Question: DESeq2 results interpretation
gravatar for anna
11 months ago by
anna0 wrote:

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")
results deseq2 interpretation • 1.1k views
ADD COMMENTlink modified 13 days ago • written 11 months ago by anna0
gravatar for Kevin Blighe
11 months ago by
Kevin Blighe46k
Kevin Blighe46k wrote:

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 counts, 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

NB - with blind=FALSE, the normalised counts are transformed and adjusted based on your design formula; with blind=TRUE, the normalised counts are transformed as is


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)


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


ADD COMMENTlink modified 11 months ago • written 11 months ago by Kevin Blighe46k
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