1 Changelog
- Still hunting for messed up colors, changed input data to match new version.
2 Introduction
The various differential expression analyses of the data generated in tmrc3_datasets will occur in this document.
2.1 Naming conventions
I am going to try to standardize how I name the various data structures created in this document. Most of the large data created are either sets of differential expression analyses, their combined results, or the set of results deemed ‘significant’.
Hopefully by now they all follow these guidelines:
{clinic(s)}sample-subset}{primary-question(s)}{datatype}{batch-method}
- {clinic}: This is either tc or t for Tumaco and Cali, or just Tumaco.
- {sample-subset}: Things like ‘all’ or ‘monocytes’.
- {primary-question}: Shorthand name for the primary contrasts performed, thus ‘clinics’ would suggest a comparison of Tumaco vs. Cali. ‘visits’ would compare v2/v1, etc.
- {datatype}: de, table, sig
- {batch-type}: nobatch, batch{factor}, sva. {factor} in this instance should be a column from the metadata.
With this in mind, ‘tc_biopsies_clinic_de_sva’ should be the Tumaco+Cali biopsy data after performing the differential expression analyses comparing the clinics using sva.
I suspect there remain some exceptions and/or errors.
2.2 Define contrasts for DE analyses
Each of the following lists describes the set of contrasts that I think are interesting for the various ways one might consider the TMRC3 dataset. The variables are named according to the assumed data with which they will be used, thus tc_cf_contrasts is expected to be used for the Tumaco+Cali data and provide a series of cure/fail comparisons which (to the extent possible) across both locations. In every case, the name of the list element will be used as the contrast name, and will thus be seen as the sheet name in the output xlsx file(s); the two pieces of the character vector value are the numerator and denominator of the associated contrast.
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"))
visit_v1later <- list(
"later_vs_first" = c("later", "first"))
celltypes <- list(
"eo_mono" = c("eosinophils", "monocytes"),
"ne_mono" = c("neutrophils", "monocytes"),
"eo_ne" = c("eosinophils", "neutrophils"))3 Compare samples by clinic
3.1 DE: Compare clinics, all samples
Perform a svaseq-guided comparison of the two clinics. Ideally this will give some clue about just how strong the clinic-based batch effect really is and what its causes are.
tc_clinic_type <- tc_valid %>%
set_expt_conditions(fact = "clinic") %>%
set_expt_batches(fact = "typeofcells")##
## Cali Tumaco
## 61 123
##
## biopsy eosinophils monocytes neutrophils
## 18 41 63 62table(pData(tc_clinic_type)[["condition"]])##
## Cali Tumaco
## 61 123tc_all_clinic_de_sva <- all_pairwise(tc_clinic_type, model_batch = "svaseq",
filter = TRUE)##
## Cali Tumaco
## 61 123## 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.tc_all_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "Tumaco_vs_Cali"tc_all_clinic_table_sva <- combine_de_tables(
tc_all_clinic_de_sva, keepers = clinic_contrasts,
# rda = glue("rda/tc_all_clinic_table_sva-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for clinics.tc_all_clinic_sig_sva <- extract_significant_genes(
tc_all_clinic_table_sva,
excel = 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-v202304.xlsx before writing the tables.3.1.1 Visualize clinic differences
Let us take a quick look at the results of the comparison of Tumaco/Cali
Note: I keep re-introducing an error which causes these (volcano and MA) plots to be reversed with respect to the logFC values. Pay careful attention to these and make sure that they agree with the numbers of genes observed in the contrast.
## Check that up is up
summary(tc_all_clinic_table_sva[["data"]][["clinics"]][["deseq_logfc"]])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -20.580 -0.584 -0.155 -0.255 0.172 3.515## I think we can assume that most genes are down when considering Tumaco/Cali.
sum(tc_all_clinic_table_sva$data$clinics$deseq_logfc < -1.0 &
tc_all_clinic_table_sva$data$clinics$deseq_adjp < 0.05)## [1] 1792tc_all_clinic_table_sva[["plots"]][["clinics"]][["deseq_vol_plots"]]
## Ok, so it says 1794 up, but that is clearly the down side... Something is definitely messed up.
## The points are on the correct sides of the plot, but the categories of up/down are reversed.
## Theresa noted that she colors differently, and I think better: left side gets called
## 'increased in denominator', right side gets called 'increased in numerator';
## these two groups are colored according to their condition colors, and everything else is gray.
## I am checking out Theresa's helper_functions.R to get a sense of how she handles this, I think
## I can use a variant of her idea pretty easily:
## 1. Add a column 'Significance', which is a factor, and contains either 'Not enriched',
## 'Enriched in x', or 'Enriched in y' according to the logfc/adjp.
## 2. use the significance column for the geom_point color/fill in the volcano plot.
## My change to this idea would be to extract the colors from the input expressionset.3.1.2 Ontology Search by clinic
increased_tumaco_categories <- simple_gprofiler(
tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]])## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSEincreased_tumaco_categories[["pvalue_plots"]][["BP"]]
increased_cali_categories <- simple_gprofiler(
tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]])## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSEincreased_cali_categories[["pvalue_plots"]][["BP"]]
There appear to be many more genes which are increased in the Tumaco samples with respect to the Cali samples.
3.2 DE: Compare clinics, eosinophil samples
The remaining cell types all have pretty strong clinic-based variance; but I am not certain if it is consistent across cell types.
table(pData(tc_eosinophils)[["condition"]])##
## Cali_cure Tumaco_cure Tumaco_failure
## 15 17 9tc_eosinophils_clinic_de_nobatch <- all_pairwise(tc_eosinophils,
model_batch = FALSE, filter = TRUE)##
## Cali_cure Tumaco_cure Tumaco_failure
## 15 17 9
tc_eosinophils_clinic_de_nobatch[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_eosinophils_clinic_table_nobatch <- combine_de_tables(
tc_eosinophils_clinic_de_nobatch, keepers = tc_cf_contrasts,
# rda = glue("rda/tc_eosinophils_clinic_table_nobatch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_nobatch-v202304.xlsx before writing the tables.## Adding venn plots for tumaco.## Adding venn plots for cure.tc_eosinophils_clinic_sig_nobatch <- extract_significant_genes(
tc_eosinophils_clinic_table_nobatch,
excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v202304.xlsx before writing the tables.tc_eosinophils_clinic_de_sva <- all_pairwise(tc_eosinophils, model_batch = "svaseq", filter = TRUE)##
## Cali_cure Tumaco_cure Tumaco_failure
## 15 17 9## Removing 0 low-count genes (10864 remaining).## Setting 1043 low elements to zero.## transform_counts: Found 1043 values equal to 0, adding 1 to the matrix.
tc_eosinophils_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_eosinophils_clinic_table_sva <- combine_de_tables(
tc_eosinophils_clinic_de_sva, keepers = tc_cf_contrasts,
# rda = glue("rda/tc_eosinophils_clinic_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for tumaco.## Adding venn plots for cure.tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
tc_eosinophils_clinic_table_sva,
excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v202304.xlsx before writing the tables.3.3 DE: Compare clinics, biopsy samples
Interestingly to me, the biopsy samples appear to have the least location-based variance. But we can perform an explicit DE and see how well that hypothesis holds up.
Note that these data include cure and fail samples for
table(pData(tc_biopsies)[["condition"]])##
## Cali_cure Tumaco_cure Tumaco_failure
## 4 9 5tc_biopsies_clinic_de_sva <- all_pairwise(tc_biopsies,
model_batch = "svaseq", filter = TRUE)##
## Cali_cure Tumaco_cure Tumaco_failure
## 4 9 5## 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.
tc_biopsies_clinic_de_sva[["deseq"]][["contrasts_performed"]]## [1] "Tumacofailure_vs_Tumacocure" "Tumacofailure_vs_Calicure"
## [3] "Tumacocure_vs_Calicure"tc_biopsies_clinic_table_sva <- combine_de_tables(
tc_biopsies_clinic_de_sva, keepers = tc_cf_contrasts,
# rda = glue("rda/tc_biopsies_clinic_table_sva-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for tumaco.## Adding venn plots for cure.tc_biopsies_clinic_sig_sva <- extract_significant_genes(
tc_biopsies_clinic_table_sva,
excel = 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-v202304.xlsx before writing the tables.3.4 DE: Compare clinics, monocyte samples
At least for the moment, I am only looking at the differences between no-batch vs. sva across clinics for the monocyte samples. This was chosen mostly arbitrarily.
3.4.1 DE: Compare clinics, monocytes without batch estimation
Our baseline is the comparison of the monocytes samples without batch in the model or surrogate estimation. In theory at least, this should correspond to the PCA plot above when no batch estimation was performed.
tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch = FALSE, filter = TRUE)##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 21 21
tc_monocytes_table_nobatch <- combine_de_tables(
tc_monocytes_de_nobatch, keepers = clinic_cf_contrasts,
# rda = glue("rda/tc_monocytes_clinic_table_nobatch-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for cali.## Adding venn plots for tumaco.## Adding venn plots for cure.## Adding venn plots for fail.tc_monocytes_sig_nobatch <- extract_significant_genes(
tc_monocytes_table_nobatch,
excel = 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-v202304.xlsx before writing the tables.3.4.2 DE: Compare clinics, monocytes with svaseq
In contrast, the following comparison should give a view of the data corresponding to the svaseq PCA plot above. In the best case scenario, we should therefore be able to see some significane differences between the Tumaco cure and fail samples.
tc_monocytes_de_sva <- all_pairwise(tc_monocytes, model_batch = "svaseq", filter = TRUE)##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 21 21## 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.
tc_monocytes_table_sva <- combine_de_tables(
tc_monocytes_de_sva, keepers = clinic_cf_contrasts,
# rda = glue("rda/tc_monocytes_clinic_table_sva-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for cali.## Adding venn plots for tumaco.## Adding venn plots for cure.## Adding venn plots for fail.tc_monocytes_sig_sva <- extract_significant_genes(
tc_monocytes_table_sva,
excel = 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-v202304.xlsx before writing the tables.3.4.3 DE Compare: How similar are the no-batch vs. SVA results?
The following block shows that these two results are exceedingly different, sugesting that the Cali cure/fail and Tumaco cure/fail cannot easily be considered in the same analysis. I did some playing around with my calculate_aucc function in this block and found that it is in some important way broken, at least if one expands the top-n genes to more than 20% of the number of genes in the data.
cali_table <- tc_monocytes_table_nobatch[["data"]][["cali"]]
table <- tc_monocytes_table_nobatch[["data"]][["tumaco"]]
cali_merged <- merge(cali_table, table, by = "row.names")
cor.test(cali_merged[, "deseq_logfc.x"], cali_merged[, "deseq_logfc.y"])##
## Pearson's product-moment correlation
##
## data: cali_merged[, "deseq_logfc.x"] and cali_merged[, "deseq_logfc.y"]
## t = 0.92, df = 11102, p-value = 0.4
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.009917 0.027280
## sample estimates:
## cor
## 0.008685cali_aucc <- calculate_aucc(cali_table, table, px = "deseq_adjp", py = "deseq_adjp",
lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc$plot
cali_table_sva <- tc_monocytes_table_sva[["data"]][["cali"]]
tumaco_table_sva <- tc_monocytes_table_sva[["data"]][["tumaco"]]
cali_merged_sva <- merge(cali_table_sva, tumaco_table_sva, by = "row.names")
cor.test(cali_merged_sva[, "deseq_logfc.x"], cali_merged_sva[, "deseq_logfc.y"])##
## Pearson's product-moment correlation
##
## data: cali_merged_sva[, "deseq_logfc.x"] and cali_merged_sva[, "deseq_logfc.y"]
## t = 16, df = 11102, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1356 0.1720
## sample estimates:
## cor
## 0.1539cali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px = "deseq_adjp",
py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_sva$plot
3.5 DE: Compare clinics, neutrophil samples
tc_neutrophils_de_nobatch <- all_pairwise(tc_neutrophils,
model_batch = FALSE, filter = TRUE)##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 20 21
tc_neutrophils_table_nobatch <- combine_de_tables(
tc_neutrophils_de_nobatch, keepers = clinic_cf_contrasts,
# rda = glue("rda/tc_neutrophils_clinic_table_nobatch-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for cali.## Adding venn plots for tumaco.## Adding venn plots for cure.## Adding venn plots for fail.tc_neutrophils_sig_nobatch <- extract_significant_genes(
tc_neutrophils_table_nobatch,
excel = 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-v202304.xlsx before writing the tables.tc_neutrophils_de_sva <- all_pairwise(tc_neutrophils,
model_batch = "svaseq", filter = TRUE)##
## Cali_cure Cali_failure Tumaco_cure Tumaco_failure
## 18 3 20 21## 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.
tc_neutrophils_table_sva <- combine_de_tables(
tc_neutrophils_de_sva, keepers = clinic_cf_contrasts,
# rda = glue("rda/tc_neutrophils_clinic_table_sva-v{ver}.rda"),
excel = 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-v202304.xlsx before writing the tables.## Adding venn plots for cali.## Adding venn plots for tumaco.## Adding venn plots for cure.## Adding venn plots for fail.tc_neutrophils_sig_sva <- extract_significant_genes(
tc_neutrophils_table_sva,
excel = 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-v202304.xlsx before writing the tables.4 GSVA Load mSigDB data
Conversely, I can load some of the MsigDB categories from broad and perform a similar analysis using goseq to see if there are over represented categories.
broad_c7 <- load_gmt_signatures(signatures = "reference/msigdb/c7.all.v7.5.1.entrez.gmt",
signature_category = "c7")
broad_c2 <- load_gmt_signatures(signatures = "reference/msigdb/c2.all.v7.5.1.entrez.gmt",
signature_category = "c2")
broad_h <- load_gmt_signatures(signatures = "reference/msigdb/h.all.v7.5.1.entrez.gmt",
signature_category = "h")
clinic_gsea_msig_c2 <- goseq_msigdb(clinic_sigenes, length_db = hs_length,
signatures = broad_c2, signature_category = "c2")## Error in "character" %in% class(sig_genes): object 'clinic_sigenes' not found4.0.1 GSEA: Compare clinics, Eosinophil samples
In the following block, I am looking at the gProfiler over represented groups observed across clinics in only the Eosinophils. First I do so for all genes(up or down), followed by only the up and down groups. Each of the following will include only the Reactome and GO:BP plots. These searches did not have too many other hits, excepting the transcription factor database.
tc_eosinophils_gp <- simple_gprofiler(tc_eosinophils_sigenes)## Error in "character" %in% class(sig_genes): object 'tc_eosinophils_sigenes' not foundtc_eosinophils_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_eosinophils_gp' not foundtc_eosinophils_up_gp <- simple_gprofiler(tc_eosinophils_sigenes_up)## Error in "character" %in% class(sig_genes): object 'tc_eosinophils_sigenes_up' not foundtc_eosinophils_up_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_up_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_eosinophils_up_gp' not foundtc_eosinophils_down_gp <- simple_gprofiler(tc_eosinophils_sigenes_down)## Error in "character" %in% class(sig_genes): object 'tc_eosinophils_sigenes_down' not foundtc_eosinophils_down_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not foundtc_eosinophils_down_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_eosinophils_down_gp' not found4.0.2 GSEA: Compare clinics, Monocyte samples
