[lx]] and tbl3[[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
##
##
## $plot
shared_ids <- rownames(t_cf_monocyte_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_monocyte_tables_batchvisit[["data"]][[1]])
first <- t_cf_monocyte_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_monocyte_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 180, df = 10857, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8611 0.8705
## sample estimates:
## cor
## 0.86594.2.4.2 Neutrophil samples
t_cf_neutrophil_de_sva <- all_pairwise(t_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 20 21## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 750 low elements to zero.## transform_counts: Found 750 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_tables_sva <- combine_de_tables(
t_cf_neutrophil_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_sig_sva <- extract_significant_genes(
t_cf_neutrophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_sig_sva$deseq$ups[[1]])## [1] 84 58dim(t_cf_neutrophil_sig_sva$deseq$downs[[1]])## [1] 29 58t_cf_neutrophil_de_batchvisit <- all_pairwise(t_neutrophils, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 20 21## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 12 13 16## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_tables_batchvisit <- combine_de_tables(
t_cf_neutrophil_de_batchvisit,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_tables_batchvisit-v{ver}.xlsx"))
t_cf_neutrophil_sig_batchvisit <- extract_significant_genes(
t_cf_neutrophil_tables_batchvisit,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_sig_batchvisit-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_sig_batchvisit$deseq$ups[[1]])## [1] 92 58dim(t_cf_neutrophil_sig_batchvisit$deseq$downs[[1]])## [1] 47 584.2.5 Neutrophils by time
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]],
pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)
t_cf_neutrophil_visits_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 8 8 7 6 5 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9099 remaining).## Setting 686 low elements to zero.## transform_counts: Found 686 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_cf_neutrophil_visits_tables_sva <- combine_de_tables(
t_cf_neutrophil_visits_de_sva,
keepers = visitcf_contrasts,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_visits_sig_sva <- extract_significant_genes(
t_cf_neutrophil_visits_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_visits_sig_sva$deseq$ups[[1]])## [1] 12 47dim(t_cf_neutrophil_visits_sig_sva$deseq$downs[[1]])## [1] 6 47t_cf_neutrophil_v1_de_sva <- all_pairwise(tv1_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 8 8## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8715 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v1_tables_sva <- combine_de_tables(
t_cf_neutrophil_v1_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v1_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v1_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v1_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_v1_sig_sva$deseq$ups[[1]])## [1] 5 58dim(t_cf_neutrophil_v1_sig_sva$deseq$downs[[1]])## [1] 8 58t_cf_neutrophil_v2_de_sva <- all_pairwise(tv2_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 7 6## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8450 remaining).## Setting 78 low elements to zero.## transform_counts: Found 78 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v2_tables_sva <- combine_de_tables(
t_cf_neutrophil_v2_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v2_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v2_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v2_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 58dim(t_cf_neutrophil_v2_sig_sva$deseq$downs[[1]])## [1] 3 58t_cf_neutrophil_v3_de_sva <- all_pairwise(tv3_neutrophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 5 7## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (8503 remaining).## Setting 83 low elements to zero.## transform_counts: Found 83 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_neutrophil_v3_tables_sva <- combine_de_tables(
t_cf_neutrophil_v3_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v3_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_v3_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v3_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_neutrophil_v3_sig_sva$deseq$ups[[1]])## [1] 5 58dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 584.2.5.1 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: tbl3[[lx]] and tbl3[[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
##
##
## $plot
shared_ids <- rownames(t_cf_neutrophil_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_neutrophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_neutrophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_neutrophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 192, df = 9097, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8915 0.8996
## sample estimates:
## cor
## 0.89564.2.5.2 Eosinophils
t_cf_eosinophil_de_sva <- all_pairwise(t_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 17 9## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 325 low elements to zero.## transform_counts: Found 325 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_tables_sva <- combine_de_tables(
t_cf_eosinophil_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_sig_sva <- extract_significant_genes(
t_cf_eosinophil_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_sig_sva$deseq$ups[[1]])## [1] 116 58dim(t_cf_eosinophil_sig_sva$deseq$downs[[1]])## [1] 74 58t_cf_eosinophil_de_batchvisit <- all_pairwise(t_eosinophils, model_batch = TRUE, filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 17 9## This analysis will include a batch factor in the model comprised of:##
## 3 2 1
## 9 9 8## This will pre-filter the input data using normalize_expt's: TRUE argument.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_tables_batchvisit <- combine_de_tables(
t_cf_eosinophil_de_batchvisit,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_tables_batchvisit-v{ver}.xlsx"))
t_cf_eosinophil_sig_batchvisit <- extract_significant_genes(
t_cf_eosinophil_tables_batchvisit,
excel = glue::glue("excel/t_cf_eosinophil_sig_batchvisit-v{ver}.xlsx"))## Deleting the file excel/t_cf_eosinophil_sig_batchvisit-v202207.xlsx before writing the tables.## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_sig_batchvisit$deseq$ups[[1]])## [1] 99 58dim(t_cf_eosinophil_sig_batchvisit$deseq$downs[[1]])## [1] 35 58visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]],
pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact=visitcf_factor)
t_cf_eosinophil_visits_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 5 3 6 3 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10530 remaining).## Setting 374 low elements to zero.## transform_counts: Found 374 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.
