From caeeea12e956cdf604277fad658bf5b3f537e7e9 Mon Sep 17 00:00:00 2001
From: Holger Brandl <holgerbrandl@gmail.com>
Date: Mon, 21 Sep 2015 13:49:54 +0200
Subject: [PATCH] update core dependencies
---
dge_workflow/featcounts_deseq.R | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/dge_workflow/featcounts_deseq.R b/dge_workflow/featcounts_deseq.R
index 0c81e14..72baaec 100755
--- a/dge_workflow/featcounts_deseq.R
+++ b/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)
--
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