In the following block I repeated the above query, but this time looking at the monocyte samples.
tc_monocytes_gp <- simple_gprofiler(tc_monocytes_sigenes)## Error in "character" %in% class(sig_genes): object 'tc_monocytes_sigenes' not foundtc_monocytes_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_monocytes_gp' not foundtc_monocytes_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_monocytes_gp' not foundtc_monocytes_up_gp <- simple_gprofiler(tc_monocytes_sigenes_up)## Error in "character" %in% class(sig_genes): object 'tc_monocytes_sigenes_up' not foundtc_monocytes_up_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_monocytes_up_gp' not foundtc_monocytes_up_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_monocytes_up_gp' not foundtc_monocytes_down_gp <- simple_gprofiler(tc_monocytes_sigenes_down)## Error in "character" %in% class(sig_genes): object 'tc_monocytes_sigenes_down' not foundtc_monocytes_down_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_monocytes_down_gp' not foundtc_monocytes_down_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_monocytes_down_gp' not found4.0.3 GSEA: Compare clinics, Neutrophil samples
Ibid. This time looking at the Neutrophils. Thus the first two images should be a superset of the second and third pairs of images; assuming that the genes in the up/down list do not cause the groups to no longer be significant. Interestingly, the reactome search did not return any hits for the increased search.
tc_neutrophils_gp <- simple_gprofiler(tc_neutrophils_sigenes)## Error in "character" %in% class(sig_genes): object 'tc_neutrophils_sigenes' not found## tc_neutrophils_gp$pvalue_plots$REAC ## no hits
tc_neutrophils_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_neutrophils_gp' not foundtc_neutrophils_gp$pvalue_plots$TF## Error in eval(expr, envir, enclos): object 'tc_neutrophils_gp' not foundtc_neutrophils_up_gp <- simple_gprofiler(tc_neutrophils_sigenes_up)## Error in "character" %in% class(sig_genes): object 'tc_neutrophils_sigenes_up' not found## tc_neutrophils_up_gp$pvalue_plots$REAC ## No hits
tc_neutrophils_up_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_neutrophils_up_gp' not foundtc_neutrophils_down_gp <- simple_gprofiler(tc_neutrophils_sigenes_down)## Error in "character" %in% class(sig_genes): object 'tc_neutrophils_sigenes_down' not foundtc_neutrophils_down_gp$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'tc_neutrophils_down_gp' not foundtc_neutrophils_down_gp$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tc_neutrophils_down_gp' not found5 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
5.1 GSEA: Extract clinic-specific genes
Given the above comparisons, we can extract some gene sets which resulted from those DE analyses and eventually perform some ontology/KEGG/reactome/etc searches. This reminds me, I want to make my extract_significant_ functions to return gene-set data structures and my various ontology searches to take them as inputs. This should help avoid potential errors when extracting up/down genes.
clinic_sigenes_up <- rownames(tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]])
clinic_sigenes_down <- rownames(tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]])
clinic_sigenes <- c(clinic_sigenes_up, clinic_sigenes_down)
tc_eosinophils_sigenes_up <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_eosinophils_sigenes_down <- rownames(tc_eosinophils_clinic_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_monocytes_sigenes_up <- rownames(tc_monocytes_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_monocytes_sigenes_down <- rownames(tc_monocytes_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_neutrophils_sigenes_up <- rownames(tc_neutrophils_sig_sva[["deseq"]][["ups"]][["cure"]])
tc_neutrophils_sigenes_down <- rownames(tc_neutrophils_sig_sva[["deseq"]][["downs"]][["cure"]])
tc_eosinophils_sigenes <- c(tc_eosinophils_sigenes_up,
tc_eosinophils_sigenes_down)
tc_monocytes_sigenes <- c(tc_monocytes_sigenes_up,
tc_monocytes_sigenes_down)
tc_neutrophils_sigenes <- c(tc_neutrophils_sigenes_up,
tc_neutrophils_sigenes_down)5.2 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.
5.2.1 GSEA: Compare clinics, all samples
Note that in the following block I used the function simple_gprofiler(), but later in this document I will use all_gprofiler(). The first invocation limits the search to a single table, while the second will iterate over every result in a pairwise differential expression analysis.
In this instance, we are looking at the vector of gene IDs deemed significantly different between the two clinics in either the up or down direction.
One other thing worth noting, the new version of gProfiler provides some fun interactive plots. I will add an example here.
tc_eosionphil_gprofiler <- simple_gprofiler(tc_eosinophils_sigenes_up)## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSEclinic_gp <- simple_gprofiler(clinic_sigenes)## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSE
## No results to show
## Please make sure that the organism is correct or set significant = FALSEclinic_gp$pvalue_plots$REAC
clinic_gp$pvalue_plots$BP
clinic_gp$pvalue_plots$TF
clinic_gp$interactive_plots$GO6 Tumaco and Cali, cure vs. fail
In all of the above, we are looking to understand the differences between the two location. Let us now step back and perform the original question: fail/cure without regard to location.
I performed this query with a few different parameters, notably with(out) sva and again using each cell type, including biopsies. The main reasion I am keeping these comparisons is in the relatively weak hope that there will be sufficient signal in the full dataset that it might be able to overcome the apparently ridiculous batch effect from the two clinics.
6.1 All cell types together, with(out) SVA
tc_all_cf_de_sva <- all_pairwise(tc_valid, filter = TRUE, model_batch = "svaseq")##
## cure failure
## 122 62## Removing 0 low-count genes (14290 remaining).## Setting 27033 low elements to zero.## transform_counts: Found 27033 values equal to 0, adding 1 to the matrix.tc_all_cf_table_sva <- combine_de_tables(
tc_all_cf_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/tc_valid_cf_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.tc_all_cf_sig_sva <- extract_significant_genes(
tc_all_cf_table_sva,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_sva-v202304.xlsx before writing the tables.tc_all_cf_de_batch <- all_pairwise(tc_valid, filter = TRUE, model_batch = TRUE)##
## cure failure
## 122 62
##
## 1 2 3
## 83 50 51tc_all_cf_table_batch <- combine_de_tables(
tc_all_cf_de_batch,
keepers = t_cf_contrast,
# rda = glue("rda/tc_valid_cf_table_batch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_batch-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.tc_all_cf_sig_batch <- extract_significant_genes(
tc_all_cf_table_batch,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v202304.xlsx before writing the tables.6.2 Biopsies, with(out) SVA
In the following block, we repeat the same question, but using only the biopsy samples from both clinics.
tc_biopsies_cf <- set_expt_conditions(tc_biopsies, fact = "finaloutcome")##
## cure failure
## 13 5tc_biopsies_cf_de_sva <- all_pairwise(tc_biopsies_cf, filter = TRUE, model_batch = "svaseq")##
## cure failure
## 13 5## Removing 0 low-count genes (13608 remaining).## Setting 222 low elements to zero.## transform_counts: Found 222 values equal to 0, adding 1 to the matrix.tc_biopsies_cf_table_sva <- combine_de_tables(
tc_biopsies_cf_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/tc_biopsies_cf_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/Biopsies/tc_biopsies_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Biopsies/tc_biopsies_cf_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.tc_biopsies_cf_sig_sva <- extract_significant_genes(
tc_biopsies_cf_table_sva,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_sva-v202304.xlsx before writing the tables.tc_biopsies_cf_de_batch <- all_pairwise(tc_biopsies_cf, filter = TRUE, model_batch = TRUE)##
## cure failure
## 13 5
##
## 1
## 18tc_biopsies_cf_table_batch <- combine_de_tables(
tc_biopsies_cf_de_batch, keepers = t_cf_contrast,
# rda = glue("rda/tc_biopsies_cf_table_batch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_table_batch-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.tc_biopsies_cf_sig_batch <- extract_significant_genes(
tc_biopsies_cf_table_batch,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_biopsies_cf_sig_batch-v202304.xlsx before writing the tables.6.3 Eosinophils, with(out) SVA
In the following block, we repeat the same question, but using only the Eosinophil samples from both clinics.
tc_eosinophils_cf <- set_expt_conditions(tc_eosinophils, fact = "finaloutcome")##
## cure failure
## 32 9tc_eosinophils_cf_de_sva <- all_pairwise(tc_eosinophils_cf, filter = TRUE, model_batch = "svaseq")##
## cure failure
## 32 9## Removing 0 low-count genes (10864 remaining).## Setting 856 low elements to zero.## transform_counts: Found 856 values equal to 0, adding 1 to the matrix.tc_eosinophils_cf_table_sva <- combine_de_tables(
tc_eosinophils_cf_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/tc_eosinophils_cf_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/Eosinophils/tc_eosinophils_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Eosinophils/tc_eosinophils_cf_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.tc_eosinophils_cf_sig_sva <- extract_significant_genes(
tc_eosinophils_cf_table_sva,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_sva-v202304.xlsx before writing the tables.tc_eosinophils_cf_de_batch <- all_pairwise(tc_eosinophils_cf, filter = TRUE, model_batch = TRUE)##
## cure failure
## 32 9
##
## 3 2 1
## 13 14 14tc_eosinophils_cf_table_batch <- combine_de_tables(
tc_eosinophils_cf_de_batch, keepers = t_cf_contrast,
# rda = glue("rda/tc_eosinophils_cf_table_batch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_table_batch-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.tc_eosinophils_cf_sig_batch <- extract_significant_genes(
tc_eosinophils_cf_table_batch,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v202304.xlsx before writing the tables.6.4 Monocytes, with(out) SVA
Repeat yet again, this time with the monocyte samples. The idea is to see if there is a cell type which is particularly good (or bad) at discriminating the two clinics.
tc_monocytes_cf <- set_expt_conditions(tc_monocytes, fact = "finaloutcome")##
## cure failure
## 39 24tc_monocytes_cf_de_sva <- all_pairwise(tc_monocytes_cf, filter = TRUE, model_batch = "svaseq")##
## cure failure
## 39 24## Removing 0 low-count genes (11104 remaining).## Setting 1326 low elements to zero.## transform_counts: Found 1326 values equal to 0, adding 1 to the matrix.tc_monocytes_cf_table_sva <- combine_de_tables(
tc_monocytes_cf_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/tc_monocytes_cf_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/Monocytes/tc_monocytes_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Monocytes/tc_monocytes_cf_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.tc_monocytes_cf_sig_sva <- extract_significant_genes(
tc_monocytes_cf_table_sva,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_sva-v202304.xlsx before writing the tables.tc_monocytes_cf_de_batch <- all_pairwise(tc_monocytes_cf, filter = TRUE, model_batch = TRUE)##
## cure failure
## 39 24
##
## 3 2 1
## 19 18 26tc_monocytes_cf_table_batch <- combine_de_tables(
tc_monocytes_cf_de_batch, keepers = t_cf_contrast,
# rda = glue("rda/tc_monocytes_cf_table_batch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_table_batch-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.tc_monocytes_cf_sig_batch <- extract_significant_genes(
tc_monocytes_cf_table_batch,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_monocytes_cf_sig_batch-v202304.xlsx before writing the tables.6.5 Neutrophils, with(out) SVA
Last try, this time using the Neutrophil samples.
tc_neutrophils_cf <- set_expt_conditions(tc_neutrophils, fact = "finaloutcome")##
## cure failure
## 38 24tc_neutrophils_cf_de_sva <- all_pairwise(tc_neutrophils_cf,
filter = TRUE, model_batch = "svaseq")##
## cure failure
## 38 24## Removing 0 low-count genes (9242 remaining).## Setting 1562 low elements to zero.## transform_counts: Found 1562 values equal to 0, adding 1 to the matrix.tc_neutrophils_cf_table_sva <- combine_de_tables(
tc_neutrophils_cf_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/tc_neutrophils_cf_table_sva-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/Neutrophils/tc_neutrophils_cf_table_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/Neutrophils/tc_neutrophils_cf_table_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.tc_neutrophils_cf_sig_sva <- extract_significant_genes(
tc_neutrophils_cf_table_sva,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_sva-v202304.xlsx before writing the tables.tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter = TRUE, model_batch = TRUE)##
## cure failure
## 38 24
##
## 3 2 1
## 19 18 25## Error in e$fun(obj, substitute(ex), parent.frame(), e$data): worker initialization failed: there is no package called ‘hpgltools’tc_neutrophils_cf_table_batch <- combine_de_tables(
tc_neutrophils_cf_de_batch, keepers = t_cf_contrast,
# rda = glue("rda/tc_neutrophils_cf_table_batch-v{ver}.rda"),
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_table_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_table_batch-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'tc_neutrophils_cf_de_batch' not foundtc_neutrophils_cf_sig_batch <- extract_significant_genes(
tc_neutrophils_cf_table_batch,
excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))## Deleting the file analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v202304.xlsx before writing the tables.## Error in extract_significant_genes(tc_neutrophils_cf_table_batch, excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx")): object 'tc_neutrophils_cf_table_batch' not found7 Only Tumaco samples
Start over, this time with only the samples from Tumaco. We currently are assuming these will prove to be the only analyses used for final interpretation. This is primarily because we have insufficient failed treatment samples from Cali.
xlsx_prefix <- "analyses/4_tumaco/DE_Cure_vs_Fail"7.1 All samples
Start by considering all Tumaco cell types. Note that in this case we only use SVA, primarily because I am not certain what would be an appropriate batch factor, perhaps visit?
t_cf_clinical_de_sva <- all_pairwise(t_clinical, model_batch = "svaseq", filter = TRUE)##
## cure failure
## 67 56## 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.t_cf_clinical_table_sva <- combine_de_tables(
t_cf_clinical_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/t_clinical_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_clinical_sig_sva <- extract_significant_genes(
t_cf_clinical_table_sva,
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_clinical_sig_sva$deseq$ups[[1]])## [1] 93 50dim(t_cf_clinical_sig_sva$deseq$downs[[1]])## [1] 183 507.2 gProfiler search of all samples
The following gProfiler searches use the all_gprofiler() function instead of simple_gprofiler(). As a result, the results are separated by {contrast}_{direction}. Thus ‘outcome_down’.
The same plots are available as the previous gProfiler searches, but in many of the following runs, I used the dotplot() function to get a slightly different view of the results.
t_cf_clinical_gp <- all_gprofiler(t_cf_clinical_sig_sva)
## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["WP_enrich"]])
