Created
November 1, 2013 21:51
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basic work flow of DESeq
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| setwd("/home/tommy/scripts") | |
| library("DESeq") | |
| countsTable<- read.delim("All_counts_nozero_1pseudocount_with_header.txt", header=TRUE) | |
| rownames(countsTable)<- countsTable$Gene | |
| countsTable<- countsTable[,-1] | |
| head(countsTable) | |
| conds<- factor(c("alpha","beta","alpha","beta","alpha","beta")) | |
| cds<- newCountDataSet(countsTable, conds) | |
| cds<- estimateSizeFactors(cds) | |
| sizeFactors(cds) | |
| head(counts(cds)) | |
| head(counts(cds,normalized=TRUE)) | |
| cds<- estimateDispersions(cds) | |
| plotDispEsts(cds) | |
| res = nbinomTest( cds, 'alpha', 'beta' ) | |
| head(res) | |
| plotMA(res) | |
| hist(res$pval, breaks=100, col='skyblue', border='slateblue', main='') | |
| resSig = res[ res$pval < 0.1, ] | |
| head(resSig) | |
| cdsBlind<- estimateDispersions(cds, method= "blind") | |
| vsd<- varianceStabilizingTransformation(cdsBlind) | |
| plotPCA(vsd, intgroup=c("condition")) |
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