t_cf_eosinophil_visits_tables_sva <- combine_de_tables(
t_cf_eosinophil_visits_de_sva,
keepers = visitcf_contrasts,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_visits_sig_sva <- extract_significant_genes(
t_cf_eosinophil_visits_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_visits_sig_sva$deseq$ups[[1]])## [1] 9 47dim(t_cf_eosinophil_visits_sig_sva$deseq$downs[[1]])## [1] 11 474.2.6 Eosinophil time comparisons
t_cf_eosinophil_v1_de_sva <- all_pairwise(tv1_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 5 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (9977 remaining).## Setting 57 low elements to zero.## transform_counts: Found 57 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v1_tables_sva <- combine_de_tables(
t_cf_eosinophil_v1_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v1_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v1_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v1_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v1_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_v1_sig_sva$deseq$ups[[1]])## [1] 13 58dim(t_cf_eosinophil_v1_sig_sva$deseq$downs[[1]])## [1] 19 58t_cf_eosinophil_v2_de_sva <- all_pairwise(tv2_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10115 remaining).## Setting 90 low elements to zero.## transform_counts: Found 90 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v2_tables_sva <- combine_de_tables(
t_cf_eosinophil_v2_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v2_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v2_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v2_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v2_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 58dim(t_cf_eosinophil_v2_sig_sva$deseq$downs[[1]])## [1] 4 58t_cf_eosinophil_v3_de_sva <- all_pairwise(tv3_eosinophils, model_batch = "svaseq", filter = TRUE)## This DE analysis will perform all pairwise comparisons among:##
## Tumaco_cure Tumaco_failure
## 6 3## This analysis will include surrogate estimates from: svaseq.## This will pre-filter the input data using normalize_expt's: TRUE argument.## Removing 0 low-count genes (10078 remaining).## Setting 48 low elements to zero.## transform_counts: Found 48 values equal to 0, adding 1 to the matrix.## Finished running DE analyses, collecting outputs.## Comparing analyses.t_cf_eosinophil_v3_tables_sva <- combine_de_tables(
t_cf_eosinophil_v3_de_sva,
keepers = cf_contrast,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v3_tables_sva-v{ver}.xlsx"))
t_cf_eosinophil_v3_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v3_tables_sva,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v3_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.dim(t_cf_eosinophil_v3_sig_sva$deseq$ups[[1]])## [1] 68 58dim(t_cf_eosinophil_v3_sig_sva$deseq$downs[[1]])## [1] 29 584.2.6.1 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: tbl3[[lx]] and tbl3[[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_batchbisit[["data"]][[1]][rownames(first), ]## Error in eval(expr, envir, enclos): object 't_cf_eosinophil_tables_batchbisit' not foundcor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])## Error in cor.test.default(first[["deseq_logfc"]], second[["deseq_logfc"]]): 'x' and 'y' must have the same length4.2.6.2 Compare monocyte CF, neutrophil CF, eosinophil CF
t_mono_neut_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][[1]],
tbl2=t_cf_neutrophil_tables_sva[["data"]][[1]],
py="deseq_adjp", ly="deseq_logfc")
t_mono_neut_sva_aucc## $aucc
## [1] 0.2058
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl3[[lx]] and tbl3[[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"]][["fail_vs_cure"]],
tbl2=t_cf_eosinophil_tables_sva[["data"]][["fail_vs_cure"]],
py="deseq_adjp", ly="deseq_logfc")## Error in cor.test(tbl3[[lx]], tbl3[[ly]]): object 'tbl3' not foundt_mono_eo_sva_aucc## Error in eval(expr, envir, enclos): object 't_mono_eo_sva_aucc' not foundt_neut_eo_sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][["fail_vs_cure"]],
tbl2=t_cf_eosinophil_tables_sva[["data"]][["fail_vs_cure"]],
py="deseq_adjp", ly="deseq_logfc")## Error in cor.test(tbl3[[lx]], tbl3[[ly]]): object 'tbl3' not foundt_neut_eo_sva_aucc## Error in eval(expr, envir, enclos): object 't_neut_eo_sva_aucc' not found4.2.7 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.
4.2.7.1 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,
excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_visit_cf_all_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_visit_cf_all_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.4.2.7.2 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,
excel = glue::glue("{xlsx_prefix}/Monocytes/t_visit_cf_monocyte_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_visit_cf_monocyte_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.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"]]
4.2.7.3 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,
excel = glue::glue("{xlsx_prefix}/Neutrophils/t_visit_cf_neutrophil_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_visit_cf_neutrophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.4.2.7.4 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,
excel = glue::glue("{xlsx_prefix}/Eosinophils/t_visit_cf_eosinophil_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_visit_cf_eosinophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: basic_adjp.4.3 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.