## Transcription factor database of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["TF_enrich"]])
## Reactome of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["REAC_enrich"]])
## GO of the down c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_down"]][["GO_enrich"]])
t_cf_clinical_gp[["outcome_up"]][["pvalue_plots"]][["BP"]]
## Reactome of the down c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["REAC_enrich"]])
8 Visit comparisons
Later in this document I do a bunch of visit/cf comparisons. In this block I want to explicitly only compare v1 to other visits. This is something I did quite a lot in the 2019 datasets, but never actually moved to this document.
v1_vs_later <- all_pairwise(tc_v1vs, model_batch = "svaseq", filter = TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'tc_v1vs' not foundv1_vs_later_table <- combine_de_tables(
v1_vs_later, keepers = visit_v1later,
excel = glue("excel/v1_vs_later_tables-v{ver}.xlsx"))## Deleting the file excel/v1_vs_later_tables-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'v1_vs_later' not foundv1_vs_later_sig <- extract_significant_genes(
v1_vs_later_table,
excel = glue("excel/v1_vs_later_sig-v{ver}.xlsx"))## Deleting the file excel/v1_vs_later_sig-v202304.xlsx before writing the tables.## Error in extract_significant_genes(v1_vs_later_table, excel = glue("excel/v1_vs_later_sig-v{ver}.xlsx")): object 'v1_vs_later_table' not foundv1later_gp <- all_gprofiler(v1_vs_later_sig)## Error in all_gprofiler(v1_vs_later_sig): object 'v1_vs_later_sig' not foundv1later_gp[[1]]$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'v1later_gp' not foundv1later_gp[[2]]$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'v1later_gp' not foundtv1_vs_later <- all_pairwise(t_v1vs, model_batch = "svaseq", filter = TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 't_v1vs' not foundtv1_vs_later_table <- combine_de_tables(
tv1_vs_later, keepers = visit_v1later,
excel = glue("excel/tv1_vs_later_tables-v{ver}.xlsx"))## Deleting the file excel/tv1_vs_later_tables-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'tv1_vs_later' not foundtv1_vs_later_sig <- extract_significant_genes(
tv1_vs_later_table,
excel = glue("excel/tv1_vs_later_sig-v{ver}.xlsx"))## Deleting the file excel/tv1_vs_later_sig-v202304.xlsx before writing the tables.## Error in extract_significant_genes(tv1_vs_later_table, excel = glue("excel/tv1_vs_later_sig-v{ver}.xlsx")): object 'tv1_vs_later_table' not foundv1later_gp <- all_gprofiler(v1_vs_later_sig)## Error in all_gprofiler(v1_vs_later_sig): object 'v1_vs_later_sig' not foundv1later_gp[[1]]$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'v1later_gp' not foundv1later_gp[[2]]$pvalue_plots$REAC## Error in eval(expr, envir, enclos): object 'v1later_gp' not foundtv1later_gp <- all_gprofiler(tv1_vs_later_sig)## Error in all_gprofiler(tv1_vs_later_sig): object 'tv1_vs_later_sig' not foundtv1later_gp[[1]]$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tv1later_gp' not foundtv1later_gp[[2]]$pvalue_plots$BP## Error in eval(expr, envir, enclos): object 'tv1later_gp' not found9 Sex comparison
tc_sex_de <- all_pairwise(tc_sex, model_batch = "svaseq", filter = TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'tc_sex' not foundtc_sex_table <- combine_de_tables(
tc_sex_de, excel = glue("excel/tc_sex_table-v{ver}.xlsx"))## Deleting the file excel/tc_sex_table-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'tc_sex_de' not foundtc_sex_sig <- extract_significant_genes(
tc_sex_table, excel = glue("excel/tc_sex_sig-v{ver}.xlsx"))## Deleting the file excel/tc_sex_sig-v202304.xlsx before writing the tables.## Error in extract_significant_genes(tc_sex_table, excel = glue("excel/tc_sex_sig-v{ver}.xlsx")): object 'tc_sex_table' not foundtc_sex_gp <- all_gprofiler(tc_sex_sig)## Error in all_gprofiler(tc_sex_sig): object 'tc_sex_sig' not foundt_sex <- subset_expt(tc_sex, subset = "clinic == 'Tumaco'")## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'tc_sex' not foundt_sex_de <- all_pairwise(t_sex, model_batch = "svaseq", filter = TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 't_sex' not foundt_sex_table <- combine_de_tables(
t_sex_de, excel = glue("excel/t_sex_table-v{ver}.xlsx"))## Deleting the file excel/t_sex_table-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 't_sex_de' not foundt_sex_sig <- extract_significant_genes(
t_sex_table, excel = glue("excel/t_sex_sig-v{ver}.xlsx"))## Deleting the file excel/t_sex_sig-v202304.xlsx before writing the tables.## Error in extract_significant_genes(t_sex_table, excel = glue("excel/t_sex_sig-v{ver}.xlsx")): object 't_sex_table' not foundt_sex_gp <- all_gprofiler(t_sex_sig)## Error in all_gprofiler(t_sex_sig): object 't_sex_sig' not found9.0.1 Separate the Tumaco data by visit
One of the most compelling ideas in the data is the opportunity to find genes in the first visit which may help predict the likelihood that a person will respond well to treatment. The following block will therefore look at cure/fail from Tumaco at visit 1.
9.0.1.1 Cure/Fail, Tumaco Visit 1
t_cf_clinical_v1_de_sva <- all_pairwise(tv1_samples, model_batch = "svaseq", filter = TRUE)##
## cure failure
## 30 24## 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.t_cf_clinical_v1_table_sva <- combine_de_tables(
t_cf_clinical_v1_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/t_clinical_v1_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v1_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_clinical_v1_sig_sva <- extract_significant_genes(
t_cf_clinical_v1_table_sva,
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v1_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_clinical_v1_sig_sva$deseq$ups[[1]])## [1] 28 50dim(t_cf_clinical_v1_sig_sva$deseq$downs[[1]])## [1] 74 509.0.1.2 Cure/Fail, Tumaco Visit 2
The visit 2 and visit 3 samples are interesting because they provide an opportunity to see if we can observe changes in response in the middle and end of treatment…
t_cf_clinical_v2_de_sva <- all_pairwise(tv2_samples, model_batch = "svaseq", filter = TRUE)##
## cure failure
## 20 15## Removing 0 low-count genes (11559 remaining).## Setting 2848 low elements to zero.## transform_counts: Found 2848 values equal to 0, adding 1 to the matrix.t_cf_clinical_v2_table_sva <- combine_de_tables(
t_cf_clinical_v2_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/t_clinical_v2_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v2_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_clinical_v2_sig_sva <- extract_significant_genes(
t_cf_clinical_v2_table_sva,
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v2_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_clinical_v2_sig_sva$deseq$ups[[1]])## [1] 51 50dim(t_cf_clinical_v2_sig_sva$deseq$downs[[1]])## [1] 15 509.0.1.3 Cure/Fail, Tumaco Visit 3
t_cf_clinical_v3_de_sva <- all_pairwise(tv3_samples, model_batch = "svaseq", filter = TRUE)##
## cure failure
## 17 17## Removing 0 low-count genes (11449 remaining).## Setting 1878 low elements to zero.## transform_counts: Found 1878 values equal to 0, adding 1 to the matrix.t_cf_clinical_v3_table_sva <- combine_de_tables(
t_cf_clinical_v3_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/t_clinical_v3_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v3_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_clinical_v3_sig_sva <- extract_significant_genes(
t_cf_clinical_v3_table_sva,
excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/All_Samples/t_clinical_v3_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_clinical_v3_sig_sva$deseq$ups[[1]])## [1] 120 50dim(t_cf_clinical_v3_sig_sva$deseq$downs[[1]])## [1] 62 509.0.1.4 Visit 1 gProfiler searches
It looks like there are very few groups in the visit 1 significant genes.
t_cf_clinical_v1_sig_sva_gp <- all_gprofiler(t_cf_clinical_v1_sig_sva)
## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v1_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.1.5 Visit 2 gProfiler searches
Up: 74 GO, 4 KEGG, 6 reactome, 4 WP, 56 TF, 1 miRNA, 0 HP/HPA/CORUM. Down: 19 GO, 1 KEGG, 1 HP, 2 HPA, 0 reactome/wp/tf/corum
t_cf_clinical_v2_sig_sva_gp <- all_gprofiler(t_cf_clinical_v2_sig_sva)
## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.1.6 Visit 3 gProfiler searches
Up: 120 genes; 141 GO, 1 KEGG, 5 Reactome, 2 WP, 30 TF, 1 miRNA, 0 HPA/CORUM/HP Down: 62 genes; 30 GO, 2 KEGG, 1 Reactome, 0 WP/TF/miRNA/HPA/CORUM/HP,
t_cf_clinical_v3_sig_sva_gp <- all_gprofiler(t_cf_clinical_v3_sig_sva)
## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.2 Repeat no biopsies
The biopsy samples are problematic for a few reasons, so let us repeat without them.
t_cf_clinical_nobiop_de_sva <- all_pairwise(t_clinical_nobiop,
model_batch = "svaseq", filter = TRUE)##
## cure failure
## 58 51## 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.t_cf_clinical_nobiop_table_sva <- combine_de_tables(
t_cf_clinical_nobiop_de_sva, keepers = t_cf_contrast,
# rda = glue("rda/t_clinical_nobiop_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/No_Biopsies/t_clinical_nobiop_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_clinical_nobiop_sig_sva <- extract_significant_genes(
t_cf_clinical_nobiop_table_sva,
excel = glue("{xlsx_prefix}/No_Biopsies/t_clinical_nobiop_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/No_Biopsies/t_clinical_nobiop_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_clinical_nobiop_sig_sva$deseq$ups[[1]])## [1] 137 50dim(t_cf_clinical_nobiop_sig_sva$deseq$downs[[1]])## [1] 73 509.0.2.1 gProfiler: Clinical no biopsies
Up: 137 genes; 88 GO, 0 KEGG, 6 Reactome, 1 WP, 46 TF, 1 miRNA, 0 others Down: 73 genes; 78 GO, 1 KEGG, 1 Reactome, 9 TF, 0 others
t_cf_clinical_nobiop_sig_sva_gp <- all_gprofiler(t_cf_clinical_nobiop_sig_sva)
enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.3 By cell type
Now let us switch our view to each individual cell type collected. The hope here is that we will be able to learn some cell-specific differences in the response for people who did(not) respond well.
9.0.3.1 Cure/Fail, Biopsies
t_cf_biopsy_de_sva <- all_pairwise(t_biopsies, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 9 5## Removing 0 low-count genes (13506 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.t_cf_biopsy_table_sva <- combine_de_tables(
t_cf_biopsy_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_biopsy_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Biopsies/t_biopsy_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Biopsies/t_biopsy_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_biopsy_sig_sva <- extract_significant_genes(
t_cf_biopsy_table_sva,
excel = glue("{xlsx_prefix}/Biopsies/t_cf_biopsy_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Biopsies/t_cf_biopsy_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_biopsy_sig_sva$deseq$ups[[1]])## [1] 17 50dim(t_cf_biopsy_sig_sva$deseq$downs[[1]])## [1] 11 509.0.3.2 gProfiler: Biopsies
Up: 17 genes; 74 GO, 3 KEGG, 1 Reactome, 3 WP, 1 TF, 0 others Down: 11 genes; 2 GO, 0 others
t_cf_biopsy_sig_sva_gp <- all_gprofiler(t_cf_biopsy_sig_sva)
enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.3.3 Cure/Fail, Monocytes
Same question, but this time looking at monocytes. In addition, this comparison was done twice, once using SVA and once using visit as a batch factor.
t_cf_monocyte_de_sva <- all_pairwise(t_monocytes, model_batch = "svaseq",
filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 21 21## Removing 0 low-count genes (10859 remaining).## Setting 730 low elements to zero.## transform_counts: Found 730 values equal to 0, adding 1 to the matrix.t_cf_monocyte_tables_sva <- combine_de_tables(
t_cf_monocyte_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_monocyte_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_monocyte_sig_sva <- extract_significant_genes(
t_cf_monocyte_tables_sva,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_monocyte_sig_sva$deseq$ups[[1]])## [1] 60 50dim(t_cf_monocyte_sig_sva$deseq$downs[[1]])## [1] 53 50t_cf_monocyte_de_batchvisit <- all_pairwise(t_monocytes, model_batch = TRUE, filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 21 21
##
## 3 2 1
## 13 13 16t_cf_monocyte_tables_batchvisit <- combine_de_tables(
t_cf_monocyte_de_batchvisit, keepers = cf_contrast,
# rda = glue("rda/t_monocyte_cf_table_batchvisit-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_tables_batchvisit-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_cf_tables_batchvisit-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.t_cf_monocyte_sig_batchvisit <- extract_significant_genes(
t_cf_monocyte_tables_batchvisit,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_cf_sig_batchvisit-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_cf_sig_batchvisit-v202304.xlsx before writing the tables.dim(t_cf_monocyte_sig_batchvisit$deseq$ups[[1]])## [1] 43 50dim(t_cf_monocyte_sig_batchvisit$deseq$downs[[1]])## [1] 93 509.0.3.4 gProfiler: Monocytes
Now that I am looking back over these results, I am not compeltely certain why I only did the gprofiler search for the sva data…
Up: 60 genes; 12 GO, 1 KEGG, 1 WP, 4 TF, 0 others Down: 53 genes; 26 GO, 1 KEGG, 1 Reactome, 2 TF, 0 others
t_cf_monocyte_sig_sva_gp <- all_gprofiler(t_cf_monocyte_sig_sva)
enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
t_cf_monocyte_sig_batch_gp <- all_gprofiler(t_cf_monocyte_sig_batchvisit)
enrichplot::dotplot(t_cf_monocyte_sig_batch_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_monocyte_sig_batch_gp[["outcome_up"]][["HP_enrich"]])
9.0.4 Individual visits, Monocytes
Now focus in on the monocyte samples on a per-visit basis.
9.0.4.1 Visit 1
t_cf_monocyte_v1_de_sva <- all_pairwise(tv1_monocytes, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 8 8## Removing 0 low-count genes (10479 remaining).## Setting 187 low elements to zero.## transform_counts: Found 187 values equal to 0, adding 1 to the matrix.t_cf_monocyte_v1_tables_sva <- combine_de_tables(
t_cf_monocyte_v1_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_monocyte_v1_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v1_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_monocyte_v1_sig_sva <- extract_significant_genes(
t_cf_monocyte_v1_tables_sva,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v1_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_monocyte_v1_sig_sva$deseq$ups[[1]])## [1] 14 50dim(t_cf_monocyte_v1_sig_sva$deseq$downs[[1]])## [1] 52 509.0.4.2 Visit 2
t_cf_monocyte_v2_de_sva <- all_pairwise(tv2_monocytes, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 7 6## Removing 0 low-count genes (10520 remaining).## Setting 115 low elements to zero.## transform_counts: Found 115 values equal to 0, adding 1 to the matrix.t_cf_monocyte_v2_tables_sva <- combine_de_tables(
t_cf_monocyte_v2_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_monocyte_v2_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v2_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_monocyte_v2_sig_sva <- extract_significant_genes(
t_cf_monocyte_v2_tables_sva,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v2_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_monocyte_v2_sig_sva$deseq$ups[[1]])## [1] 0 50dim(t_cf_monocyte_v2_sig_sva$deseq$downs[[1]])## [1] 1 509.0.4.3 Visit 3
t_cf_monocyte_v3_de_sva <- all_pairwise(tv3_monocytes, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 6 7## Removing 0 low-count genes (10374 remaining).## Setting 55 low elements to zero.## transform_counts: Found 55 values equal to 0, adding 1 to the matrix.t_cf_monocyte_v3_tables_sva <- combine_de_tables(
t_cf_monocyte_v3_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_monocyte_v3_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v3_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_monocyte_v3_sig_sva <- extract_significant_genes(
