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DESeq2 results interpretation

how can i see the expression level? and which result shows if a gene is differentially expressed or not?

Also, how can i add the row names containing the name of the genes? Here's my command line: 

> dds <- DESeqDataSetFromMatrix(countData=countsTable, colData = colData, design=~condition)
> dds$condition <- relevel(dds$condition, ref = "14d")
> colData(dds)$condition <- relevel(colData(dds)$condition, "14d")
deseq2 results interpretation
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written by anna • anna

1 answer

The negative binomial-distributed normalised counts on which the statistical inferences are made can be accessed via:

counts(dds, normalized=TRUE)

You may also have access to the transformed expression levels, on which statistical inferences are not made but which can be used for downstream functions, like plotting expression levels in heatmaps or box-and-whisker plots, for example.

vsd <- vst(dds, blind=FALSE) # variance-stabilised transformation

rld <- rlog(dds, blind=FALSE) # reguarised log transformation

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Typically, padj (adjusted P value) < 0.05 is a good starting point for determining which genes are differentially expressed. For example:

subset(res, padj<0.05)

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Gene names should be carried all the way through from your countsTable object. Double-check that you have rownames set for countsTable.

Kevin

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written by Kevin Blighe Kevin Blighe

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