4.3.1 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.4.3.2 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
4.3.3 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"))
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"))
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"))
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"))4.4 Comparing visits without regard to cure/fail
4.4.1 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,
excel = glue::glue("analyses/4_tumaco/DE_Visits/t_visit_all_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_visit_all_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.t_visit_all_saved <- save(list = "t_visit_all_table_sva",
file = glue::glue("rda/visit_all_table-v{ver}.rda"))4.4.2 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,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_visit_monocyte_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_visit_monocyte_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.4.4.3 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,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_visit_neutrophil_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_visit_neutrophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.visit_neutrophil_saved <- save(list = "t_visit_neutrophil_table_sva",
file = glue::glue("rda/visit_neutrophil_table_sva-v{ver}.rda"))4.4.4 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,
excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_visit_eosinophil_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_visit_eosinophil_sig_sva-v{ver}.xlsx"))## Using p column: limma_adjp.## Using p column: edger_adjp.## Using p column: deseq_adjp.## Using p column: ebseq_adjp.## Using p column: basic_adjp.visit_eosinophil_saved <- save(list = "t_visit_eosinophil_table",
file = glue::glue("rda/visit_eosinophil_table-v{ver}.rda"))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))
}tmp <- loadme(filename=savefile)---
title: "L. panamensis 202207: 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,
    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,
    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 <- 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,
    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"))
```

## 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,
    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,
    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_sva_merged[, "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,
    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,
    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_clinic_type_sig_sva[["deseq"]][["ups"]][["clinics"]])
clinic_sigenes_down <- rownames(tc_clinic_type_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
```

### 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
```

# 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,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_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_cf_clinical_sig_sva-v{ver}.xlsx"))

dim(t_cf_clinical_sig$deseq$ups[[1]])
dim(t_cf_clinical_sig$deseq$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,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v1_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_cf_clinical_v1_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v1_sig$deseq$ups[[1]])
dim(t_cf_clinical_v1_sig$deseq$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,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v2_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_cf_clinical_v2_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,
    excel = glue::glue("{xlsx_prefix}/All_Samples/t_cf_clinical_v3_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_cf_clinical_v3_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,
    excel = glue::glue("{xlsx_prefix}/No_Biopsies/t_cf_clinical_nobiop_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_cf_clinical_nobiop_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,
    excel = glue::glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_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_cf_monocyte_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_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_cf_monocyte_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v1_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_cf_monocyte_v1_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v2_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_cf_monocyte_v2_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_cf_monocyte_v3_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_cf_monocyte_v3_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_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_cf_neutrophil_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_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_cf_neutrophil_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_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_cf_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v1_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_cf_neutrophil_v1_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v2_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_cf_neutrophil_v2_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_cf_neutrophil_v3_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_cf_neutrophil_v3_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_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_cf_eosinophil_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_tables_batchvisit-v{ver}.xlsx"))
t_cf_eosinophil_sig_batchvisit <- extract_significant_genes(
    t_cf_eosinophil_tables_batchvisit,
    excel = glue::glue("excel/t_cf_eosinophil_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_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_cf_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v1_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_cf_eosinophil_v1_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v2_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_cf_eosinophil_v2_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_cf_eosinophil_v3_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_cf_eosinophil_v3_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_batchbisit[["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"]][[1]],
                                       tbl2=t_cf_neutrophil_tables_sva[["data"]][[1]],
                                       py="deseq_adjp", ly="deseq_logfc")
t_mono_neut_sva_aucc

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

t_neut_eo_sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][["fail_vs_cure"]],
                                     tbl2=t_cf_eosinophil_tables_sva[["data"]][["fail_vs_cure"]],
                                     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,
    excel = glue::glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_visit_cf_all_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_visit_cf_all_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,
    excel = glue::glue("{xlsx_prefix}/Monocytes/t_visit_cf_monocyte_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_visit_cf_monocyte_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,
    excel = glue::glue("{xlsx_prefix}/Neutrophils/t_visit_cf_neutrophil_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_visit_cf_neutrophil_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,
    excel = glue::glue("{xlsx_prefix}/Eosinophils/t_visit_cf_eosinophil_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_visit_cf_eosinophil_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,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/t_visit_all_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_visit_all_sig_sva-v{ver}.xlsx"))
t_visit_all_saved <- save(list = "t_visit_all_table_sva",
                          file = glue::glue("rda/visit_all_table-v{ver}.rda"))
```

### 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,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Monocytes/t_visit_monocyte_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_visit_monocyte_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,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_visit_neutrophil_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_visit_neutrophil_sig_sva-v{ver}.xlsx"))
visit_neutrophil_saved <- save(list = "t_visit_neutrophil_table_sva",
                               file = glue::glue("rda/visit_neutrophil_table_sva-v{ver}.rda"))
```

### 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,
    excel = glue::glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_visit_eosinophil_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_visit_eosinophil_sig_sva-v{ver}.xlsx"))
visit_eosinophil_saved <- save(list = "t_visit_eosinophil_table",
                               file = glue::glue("rda/visit_eosinophil_table-v{ver}.rda"))
```


```{r saveme, eval=FALSE}
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)
```
