update core dependencies

dge_workflow/featcounts_deseq.R

@@ -19,14 +19,15 @@ opts <- docopt(doc, commandArgs(TRUE))
 
 #opts <- docopt(doc, "--contrasts ~/MPI-CBG_work/P5_DESeq/dba_contrasts_human.txt ~/MPI-CBG_work/P5_DESeq/countMatrix_human.txt")
 ## opts <- docopt(doc, "countMatrix.txt")
+## opts <- docopt(doc, "star_counts_matrix.txt")
 
 require(knitr)
 require(DESeq2)
 
 
-devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.12/R/core_commons.R")
-devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.12/R/ggplot_commons.R")
-devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.12/R/bio/diffex_commons.R")
+devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.14/R/core_commons.R")
+devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.14/R/ggplot_commons.R")
+devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.14/R/bio/diffex_commons.R")
 
 
 require.auto(DT)
@@ -149,7 +150,7 @@ summary(results(dds))
 #' The dispersion plot shows how the estimates are shrunk from the gene wise values (black dots) toward the fitted estimates, with the final values used in testing being the blue dots.
 #' The dispersion can be understood as the square of the coefficient of biological variation.  So, if a gene's expression typically differs from replicate to replicate sample by 20%, this gene's dispersion is: .20^2 = .04.
 ## The function estimateDispersions performs three steps. First, it estimates, for each gene and each (replicated) condition, a dispersion value, then, it fits, for each condition, a curve through the estimates. Finally, it assigns to each gene a dispersion value, using either the estimated or the fitted value.
-dds <- estimateDispersions(dds)
+#dds <- estimateDispersions(dds)
 plotDispEsts(dds, main="Dispersion plot")
 
 ########################################################################################################################
@@ -192,7 +193,6 @@ heatmap.2(distMatrix, labRow=colnames(labelcntData), labCol=colnames(labelcntDat
 
 ## extract all de-sets according to our contrasts model
 deResults <- adply(contrasts, 1,  splat(function(sample_1, sample_2){
- #   browser()
         results(dds, contrast=c("condition", sample_1, sample_2)) %>%
         rownames2column("ensembl_gene_id") %>%
         as.data.frame() %>%
@@ -263,8 +263,8 @@ baseMeanPerLvl <- sapply( levels(dds$condition), function(lvl) rowMeans( counts(
 
 ## add base means to diffßex summary
 deResults <- baseMeanPerLvl %>%
-    gather(sample, norm_count, -ensembl_gene_id) %>% 
-    merge(.,., by="ensembl_gene_id", suffixes=c("_1", "_2")) %>% 
+    gather(sample, norm_count, -ensembl_gene_id) %>%
+    merge(.,., by="ensembl_gene_id", suffixes=c("_1", "_2")) %>%
 #    filter(ac(sample_1)<ac(sample_2)) %>%
     # add diffex status
     merge(deResults)