t_cf_monocyte_v3_tables_sva,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_v3_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_monocyte_v3_sig_sva$deseq$ups[[1]])## [1] 0 50dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 509.0.4.4 Monocytes: Compare sva to batch-in-model
sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][[1]],
tbl2 = t_cf_monocyte_tables_batchvisit[["data"]][[1]],
py = "deseq_adjp", ly = "deseq_logfc")
sva_aucc## $aucc
## [1] 0.6943
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 180, df = 10857, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8611 0.8705
## sample estimates:
## cor
## 0.8659
##
##
## $plot
shared_ids <- rownames(t_cf_monocyte_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_monocyte_tables_batchvisit[["data"]][[1]])
first <- t_cf_monocyte_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_monocyte_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 180, df = 10857, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8611 0.8705
## sample estimates:
## cor
## 0.86599.0.4.4.1 gProfiler: Monocytes by visit, V1
V1: Up: 14 genes; No categories V1: Down: 52 genes; 20 GO, 5 TF
t_cf_monocyte_v1_sig_sva_gp <- all_gprofiler(t_cf_monocyte_v1_sig_sva)
enrichplot::dotplot(t_cf_monocyte_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.4.4.2 gProfiler: Monocytes by visit, V2
V2: Up: 1 gene V2: Down: 0 genes.
9.0.4.4.3 gProfiler: Monocytes by visit, V3
V3: Up: 4 genes. V3: Down: 0 genes.
9.0.5 Neutrophil samples
Switch context to the Neutrophils, once again repeat the analysis using SVA and visit as a batch factor.
t_cf_neutrophil_de_sva <- all_pairwise(t_neutrophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 20 21## 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.t_cf_neutrophil_tables_sva <- combine_de_tables(
t_cf_neutrophil_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_neutrophil_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_neutrophil_sig_sva <- extract_significant_genes(
t_cf_neutrophil_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_sig_sva$deseq$ups[[1]])## [1] 84 50dim(t_cf_neutrophil_sig_sva$deseq$downs[[1]])## [1] 29 50t_cf_neutrophil_de_batchvisit <- all_pairwise(t_neutrophils, model_batch = TRUE, filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 20 21
##
## 3 2 1
## 12 13 16t_cf_neutrophil_tables_batchvisit <- combine_de_tables(
t_cf_neutrophil_de_batchvisit, keepers = cf_contrast,
# rda = glue("rda/t_neutrophil_cf_table_batchvisit-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_batchvisit-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_cf_tables_batchvisit-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.t_cf_neutrophil_sig_batchvisit <- extract_significant_genes(
t_cf_neutrophil_tables_batchvisit,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_batchvisit-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_cf_sig_batchvisit-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_sig_batchvisit$deseq$ups[[1]])## [1] 92 50dim(t_cf_neutrophil_sig_batchvisit$deseq$downs[[1]])## [1] 47 509.0.5.1 gProfiler: Neutrophils
Up: 84 genes; 5 GO, 2 Reactome, 3 TF, no others. Down: 29 genes: 12 GO, 1 Reactome, 1 TF, 1 miRNA, 11 HP, 0 others
t_cf_neutrophil_sig_sva_gp <- all_gprofiler(t_cf_neutrophil_sig_sva)
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_down"]][["HP_enrich"]])
9.0.5.2 Neutrophils by visit
When I did this with the monocytes, I split it up into multiple blocks for each visit. This time I am just going to run them all together.
visitcf_factor <- paste0("v", pData(t_neutrophils)[["visitnumber"]],
pData(t_neutrophils)[["finaloutcome"]])
t_neutrophil_visitcf <- set_expt_conditions(t_neutrophils, fact=visitcf_factor)##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 8 8 7 6 5 7t_cf_neutrophil_visits_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
filter = TRUE)##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 8 8 7 6 5 7## 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.
t_cf_neutrophil_visits_tables_sva <- combine_de_tables(
t_cf_neutrophil_visits_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_cf_neutrophil_visits_sig_sva <- extract_significant_genes(
t_cf_neutrophil_visits_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_visits_sig_sva$deseq$ups[[1]])## [1] 12 50dim(t_cf_neutrophil_visits_sig_sva$deseq$downs[[1]])## [1] 6 50t_cf_neutrophil_v1_de_sva <- all_pairwise(tv1_neutrophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 8 8## Removing 0 low-count genes (8715 remaining).## Setting 145 low elements to zero.## transform_counts: Found 145 values equal to 0, adding 1 to the matrix.t_cf_neutrophil_v1_tables_sva <- combine_de_tables(
t_cf_neutrophil_v1_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_neutrophil_v1_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v1_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_neutrophil_v1_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v1_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v1_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v1_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_v1_sig_sva$deseq$ups[[1]])## [1] 5 50dim(t_cf_neutrophil_v1_sig_sva$deseq$downs[[1]])## [1] 8 50t_cf_neutrophil_v2_de_sva <- all_pairwise(tv2_neutrophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 7 6## Removing 0 low-count genes (8450 remaining).## Setting 78 low elements to zero.## transform_counts: Found 78 values equal to 0, adding 1 to the matrix.t_cf_neutrophil_v2_tables_sva <- combine_de_tables(
t_cf_neutrophil_v2_de_sva,
keepers = cf_contrast,
# rda = glue("rda/t_neutrophil_v2_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v2_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_neutrophil_v2_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v2_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v2_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v2_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 50dim(t_cf_neutrophil_v2_sig_sva$deseq$downs[[1]])## [1] 3 50t_cf_neutrophil_v3_de_sva <- all_pairwise(tv3_neutrophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 5 7## Removing 0 low-count genes (8503 remaining).## Setting 83 low elements to zero.## transform_counts: Found 83 values equal to 0, adding 1 to the matrix.t_cf_neutrophil_v3_tables_sva <- combine_de_tables(
t_cf_neutrophil_v3_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_neutrophil_v3_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v3_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_neutrophil_v3_sig_sva <- extract_significant_genes(
t_cf_neutrophil_v3_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_v3_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_v3_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_neutrophil_v3_sig_sva$deseq$ups[[1]])## [1] 5 50dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])## [1] 4 509.0.5.2.1 gProfiler: Neutrophils by visit, V1
V1: Up: 5 genes V1: Down: 8 genes; 14 GO.
t_cf_neutrophil_v1_sig_sva_gp <- all_gprofiler(t_cf_neutrophil_v1_sig_sva)
enrichplot::dotplot(t_cf_neutrophil_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
9.0.5.2.2 gProfiler: Neutrophils by visit, V2
Up: 5 genes; 3 GO, 10 TF. Down: 1 gene.
9.0.5.3 Neutrophils: Compare sva to batch-in-model
sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][[1]],
tbl2 = t_cf_neutrophil_tables_batchvisit[["data"]][[1]],
py = "deseq_adjp", ly = "deseq_logfc")
sva_aucc## $aucc
## [1] 0.611
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 192, df = 9097, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8915 0.8996
## sample estimates:
## cor
## 0.8956
##
##
## $plot
shared_ids <- rownames(t_cf_neutrophil_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_neutrophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_neutrophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_neutrophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 192, df = 9097, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8915 0.8996
## sample estimates:
## cor
## 0.89569.0.6 Eosinophils
This time, with feeling! Repeating the same set of tasks with the eosinophil samples.
t_cf_eosinophil_de_sva <- all_pairwise(t_eosinophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 17 9## Removing 0 low-count genes (10530 remaining).## Setting 325 low elements to zero.## transform_counts: Found 325 values equal to 0, adding 1 to the matrix.t_cf_eosinophil_tables_sva <- combine_de_tables(
t_cf_eosinophil_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_eosinophil_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_eosinophil_sig_sva <- extract_significant_genes(
t_cf_eosinophil_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_sig_sva$deseq$ups[[1]])## [1] 116 50dim(t_cf_eosinophil_sig_sva$deseq$downs[[1]])## [1] 74 50t_cf_eosinophil_de_batchvisit <- all_pairwise(t_eosinophils, model_batch = TRUE, filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 17 9
##
## 3 2 1
## 9 9 8t_cf_eosinophil_tables_batchvisit <- combine_de_tables(
t_cf_eosinophil_de_batchvisit, keepers = cf_contrast,
# rda = glue("rda/t_eosinophil_cf_table_batchvisit-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_cf_tables_batchvisit-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_cf_tables_batchvisit-v202304.xlsx before writing the tables.
## Adding venn plots for outcome.t_cf_eosinophil_sig_batchvisit <- extract_significant_genes(
t_cf_eosinophil_tables_batchvisit,
excel = glue("excel/t_eosinophil_cf_sig_batchvisit-v{ver}.xlsx"))## Deleting the file excel/t_eosinophil_cf_sig_batchvisit-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_sig_batchvisit$deseq$ups[[1]])## [1] 99 50dim(t_cf_eosinophil_sig_batchvisit$deseq$downs[[1]])## [1] 35 50visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]],
pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact = visitcf_factor)##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 5 3 6 3 6 3t_cf_eosinophil_visits_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
filter = TRUE)##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 5 3 6 3 6 3## Removing 0 low-count genes (10530 remaining).## Setting 374 low elements to zero.## transform_counts: Found 374 values equal to 0, adding 1 to the matrix.
t_cf_eosinophil_visits_tables_sva <- combine_de_tables(
t_cf_eosinophil_visits_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_cf_eosinophil_visits_sig_sva <- extract_significant_genes(
t_cf_eosinophil_visits_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_visits_sig_sva$deseq$ups[[1]])## [1] 9 50dim(t_cf_eosinophil_visits_sig_sva$deseq$downs[[1]])## [1] 11 509.0.6.1 C/F celltype volcano plots with specific labels
num_color <- color_choices[["clinic_cf"]][["Tumaco_failure"]]
den_color <- color_choices[["clinic_cf"]][["Tumaco_cure"]]
wanted_genes <- c("FI44L", "IFI27", "PRR5", "PRR5-ARHGAP8", "RHCE",
"FBXO39", "RSAD2", "SMTNL1", "USP18", "AFAP1")
cf_monocyte_table <- t_cf_monocyte_tables_sva[["data"]][["outcome"]]
cf_monocyte_volcano <- plot_volcano_condition_de(
cf_monocyte_table, "outcome", label = wanted_genes,
fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_monocyte_volcano_labeled-v{ver}.svg"))
cf_monocyte_volcano$plot
dev.off()## png
## 2cf_monocyte_volcano$plot
cf_eosinophil_table <- t_cf_eosinophil_tables_sva[["data"]][["outcome"]]
cf_eosinophil_volcano <- plot_volcano_condition_de(
cf_eosinophil_table, "outcome", label = wanted_genes,
fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_eosinophil_volcano_labeled-v{ver}.svg"))
cf_eosinophil_volcano$plot
dev.off()## png
## 2cf_eosinophil_volcano$plot
cf_neutrophil_table <- t_cf_neutrophil_tables_sva[["data"]][["outcome"]]
cf_neutrophil_volcano <- plot_volcano_condition_de(
cf_neutrophil_table, "outcome", label = wanted_genes,
fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_neutrophil_volcano_labeled-v{ver}.svg"))
cf_neutrophil_volcano$plot
dev.off()## png
## 2cf_neutrophil_volcano$plot
9.0.6.2 gProfiler: Eosinophils
Up: 116 genes; 123 GO, 2 KEGG, 7 Reactome, 5 WP, 69 TF, 1 miRNA, 0 others Down: 74 genes; 5 GO, 1 Reactome, 4 TF, 0 others
t_cf_eosinophil_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_sig_sva)
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["TF_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["TF_enrich"]])
9.0.7 Eosinophil time comparisons
t_cf_eosinophil_v1_de_sva <- all_pairwise(tv1_eosinophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 5 3## Removing 0 low-count genes (9977 remaining).## Setting 57 low elements to zero.## transform_counts: Found 57 values equal to 0, adding 1 to the matrix.t_cf_eosinophil_v1_tables_sva <- combine_de_tables(
t_cf_eosinophil_v1_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_eosinophil_v1_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v1_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_eosinophil_v1_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v1_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v1_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v1_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_v1_sig_sva$deseq$ups[[1]])## [1] 13 50dim(t_cf_eosinophil_v1_sig_sva$deseq$downs[[1]])## [1] 19 50t_cf_eosinophil_v2_de_sva <- all_pairwise(tv2_eosinophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 6 3## Removing 0 low-count genes (10115 remaining).## Setting 90 low elements to zero.## transform_counts: Found 90 values equal to 0, adding 1 to the matrix.t_cf_eosinophil_v2_tables_sva <- combine_de_tables(
t_cf_eosinophil_v2_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_eosinophil_v2_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v2_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_eosinophil_v2_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v2_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v2_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v2_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_v2_sig_sva$deseq$ups[[1]])## [1] 9 50dim(t_cf_eosinophil_v2_sig_sva$deseq$downs[[1]])## [1] 4 50t_cf_eosinophil_v3_de_sva <- all_pairwise(tv3_eosinophils, model_batch = "svaseq", filter = TRUE)##
## Tumaco_cure Tumaco_failure
## 6 3## Removing 0 low-count genes (10078 remaining).## Setting 48 low elements to zero.## transform_counts: Found 48 values equal to 0, adding 1 to the matrix.t_cf_eosinophil_v3_tables_sva <- combine_de_tables(
t_cf_eosinophil_v3_de_sva, keepers = cf_contrast,
# rda = glue("rda/t_eosinophil_v3_cf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v3_cf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for outcome.t_cf_eosinophil_v3_sig_sva <- extract_significant_genes(
t_cf_eosinophil_v3_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_v3_cf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_v3_cf_sig_sva-v202304.xlsx before writing the tables.dim(t_cf_eosinophil_v3_sig_sva$deseq$ups[[1]])## [1] 68 50dim(t_cf_eosinophil_v3_sig_sva$deseq$downs[[1]])## [1] 29 509.0.7.1 gProfiler: Eosinophils V1
Up: 13 genes, no hits. Down: 19 genes; 11 GO, 1 Reactome, 1 TF
t_cf_eosinophil_v1_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v1_sig_sva)
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["TF_enrich"]])
9.0.7.2 gProfiler: Eosinophils V2
Up: 9 genes; 23 GO, 2 KEGG, 2 Reactome, 4 WP Down: 4 genes; no hits
t_cf_eosinophil_v2_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v2_sig_sva)
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
9.0.7.3 gProfiler: Eosinophils V3
Up: 68 genes; 95 GO, 2 KEGG, 12 Reactome, 3 WP, 63 TF, 1 miRNA Down: 29 genes; 3 GO, 1 WP, 1 TF, 3 miRNA
t_cf_eosinophil_v3_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v3_sig_sva)
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
9.0.7.4 Eosinophils: Compare sva to batch-in-visit
sva_aucc <- calculate_aucc(t_cf_eosinophil_tables_sva[["data"]][[1]],
tbl2 = t_cf_eosinophil_tables_batchvisit[["data"]][[1]],
py = "deseq_adjp", ly = "deseq_logfc")
sva_aucc## $aucc
## [1] 0.5764
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 152, df = 10528, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8232 0.8352
