Hi
I'm trying to use DESeq2 to find differentially expressed genes between sample with replicates and sample without replicates. I have 2 condition, control and tumour. I am new to Deseq2 package, do you think my command lines are OK for my experiment?
Thanks in advance
code for find DE in tumour vs control (tumour /control )
> countsTable <- read.delim ("/feature/Cont_control_34.txt", header=TRUE, row.names=1) > pdata = data.frame(condition = factor(c("control","tumour","tumour","tumour"))) > dds <- DESeqDataSetFromMatrix(countData=countsTable, colData = pdata, design=~condition) > colData(dds)$condition <- relevel(colData(dds)$condition, "control","tumour") > design(dds) ~condition > dds <- DESeq(dds) > res <- results(dds) > res <- res[order(res$padj),] > sig.up.results <- res[which(res$padj < 0.05 & res$log2FoldChange > 1),] > sig.down.results <- res[which(res$padj < 0.05 & res$log2FoldChange < -1),] > sig.results <- res[which(res$padj < 0.05),] > sig.results <- sig.results[order(sig.results$log2FoldChange, decreasing=TRUE),]
Also, we have a different routine for *testing* against a fold change threshold, rather than testing a null hypothesis of zero log fold change and then providing post hoc filters. Read over the DESeq2 paper and the vignette if you're interested in this topic.
hi Micheal
Thanks for your help, so there is not any difference between these codes for find DE in tumour vs control (tumour /control ), right?
Ehsan
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