deseq workflow cosmetics

dge_workflow/featcounts_deseq_mf.R

@@ -39,7 +39,7 @@ count_matrix_file <- opts$count_matrix
 design_matrix_file <- opts$design_matrix
 contrasts_file <- opts$contrasts
 
-resultsBase <- if(str_length(opts$out)>0) paste0(opts$outprefix, ".") else ""
+resultsBase <- if(str_length(opts$out)>0) paste0(opts$out, ".") else ""
 
 pcutoff <- if(is.null(opts$pcutoff)) NULL else as.numeric(opts$pcutoff)
 qcutoff <- if(is.numeric(pcutoff)) NULL else as.numeric(opts$qcutoff)
@@ -49,7 +49,7 @@ if(is.numeric(pcutoff)) opts$qcutoff <- NULL
 designFormula <- opts$design
 ## consider last element of design formula as sample attribute
 contrastAttribute <- str_split(designFormula, "[+]") %>% unlist() %>% tail(1)
-assert(designFormula!="sample" && !is.null(design_matrix_file), "custom designs are not supported without a design matrix")
+assert(designFormula=="sample" || !is.null(design_matrix_file), "custom designs are not supported without a design matrix")
 
 
 #' # Differential Expression Analysis
@@ -434,7 +434,7 @@ geneInfo <- quote({
 }) %>% cache_it("geneInfo")
 
 ## save as reference for downstream analysis
-write_tsv(geneInfo, path="geneInfo.txt")
+write_tsv(geneInfo, path=paste0(resultsBase, "geneInfo.txt"))
 
 
 #' Export counts per replicate as double-normalized FPKMs=(read_count * 10^9)/(gene_length * library_size)
@@ -450,10 +450,11 @@ fpkms <- countMatrix %>%
 
 #fpkms %>% ggplot(aes(fpkm)) + geom_histogram() + scale_x_log10(labels=comma)
 
-write_tsv(fpkms, "fpkms_by_replicate.long.txt")
+write_tsv(fpkms, paste0(resultsBase, "fpkms_by_replicate.long.txt"))
 fpkms %>%
     transmute(ensembl_gene_id, replicate, fpkm) %>%
-    spread(replicate, fpkm) %>% write_tsv("fpkms_by_replicate.wide.txt")
+    spread(replicate, fpkm) %>%
+    write_tsv(paste0(resultsBase, "fpkms_by_replicate.wide.txt"))
 
 #' Also export averaged counts per sample as double-normalized FPKMs=(read_count * 10^9)/(gene_length * library_size)
 
@@ -477,10 +478,10 @@ sampleFpkms <- countMatrix %>%
 
 #fpkms %>% ggplot(aes(fpkm)) + geom_histogram() + scale_x_log10(labels=comma)
 
-write_tsv(sampleFpkms, "fpkms_by_sample.long.txt")
+write_tsv(sampleFpkms, paste0(resultsBase, "fpkms_by_sample.long.txt"))
 sampleFpkms %>%
     transmute(ensembl_gene_id, sample, fpkm) %>%
-    spread(sample, fpkm) %>% write_tsv("fpkms_by_sample.wide.txt")
+    spread(sample, fpkm) %>% write_tsv(paste0(resultsBase, "fpkms_by_sample.wide.txt"))
 
 #'  [FPKMs as table](fpkms.long.txt) [FPKMs as Matrix](fpkms.wide.txt)