## sample estimates:
## cor
## 0.8293
##
##
## $plot
shared_ids <- rownames(t_cf_eosinophil_tables_sva[["data"]][[1]]) %in%
rownames(t_cf_eosinophil_tables_batchvisit[["data"]][[1]])
first <- t_cf_eosinophil_tables_sva[["data"]][[1]][shared_ids, ]
second <- t_cf_eosinophil_tables_batchvisit[["data"]][[1]][rownames(first), ]
cor.test(first[["deseq_logfc"]], second[["deseq_logfc"]])##
## Pearson's product-moment correlation
##
## data: first[["deseq_logfc"]] and second[["deseq_logfc"]]
## t = 152, df = 10528, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8232 0.8352
## sample estimates:
## cor
## 0.82939.0.7.5 Compare monocyte CF, neutrophil CF, eosinophil CF
t_mono_neut_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
tbl2 = t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
py = "deseq_adjp", ly = "deseq_logfc")
t_mono_neut_sva_aucc## $aucc
## [1] 0.2058
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 43, df = 8575, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4033 0.4381
## sample estimates:
## cor
## 0.4209
##
##
## $plot
t_mono_eo_sva_aucc <- calculate_aucc(t_cf_monocyte_tables_sva[["data"]][["outcome"]],
tbl2 = t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
py = "deseq_adjp", ly = "deseq_logfc")
t_mono_eo_sva_aucc## $aucc
## [1] 0.09657
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 22, df = 9763, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2028 0.2405
## sample estimates:
## cor
## 0.2217
##
##
## $plot
t_neut_eo_sva_aucc <- calculate_aucc(t_cf_neutrophil_tables_sva[["data"]][["outcome"]],
tbl2 = t_cf_eosinophil_tables_sva[["data"]][["outcome"]],
py = "deseq_adjp", ly = "deseq_logfc")
t_neut_eo_sva_aucc## $aucc
## [1] 0.1583
##
## $cor
##
## Pearson's product-moment correlation
##
## data: tbl[[lx]] and tbl[[ly]]
## t = 36, df = 8569, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3467 0.3834
## sample estimates:
## cor
## 0.3652
##
##
## $plot
9.0.8 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.
9.0.8.1 Cure/Fail by visits, all cell types
t_visit_cf_all_de_sva <- all_pairwise(t_visitcf, model_batch = "svaseq", filter = TRUE)##
## v1cure v1failure v2cure v2failure v3cure v3failure
## 30 24 20 15 17 17## 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.
t_visit_cf_all_tables_sva <- combine_de_tables(
t_visit_cf_all_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_all_visitcf_table_sva-v{ver}.rda"),
excel = glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_visit_cf_all_sig_sva <- extract_significant_genes(
t_visit_cf_all_tables_sva,
excel = glue("analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/t_all_visitcf_sig_sva-v202304.xlsx before writing the tables.t_visit_cf_all_gp <- all_gprofiler(t_visit_cf_all_sig_sva)9.0.8.2 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)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 6 7t_visit_cf_monocyte_de_sva <- all_pairwise(t_monocytes_visitcf, model_batch = "svaseq",
filter = TRUE)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 6 7## 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.
t_visit_cf_monocyte_tables_sva <- combine_de_tables(
t_visit_cf_monocyte_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_monocyte_visitcf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_visit_cf_monocyte_sig_sva <- extract_significant_genes(
t_visit_cf_monocyte_tables_sva,
excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Monocytes/t_monocyte_visitcf_sig_sva-v202304.xlsx before writing the tables.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## NULLclosed <- dev.off()
t_v1fc_deseq_ma## NULLt_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## NULLclosed <- dev.off()
t_v2fc_deseq_ma## NULLt_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## NULLclosed <- dev.off()
t_v3fc_deseq_ma## NULLOne 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"]]
9.0.8.3 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)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 5 7t_visit_cf_neutrophil_de_sva <- all_pairwise(t_neutrophil_visitcf, model_batch = "svaseq",
filter = TRUE)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 8 8 7 6 5 7## 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.
t_visit_cf_neutrophil_tables_sva <- combine_de_tables(
t_visit_cf_neutrophil_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_neutrophil_visitcf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_visit_cf_neutrophil_sig_sva <- extract_significant_genes(
t_visit_cf_neutrophil_tables_sva,
excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Neutrophils/t_neutrophil_visitcf_sig_sva-v202304.xlsx before writing the tables.9.0.8.4 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)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 5 3 6 3 6 3t_visit_cf_eosinophil_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
filter = TRUE)##
## v1_cure v1_failure v2_cure v2_failure v3_cure v3_failure
## 5 3 6 3 6 3## 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.
t_visit_cf_eosinophil_tables_sva <- combine_de_tables(
t_visit_cf_eosinophil_de_sva, keepers = visitcf_contrasts,
# rda = glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_tables_sva-v202304.xlsx before writing the tables.## Adding venn plots for v1cf.## Adding venn plots for v2cf.## Adding venn plots for v3cf.t_visit_cf_eosinophil_sig_sva <- extract_significant_genes(
t_visit_cf_eosinophil_tables_sva,
excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Cure_vs_Fail/Eosinophils/t_eosinophil_visitcf_sig_sva-v202304.xlsx before writing the tables.9.1 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.
9.1.1 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.##
## N Y
## 6 24## 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.9.1.2 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
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
9.1.3 persistence DE
persistence_de_sva <- all_pairwise(persistence_expt, filter = TRUE, model_batch = "svaseq")##
## N Y
## 6 24## 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.persistence_table_sva <- combine_de_tables(
persistence_de_sva,
excel = glue("analyses/4_tumaco/DE_Persistence/persistence_all_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_all_de_sva-v202304.xlsx before writing the tables.## Adding venn plots for Y_vs_N.persistence_monocyte_de_sva <- all_pairwise(persistence_monocyte, filter = TRUE, model_batch = "svaseq")##
## N Y
## 2 10## 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.persistence_monocyte_table_sva <- combine_de_tables(
persistence_monocyte_de_sva,
excel = glue("analyses/4_tumaco/DE_Persistence/persistence_monocyte_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_monocyte_de_sva-v202304.xlsx before writing the tables.## Adding venn plots for Y_vs_N.persistence_neutrophil_de_sva <- all_pairwise(persistence_neutrophil, filter = TRUE, model_batch = "svaseq")##
## N Y
## 3 7## 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.persistence_neutrophil_table_sva <- combine_de_tables(
persistence_neutrophil_de_sva,
excel = glue("analyses/4_tumaco/DE_Persistence/persistence_neutrophil_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_neutrophil_de_sva-v202304.xlsx before writing the tables.## Adding venn plots for Y_vs_N.persistence_eosinophil_de_sva <- all_pairwise(persistence_eosinophil, filter = TRUE, model_batch = "svaseq")##
## N Y
## 1 7## 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.## Error in checkForRemoteErrors(val): one node produced an error: c("Error in NOISeq::noiseqbio(norm, k = 0.5, norm = \"rpkm\", factor = \"condition\", : \n ERROR: To run NOISeqBIO at least two replicates per condition are needed.\n Please, run NOISeq if there are not enough replicates in your experiment.\n\n", "noiseq")persistence_eosinophil_table_sva <- combine_de_tables(
persistence_eosinophil_de_sva,
excel = glue("analyses/4_tumaco/DE_Persistence/persistence_eosinophil_de_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Persistence/persistence_eosinophil_de_sva-v202304.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'persistence_eosinophil_de_sva' not found9.2 Comparing visits without regard to cure/fail
9.2.1 All cell types
t_visit_all_de_sva <- all_pairwise(t_visit, filter = TRUE, model_batch = "svaseq")##
## 3 2 1
## 34 35 40## 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.
t_visit_all_table_sva <- combine_de_tables(
t_visit_all_de_sva, keepers = visit_contrasts,
# rda = glue("rda/t_all_visit_table_sva-v{ver}.rda"),
excel = glue("analyses/4_tumaco/DE_Visits/t_all_visit_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/t_all_visit_tables_sva-v202304.xlsx before writing the tables.## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.## Adding venn plots for v2v1.## Adding venn plots for v3v1.## Adding venn plots for v3v2.t_visit_all_sig_sva <- extract_significant_genes(
t_visit_all_table_sva,
excel = glue("analyses/4_tumaco/DE_Visits/t_all_visit_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/t_all_visit_sig_sva-v202304.xlsx before writing the tables.9.2.2 Monocyte samples
t_visit_monocytes <- set_expt_conditions(t_monocytes, fact = "visitnumber")##
## 3 2 1
## 13 13 16t_visit_monocyte_de_sva <- all_pairwise(t_visit_monocytes, filter = TRUE, model_batch = "svaseq")##
## 3 2 1
## 13 13 16## 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.
t_visit_monocyte_table_sva <- combine_de_tables(
t_visit_monocyte_de_sva, keepers = visit_contrasts,
# rda = glue("rda/t_monocyte_visit_table_sva-v{ver}.rda"),
excel = glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_tables_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_tables_sva-v202304.xlsx before writing the tables.## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.## Adding venn plots for v2v1.## Adding venn plots for v3v1.## Adding venn plots for v3v2.t_visit_monocyte_sig_sva <- extract_significant_genes(
t_visit_monocyte_table_sva,
excel = glue("analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Monocytes/t_monocyte_visit_sig_sva-v202304.xlsx before writing the tables.9.2.3 Neutrophil samples
t_visit_neutrophils <- set_expt_conditions(t_neutrophils, fact = "visitnumber")##
## 3 2 1
## 12 13 16t_visit_neutrophil_de_sva <- all_pairwise(t_visit_neutrophils, filter = TRUE, model_batch = "svaseq")##
## 3 2 1
## 12 13 16## 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.
t_visit_neutrophil_table_sva <- combine_de_tables(
t_visit_neutrophil_de_sva, keepers = visit_contrasts,
# rda = glue("rda/t_neutrophil_visit_table_sva-v{ver}.rda"),
excel = glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_table_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_table_sva-v202304.xlsx before writing the tables.## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.## Adding venn plots for v2v1.## Adding venn plots for v3v1.## Adding venn plots for v3v2.t_visit_neutrophil_sig_sva <- extract_significant_genes(
t_visit_neutrophil_table_sva,
excel = glue("analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Neutrophils/t_neutrophil_visit_sig_sva-v202304.xlsx before writing the tables.9.2.4 Eosinophil samples
t_visit_eosinophils <- set_expt_conditions(t_eosinophils, fact="visitnumber")##
## 3 2 1
## 9 9 8t_visit_eosinophil_de <- all_pairwise(t_visit_eosinophils, filter = TRUE, model_batch = "svaseq")##
## 3 2 1
## 9 9 8## 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.
t_visit_eosinophil_table <- combine_de_tables(
t_visit_eosinophil_de, keepers = visit_contrasts,
# rda = glue("rda/t_eosinophil_visit_table_sva-v{ver}.rda"),
excel = glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_table_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_table_sva-v202304.xlsx before writing the tables.## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.
## Warning in combine_extracted_plots(entry_name, combined, wanted_denominator, :
## I think this is an extra contrast table, the plots may be weird.## Adding venn plots for v2v1.## Adding venn plots for v3v1.## Adding venn plots for v3v2.t_visit_eosinophil_sig <- extract_significant_genes(
t_visit_eosinophil_table,
excel = glue("analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_sig_sva-v{ver}.xlsx"))## Deleting the file analyses/4_tumaco/DE_Visits/Eosinophils/t_eosinophil_visit_sig_sva-v202304.xlsx before writing the tables.10 Explore ROC
Alejandro showed some ROC curves for eosinophil data showing sensitivity vs. specificity of a couple genes which were observed in v1 eosinophils vs. all-times eosinophils across cure/fail. I am curious to better understand how this was done and what utility it might have in other contexts.
To that end, I want to try something similar myself. In order to properly perform the analysis with these various tools, I need to reconfigure the data in a pretty specific format:
- Single df with 1 row per set of observations (sample in this case I think)
- The outcome column(s) need to be 1 (or more?) metadata factor(s) (cure/fail or a paste0 of relevant queries (eo_v1_cure, eo_v123_cure, etc)
- The predictor column(s) are the measurements (rpkm of 1 or more genes), 1 column each gene.
If I intend to use this for our tx data, I will likely need a utility function to create the properly formatted input df.
For the purposes of my playing, I will choose three genes from the eosinophil C/F table, one which is significant, one which is not, and an arbitrary.
The input genes will therefore be chosen from the data structure: t_cf_eosinophil_tables_sva:
ENSG00000198178, ENSG00000179344, ENSG00000182628
eo_rpkm <- normalize_expt(tv1_eosinophils, convert = "rpkm", column = "cds_length")## There appear to be 5391 genes without a length.test <- all_pairwise(tmrc_external, model_batch = "svaseq", filter = "simple")## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'tmrc_external' not foundtest_table <- combine_de_tables(test, excel = "excel/tmrc3_scott_biopsies.xlsx")## Deleting the file excel/tmrc3_scott_biopsies.xlsx before writing the tables.## Error in get_expt_colors(apr[["input"]]): object 'test' not foundtest_sig <- extract_significant_genes(test_table, excel = "excel/tmrc3_scott_biopsies_sig.xlsx")## Deleting the file excel/tmrc3_scott_biopsies_sig.xlsx before writing the tables.## Error in extract_significant_genes(test_table, excel = "excel/tmrc3_scott_biopsies_sig.xlsx"): object 'test_table' not foundtmrc_external_species <- set_expt_conditions(tmrc_external, fact = "ParasiteSpecies") %>%
set_expt_colors(color_choices[["parasite"]])## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': error in evaluating the argument 'object' in selecting a method for function 'pData': object 'tmrc_external' not found## Skipping this because it is taking too long.
##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: "TMRC3 202304: Differential Expression analyses"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
runtime: shiny
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    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)
library(glue)
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 <- "202304"
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("tmrc3_differential_expression_{ver}.Rmd")
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
loaded <- load(file = glue("rda/tmrc3_data_structures-v{ver}.rda"))
```

# Changelog

* Still hunting for messed up colors, changed input data to match new version.

# Introduction

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

## Naming conventions

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

Hopefully by now they all follow these guidelines:

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

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

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

I suspect there remain some exceptions and/or errors.

## Define contrasts for DE analyses

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

```{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"))
visit_v1later <- list(
  "later_vs_first" = c("later", "first"))
celltypes <- list(
  "eo_mono" = c("eosinophils", "monocytes"),
  "ne_mono" = c("neutrophils", "monocytes"),
  "eo_ne" = c("eosinophils", "neutrophils"))
```

# Compare samples by clinic

## DE: Compare clinics, all samples

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

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

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

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

### Visualize clinic differences

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

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

```{r compare_clinic_visualization}
## Check that up is up
summary(tc_all_clinic_table_sva[["data"]][["clinics"]][["deseq_logfc"]])
## I think we can assume that most genes are down when considering Tumaco/Cali.
sum(tc_all_clinic_table_sva$data$clinics$deseq_logfc < -1.0 &
      tc_all_clinic_table_sva$data$clinics$deseq_adjp < 0.05)

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

### Ontology Search by clinic

```{r gprofiler_clinic}
increased_tumaco_categories <- simple_gprofiler(
  tc_all_clinic_sig_sva[["deseq"]][["ups"]][["clinics"]])
increased_tumaco_categories[["pvalue_plots"]][["BP"]]

increased_cali_categories <- simple_gprofiler(
  tc_all_clinic_sig_sva[["deseq"]][["downs"]][["clinics"]])
increased_cali_categories[["pvalue_plots"]][["BP"]]
```

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

## DE: Compare clinics, eosinophil samples

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

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

tc_eosinophils_clinic_table_nobatch <- combine_de_tables(
  tc_eosinophils_clinic_de_nobatch, keepers = tc_cf_contrasts,
#  rda = glue("rda/tc_eosinophils_clinic_table_nobatch-v{ver}.rda"),
  excel = 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("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_nobatch-v{ver}.xlsx"))

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

tc_eosinophils_clinic_table_sva <- combine_de_tables(
  tc_eosinophils_clinic_de_sva, keepers = tc_cf_contrasts,
#  rda = glue("rda/tc_eosinophils_clinic_table_sva-v{ver}.rda"),
  excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_table_sva-v{ver}.xlsx"))
tc_eosinophils_clinic_sig_sva <- extract_significant_genes(
  tc_eosinophils_clinic_table_sva,
  excel = glue("analyses/3_cali_and_tumaco/clinic_cf/Eosinophils/tc_eosinophils_clinic_sig_sva-v{ver}.xlsx"))
```

## DE: Compare clinics, biopsy samples

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

Note that these data include cure and fail samples for

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

tc_biopsies_clinic_table_sva <- combine_de_tables(
  tc_biopsies_clinic_de_sva, keepers = tc_cf_contrasts,
#  rda = glue("rda/tc_biopsies_clinic_table_sva-v{ver}.rda"),
  excel = 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("analyses/3_cali_and_tumaco/clinic_cf/Biopsies/tc_biopsies_clinic_sig_sva-v{ver}.xlsx"))
```

## DE: Compare clinics, monocyte samples

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

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

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

```{r tc_monocytes_de}
tc_monocytes_de_nobatch <- all_pairwise(tc_monocytes, model_batch = FALSE, filter = TRUE)

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

### DE: Compare clinics, monocytes with svaseq

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

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

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

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

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

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

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

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

cali_merged_sva <- merge(cali_table_sva, tumaco_table_sva, by = "row.names")
cor.test(cali_merged_sva[, "deseq_logfc.x"], cali_merged_sva[, "deseq_logfc.y"])
cali_aucc_sva <- calculate_aucc(cali_table_sva, tumaco_table_sva, px = "deseq_adjp",
                                py = "deseq_adjp", lx = "deseq_logfc", ly = "deseq_logfc")
cali_aucc_sva$plot
```

## DE: Compare clinics, neutrophil samples

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

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

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

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

# GSVA Load mSigDB data

Conversely, I can load some of the MsigDB categories from broad and
perform a similar analysis using goseq to see if there are over
represented categories.

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

clinic_gsea_msig_c2 <- goseq_msigdb(clinic_sigenes, length_db = hs_length,
                                    signatures = broad_c2, signature_category = "c2")
```

### GSEA: Compare clinics, Eosinophil samples

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

```{r gsea_clinic_eo}
tc_eosinophils_gp <- simple_gprofiler(tc_eosinophils_sigenes)
tc_eosinophils_gp$pvalue_plots$REAC
tc_eosinophils_gp$pvalue_plots$BP

tc_eosinophils_up_gp <- simple_gprofiler(tc_eosinophils_sigenes_up)
tc_eosinophils_up_gp$pvalue_plots$REAC
tc_eosinophils_up_gp$pvalue_plots$BP

tc_eosinophils_down_gp <- simple_gprofiler(tc_eosinophils_sigenes_down)
tc_eosinophils_down_gp$pvalue_plots$REAC
tc_eosinophils_down_gp$pvalue_plots$BP
```

### GSEA: Compare clinics, Monocyte samples

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

```{r gsea_clinic_mnocyte}
tc_monocytes_gp <- simple_gprofiler(tc_monocytes_sigenes)
tc_monocytes_gp$pvalue_plots$REAC
tc_monocytes_gp$pvalue_plots$BP

tc_monocytes_up_gp <- simple_gprofiler(tc_monocytes_sigenes_up)
tc_monocytes_up_gp$pvalue_plots$REAC
tc_monocytes_up_gp$pvalue_plots$BP

tc_monocytes_down_gp <- simple_gprofiler(tc_monocytes_sigenes_down)
tc_monocytes_down_gp$pvalue_plots$REAC
tc_monocytes_down_gp$pvalue_plots$BP
```

### GSEA: Compare clinics, Neutrophil samples

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

```{r gsea_clinic_neutrophils}
tc_neutrophils_gp <- simple_gprofiler(tc_neutrophils_sigenes)
## tc_neutrophils_gp$pvalue_plots$REAC ## no hits
tc_neutrophils_gp$pvalue_plots$BP
tc_neutrophils_gp$pvalue_plots$TF

tc_neutrophils_up_gp <- simple_gprofiler(tc_neutrophils_sigenes_up)
## tc_neutrophils_up_gp$pvalue_plots$REAC ## No hits
tc_neutrophils_up_gp$pvalue_plots$BP

tc_neutrophils_down_gp <- simple_gprofiler(tc_neutrophils_sigenes_down)
tc_neutrophils_down_gp$pvalue_plots$REAC
tc_neutrophils_down_gp$pvalue_plots$BP
```

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

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

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

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

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

## GSEA: Extract clinic-specific genes

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

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

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

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

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

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

### GSEA: Compare clinics, all samples

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

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

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

```{r gsea_clinic_gprofiler}
tc_eosionphil_gprofiler <- simple_gprofiler(tc_eosinophils_sigenes_up)

clinic_gp <- simple_gprofiler(clinic_sigenes)
clinic_gp$pvalue_plots$REAC
clinic_gp$pvalue_plots$BP
clinic_gp$pvalue_plots$TF
clinic_gp$interactive_plots$GO
```

# Tumaco and Cali, cure vs. fail

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

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

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

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

tc_all_cf_de_batch <- all_pairwise(tc_valid, filter = TRUE, model_batch = TRUE)
tc_all_cf_table_batch <- combine_de_tables(
  tc_all_cf_de_batch,
  keepers = t_cf_contrast,
#  rda = glue("rda/tc_valid_cf_table_batch-v{ver}.rda"),
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_table_batch-v{ver}.xlsx"))
tc_all_cf_sig_batch <- extract_significant_genes(
  tc_all_cf_table_batch,
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_valid_cf_sig_batch-v{ver}.xlsx"))
```

## Biopsies, with(out) SVA

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

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

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

## Eosinophils, with(out) SVA

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

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

tc_eosinophils_cf_de_batch <- all_pairwise(tc_eosinophils_cf, filter = TRUE, model_batch = TRUE)
tc_eosinophils_cf_table_batch <- combine_de_tables(
  tc_eosinophils_cf_de_batch, keepers = t_cf_contrast,
#  rda = glue("rda/tc_eosinophils_cf_table_batch-v{ver}.rda"),
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_table_batch-v{ver}.xlsx"))
tc_eosinophils_cf_sig_batch <- extract_significant_genes(
  tc_eosinophils_cf_table_batch,
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_eosinophils_cf_sig_batch-v{ver}.xlsx"))
```

## Monocytes, with(out) SVA

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

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

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

## Neutrophils, with(out) SVA

Last try, this time using the Neutrophil samples.

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

tc_neutrophils_cf_de_batch <- all_pairwise(tc_neutrophils_cf, filter = TRUE, model_batch = TRUE)
tc_neutrophils_cf_table_batch <- combine_de_tables(
  tc_neutrophils_cf_de_batch, keepers = t_cf_contrast,
#  rda = glue("rda/tc_neutrophils_cf_table_batch-v{ver}.rda"),
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_table_batch-v{ver}.xlsx"))
tc_neutrophils_cf_sig_batch <- extract_significant_genes(
  tc_neutrophils_cf_table_batch,
  excel = glue("analyses/3_cali_and_tumaco/cf/All_Samples/tc_neutrophils_cf_sig_batch-v{ver}.xlsx"))
```

# Only Tumaco samples

Start over, this time with only the samples from Tumaco.  We currently
are assuming these will prove to be the only analyses used for final
interpretation.  This is primarily because we have insufficient
failed treatment samples from Cali.

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

## All samples

Start by considering all Tumaco cell types.  Note that in this case we
only use SVA, primarily because I am not certain what would be an
appropriate batch factor, perhaps visit?

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

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

## gProfiler search of all samples

The following gProfiler searches use the all_gprofiler() function
instead of simple_gprofiler().  As a result, the results are separated
by {contrast}_{direction}.  Thus 'outcome_down'.

The same plots are available as the previous gProfiler searches, but
in many of the following runs, I used the dotplot() function to get a
slightly different view of the results.

```{r t_cf_clinical_gprofiler}
t_cf_clinical_gp <- all_gprofiler(t_cf_clinical_sig_sva)
## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["WP_enrich"]])

## Transcription factor database of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["TF_enrich"]])

## Reactome of the up c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["REAC_enrich"]])

## GO of the down c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_down"]][["GO_enrich"]])
t_cf_clinical_gp[["outcome_up"]][["pvalue_plots"]][["BP"]]

## Reactome of the down c/f genes
enrichplot::dotplot(t_cf_clinical_gp[["outcome_up"]][["REAC_enrich"]])
```

# Visit comparisons

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

```{r de_visit_comparisons}
v1_vs_later <- all_pairwise(tc_v1vs, model_batch = "svaseq", filter = TRUE)

v1_vs_later_table <- combine_de_tables(
  v1_vs_later, keepers = visit_v1later,
  excel = glue("excel/v1_vs_later_tables-v{ver}.xlsx"))
v1_vs_later_sig <- extract_significant_genes(
  v1_vs_later_table,
  excel = glue("excel/v1_vs_later_sig-v{ver}.xlsx"))

v1later_gp <- all_gprofiler(v1_vs_later_sig)
v1later_gp[[1]]$pvalue_plots$REAC
v1later_gp[[2]]$pvalue_plots$REAC

tv1_vs_later <- all_pairwise(t_v1vs, model_batch = "svaseq", filter = TRUE)
tv1_vs_later_table <- combine_de_tables(
  tv1_vs_later, keepers = visit_v1later,
  excel = glue("excel/tv1_vs_later_tables-v{ver}.xlsx"))
tv1_vs_later_sig <- extract_significant_genes(
  tv1_vs_later_table,
  excel = glue("excel/tv1_vs_later_sig-v{ver}.xlsx"))

v1later_gp <- all_gprofiler(v1_vs_later_sig)
v1later_gp[[1]]$pvalue_plots$REAC
v1later_gp[[2]]$pvalue_plots$REAC

tv1later_gp <- all_gprofiler(tv1_vs_later_sig)
tv1later_gp[[1]]$pvalue_plots$BP
tv1later_gp[[2]]$pvalue_plots$BP
```

# Sex comparison

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

t_sex <- subset_expt(tc_sex, subset = "clinic == 'Tumaco'")
t_sex_de <- all_pairwise(t_sex, model_batch = "svaseq", filter = TRUE)
t_sex_table <- combine_de_tables(
  t_sex_de, excel = glue("excel/t_sex_table-v{ver}.xlsx"))
t_sex_sig <- extract_significant_genes(
  t_sex_table, excel = glue("excel/t_sex_sig-v{ver}.xlsx"))
t_sex_gp <- all_gprofiler(t_sex_sig)
```

### Separate the Tumaco data by visit

One of the most compelling ideas in the data is the opportunity to
find genes in the first visit which may help predict the likelihood
that a person will respond well to treatment.  The following block
will therefore look at cure/fail from Tumaco at visit 1.

#### Cure/Fail, Tumaco Visit 1

```{r tumaco_timepoints_v1}
t_cf_clinical_v1_de_sva <- all_pairwise(tv1_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v1_table_sva <- combine_de_tables(
  t_cf_clinical_v1_de_sva, keepers = t_cf_contrast,
#  rda = glue("rda/t_clinical_v1_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v1_sig_sva <- extract_significant_genes(
  t_cf_clinical_v1_table_sva,
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v1_sig_sva$deseq$downs[[1]])
```

#### Cure/Fail, Tumaco Visit 2

The visit 2 and visit 3 samples are interesting because they provide
an opportunity to see if we can observe changes in response in the
middle and end of treatment...

```{r tumaco_time_v2}
t_cf_clinical_v2_de_sva <- all_pairwise(tv2_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v2_table_sva <- combine_de_tables(
  t_cf_clinical_v2_de_sva, keepers = t_cf_contrast,
#  rda = glue("rda/t_clinical_v2_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v2_sig_sva <- extract_significant_genes(
  t_cf_clinical_v2_table_sva,
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v2_sig_sva$deseq$downs[[1]])
```

#### Cure/Fail, Tumaco Visit 3

```{r tumaco_time_v3}
t_cf_clinical_v3_de_sva <- all_pairwise(tv3_samples, model_batch = "svaseq", filter = TRUE)
t_cf_clinical_v3_table_sva <- combine_de_tables(
  t_cf_clinical_v3_de_sva, keepers = t_cf_contrast,
#  rda = glue("rda/t_clinical_v3_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_clinical_v3_sig_sva <- extract_significant_genes(
  t_cf_clinical_v3_table_sva,
  excel = glue("{xlsx_prefix}/All_Samples/t_clinical_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_clinical_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_clinical_v3_sig_sva$deseq$downs[[1]])
```

#### Visit 1 gProfiler searches

It looks like there are very few groups in the visit 1 significant genes.

```{r t_cf_clinical_v1_sig_sva_gp}
t_cf_clinical_v1_sig_sva_gp <- all_gprofiler(t_cf_clinical_v1_sig_sva)

## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v1_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

#### Visit 2 gProfiler searches

Up: 74 GO, 4 KEGG, 6 reactome, 4 WP, 56 TF, 1 miRNA, 0 HP/HPA/CORUM.
Down:  19 GO, 1 KEGG, 1 HP, 2 HPA, 0 reactome/wp/tf/corum

```{r t_cf_clinical_v2_sig_sva_gp}
t_cf_clinical_v2_sig_sva_gp <- all_gprofiler(t_cf_clinical_v2_sig_sva)

## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_clinical_v2_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

#### Visit 3 gProfiler searches

Up: 120 genes; 141 GO, 1 KEGG, 5 Reactome, 2 WP, 30 TF, 1 miRNA, 0 HPA/CORUM/HP
Down: 62 genes; 30 GO, 2 KEGG, 1 Reactome, 0 WP/TF/miRNA/HPA/CORUM/HP,

```{r t_cf_clinical_v3_sig_sva_gpv2}
t_cf_clinical_v3_sig_sva_gp <- all_gprofiler(t_cf_clinical_v3_sig_sva)

## Wikipathways of the up c/f genes
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_clinical_v3_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

### Repeat no biopsies

The biopsy samples are problematic for a few reasons, so let us repeat
without them.

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

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

#### gProfiler: Clinical no biopsies

Up: 137 genes; 88 GO, 0 KEGG, 6 Reactome, 1 WP, 46 TF, 1 miRNA, 0 others
Down: 73 genes; 78 GO, 1 KEGG, 1 Reactome, 9 TF, 0 others

```{r t_cf_nobiopclinical_v3_sig_sva_gp}
t_cf_clinical_nobiop_sig_sva_gp <- all_gprofiler(t_cf_clinical_nobiop_sig_sva)

enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_clinical_nobiop_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

### By cell type

Now let us switch our view to each individual cell type collected.
The hope here is that we will be able to learn some cell-specific
differences in the response for people who did(not) respond well.

#### Cure/Fail, Biopsies

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

#### gProfiler: Biopsies

Up: 17 genes; 74 GO, 3 KEGG, 1 Reactome, 3 WP, 1 TF, 0 others
Down: 11 genes; 2 GO, 0 others

```{r t_cf_clinical_v3_sig_sva_gp}
t_cf_biopsy_sig_sva_gp <- all_gprofiler(t_cf_biopsy_sig_sva)

enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_up"]][["WP_enrich"]])

enrichplot::dotplot(t_cf_biopsy_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

#### Cure/Fail, Monocytes

Same question, but this time looking at monocytes.  In addition, this
comparison was done twice, once using SVA and once using visit as a
batch factor.

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

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

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

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

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

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

#### gProfiler: Monocytes

Now that I am looking back over these results, I am not compeltely
certain why I only did the gprofiler search for the sva data...

Up: 60 genes; 12 GO, 1 KEGG, 1 WP, 4 TF, 0 others
Down: 53 genes; 26 GO, 1 KEGG, 1 Reactome, 2 TF, 0 others

```{r t_cf_monocyte_sig_sva_gp}
t_cf_monocyte_sig_sva_gp <- all_gprofiler(t_cf_monocyte_sig_sva)

enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_monocyte_sig_sva_gp[["outcome_down"]][["GO_enrich"]])

t_cf_monocyte_sig_batch_gp <- all_gprofiler(t_cf_monocyte_sig_batchvisit)
enrichplot::dotplot(t_cf_monocyte_sig_batch_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_monocyte_sig_batch_gp[["outcome_up"]][["HP_enrich"]])
```

### Individual visits, Monocytes

Now focus in on the monocyte samples on a per-visit basis.

#### Visit 1

```{r cf_monocyte_de_visits_v1}
t_cf_monocyte_v1_de_sva <- all_pairwise(tv1_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v1_tables_sva <- combine_de_tables(
  t_cf_monocyte_v1_de_sva, keepers = cf_contrast,
#  rda = glue("rda/t_monocyte_v1_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v1_sig_sva <- extract_significant_genes(
  t_cf_monocyte_v1_tables_sva,
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v1_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v1_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v1_sig_sva$deseq$downs[[1]])
```

#### Visit 2

```{r cf_monocyte_de_visits_v2}
t_cf_monocyte_v2_de_sva <- all_pairwise(tv2_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v2_tables_sva <- combine_de_tables(
  t_cf_monocyte_v2_de_sva, keepers = cf_contrast,
#  rda = glue("rda/t_monocyte_v2_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v2_sig_sva <- extract_significant_genes(
  t_cf_monocyte_v2_tables_sva,
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v2_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v2_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v2_sig_sva$deseq$downs[[1]])
```

#### Visit 3

```{r cf_monocyte_de_visits_v3}
t_cf_monocyte_v3_de_sva <- all_pairwise(tv3_monocytes, model_batch = "svaseq", filter = TRUE)
t_cf_monocyte_v3_tables_sva <- combine_de_tables(
  t_cf_monocyte_v3_de_sva, keepers = cf_contrast,
#  rda = glue("rda/t_monocyte_v3_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_tables_sva-v{ver}.xlsx"))
t_cf_monocyte_v3_sig_sva <- extract_significant_genes(
  t_cf_monocyte_v3_tables_sva,
  excel = glue("{xlsx_prefix}/Monocytes/t_monocyte_v3_cf_sig_sva-v{ver}.xlsx"))
dim(t_cf_monocyte_v3_sig_sva$deseq$ups[[1]])
dim(t_cf_monocyte_v3_sig_sva$deseq$downs[[1]])
```

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

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

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

##### gProfiler: Monocytes by visit, V1

V1: Up: 14 genes; No categories
V1: Down: 52 genes; 20 GO, 5 TF

```{r t_cf_monocyte_sig_sva_gp_visits}
t_cf_monocyte_v1_sig_sva_gp <- all_gprofiler(t_cf_monocyte_v1_sig_sva)

enrichplot::dotplot(t_cf_monocyte_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

##### gProfiler: Monocytes by visit, V2

V2: Up: 1 gene
V2: Down: 0 genes.

##### gProfiler: Monocytes by visit, V3

V3: Up: 4 genes.
V3: Down: 0 genes.

### Neutrophil samples

Switch context to the Neutrophils, once again repeat the analysis
using SVA and visit as a batch factor.

```{r neutrophil_only}
t_cf_neutrophil_de_sva <- all_pairwise(t_neutrophils, model_batch = "svaseq", filter = TRUE)
t_cf_neutrophil_tables_sva <- combine_de_tables(
  t_cf_neutrophil_de_sva, keepers = cf_contrast,
#  rda = glue("rda/t_neutrophil_cf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_tables_sva-v{ver}.xlsx"))
t_cf_neutrophil_sig_sva <- extract_significant_genes(
  t_cf_neutrophil_tables_sva,
  excel = glue("{xlsx_prefix}/Neutrophils/t_neutrophil_cf_sig_sva-v{ver}.xlsx"))

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

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

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

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

#### gProfiler: Neutrophils

Up: 84 genes; 5 GO, 2 Reactome, 3 TF, no others.
Down: 29 genes: 12 GO, 1 Reactome, 1 TF, 1 miRNA, 11 HP, 0 others

```{r t_cf_neutrophil_sig_sva_gp}
t_cf_neutrophil_sig_sva_gp <- all_gprofiler(t_cf_neutrophil_sig_sva)

enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_neutrophil_sig_sva_gp[["outcome_down"]][["HP_enrich"]])
```

#### Neutrophils by visit

When I did this with the monocytes, I split it up into multiple blocks
for each visit.  This time I am just going to run them all together.

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

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

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

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

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

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

##### gProfiler: Neutrophils by visit, V1

V1: Up: 5 genes
V1: Down: 8 genes; 14 GO.

```{r t_cf_neutrophil_sig_sva_gp_visits1}
t_cf_neutrophil_v1_sig_sva_gp <- all_gprofiler(t_cf_neutrophil_v1_sig_sva)

enrichplot::dotplot(t_cf_neutrophil_v1_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
```

##### gProfiler: Neutrophils by visit, V2

Up: 5 genes; 3 GO, 10 TF.
Down: 1 gene.

#### 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

This time, with feeling!  Repeating the same set of tasks with the
eosinophil samples.

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

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

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

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

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

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

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

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

#### C/F celltype volcano plots with specific labels

```{r volcano_condition_de_labels}
num_color <- color_choices[["clinic_cf"]][["Tumaco_failure"]]
den_color <- color_choices[["clinic_cf"]][["Tumaco_cure"]]
wanted_genes <- c("FI44L", "IFI27", "PRR5", "PRR5-ARHGAP8", "RHCE",
                  "FBXO39", "RSAD2", "SMTNL1", "USP18", "AFAP1")

cf_monocyte_table <- t_cf_monocyte_tables_sva[["data"]][["outcome"]]
cf_monocyte_volcano <- plot_volcano_condition_de(
  cf_monocyte_table, "outcome", label = wanted_genes,
  fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
  color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_monocyte_volcano_labeled-v{ver}.svg"))
cf_monocyte_volcano$plot
dev.off()
cf_monocyte_volcano$plot

cf_eosinophil_table <- t_cf_eosinophil_tables_sva[["data"]][["outcome"]]
cf_eosinophil_volcano <- plot_volcano_condition_de(
  cf_eosinophil_table, "outcome", label = wanted_genes,
  fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
  color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_eosinophil_volcano_labeled-v{ver}.svg"))
cf_eosinophil_volcano$plot
dev.off()
cf_eosinophil_volcano$plot

cf_neutrophil_table <- t_cf_neutrophil_tables_sva[["data"]][["outcome"]]
cf_neutrophil_volcano <- plot_volcano_condition_de(
  cf_neutrophil_table, "outcome", label = wanted_genes,
  fc_col = "deseq_logfc", p_col = "deseq_adjp", line_position = NULL,
  color_high = num_color, color_low = den_color, label_size = 6)
pp(file = glue("images/cf_neutrophil_volcano_labeled-v{ver}.svg"))
cf_neutrophil_volcano$plot
dev.off()
cf_neutrophil_volcano$plot
```

#### gProfiler: Eosinophils

Up: 116 genes; 123 GO, 2 KEGG, 7 Reactome, 5 WP, 69 TF, 1 miRNA, 0 others
Down:  74 genes; 5 GO, 1 Reactome, 4 TF, 0 others

```{r t_cf_eosinophil_sig_sva_gp}
t_cf_eosinophil_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_sig_sva)

enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["REAC_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["TF_enrich"]])

enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["TF_enrich"]])
```

### Eosinophil time comparisons

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

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

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

#### gProfiler: Eosinophils V1

Up: 13 genes, no hits.
Down: 19 genes; 11 GO, 1 Reactome, 1 TF

```{r t_cf_eosinophil_sig_sva_gpv2}
t_cf_eosinophil_v1_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v1_sig_sva)

enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_down"]][["TF_enrich"]])
```

#### gProfiler: Eosinophils V2

Up: 9 genes; 23 GO, 2 KEGG, 2 Reactome, 4 WP
Down: 4 genes; no hits

```{r t_cf_eosinophil_sig_sva_gpv3}
t_cf_eosinophil_v2_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v2_sig_sva)

enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
```

#### gProfiler: Eosinophils V3

Up: 68 genes; 95 GO, 2 KEGG, 12 Reactome, 3 WP, 63 TF, 1 miRNA
Down: 29 genes; 3 GO, 1 WP, 1 TF, 3 miRNA

```{r t_cf_eosinophil_sig_sva_gpv4}
t_cf_eosinophil_v3_sig_sva_gp <- all_gprofiler(t_cf_eosinophil_v3_sig_sva)

enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["GO_enrich"]])
enrichplot::dotplot(t_cf_eosinophil_sig_sva_gp[["outcome_up"]][["WP_enrich"]])
```

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

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

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

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

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

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

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

### By visit

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

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

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

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

```{r visit_gprofiler}
t_visit_cf_all_gp <- all_gprofiler(t_visit_cf_all_sig_sva)
```

#### Cure/Fail by visit, Monocytes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

#### Cure/Fail by visit, Neutrophils

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

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

#### Cure/Fail by visit, Eosinophils

```{r visit_cf_eosinophil_de}
visitcf_factor <- paste0("v", pData(t_eosinophils)[["visitnumber"]], "_",
                         pData(t_eosinophils)[["finaloutcome"]])
t_eosinophil_visitcf <- set_expt_conditions(t_eosinophils, fact = visitcf_factor)
t_visit_cf_eosinophil_de_sva <- all_pairwise(t_eosinophil_visitcf, model_batch = "svaseq",
                                             filter = TRUE)
t_visit_cf_eosinophil_tables_sva <- combine_de_tables(
  t_visit_cf_eosinophil_de_sva, keepers = visitcf_contrasts,
#  rda = glue("rda/t_eosinophil_visitcf_table_sva-v{ver}.rda"),
  excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_tables_sva-v{ver}.xlsx"))
t_visit_cf_eosinophil_sig_sva <- extract_significant_genes(
  t_visit_cf_eosinophil_tables_sva,
  excel = glue("{xlsx_prefix}/Eosinophils/t_eosinophil_visitcf_sig_sva-v{ver}.xlsx"))
```

## Persistence in visit 3

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

### Setting up

First things first, create the datasets.

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

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

### Take a look

See if there are any patterns which look usable.

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

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

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

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

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

### persistence DE

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

## Comparing visits without regard to cure/fail

### All cell types

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

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

### Monocyte samples

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

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

### Neutrophil samples

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

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

### Eosinophil samples

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

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

# Explore ROC

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

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

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

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

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

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

ENSG00000198178, ENSG00000179344, ENSG00000182628

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

```{r scott_external}
test <- all_pairwise(tmrc_external, model_batch = "svaseq", filter = "simple")
test_table <- combine_de_tables(test, excel = "excel/tmrc3_scott_biopsies.xlsx")
test_sig <- extract_significant_genes(test_table, excel = "excel/tmrc3_scott_biopsies_sig.xlsx")

tmrc_external_species <- set_expt_conditions(tmrc_external, fact = "ParasiteSpecies") %>%
  set_expt_colors(color_choices[["parasite"]])

```

```{r saveme}
## Skipping this because it is taking too long.
##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)
```
