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Question 10 of 20 Fundamentals Interpretation

What does the adjusted p-value column in a DESeq2 results table tell you?

Short answer

It is a Benjamini-Hochberg adjusted p-value that controls the false discovery rate. Taking every gene with padj below 0.1 means you expect about 10 percent of that list to be false positives. It does not say how large or important any change is.

Why

An RNA-seq experiment tests thousands of genes at once. If 20,000 genes are tested at a raw p-value of 0.05 and none truly changes, about 1,000 will pass by chance. The Benjamini-Hochberg method adjusts the p-values so that a cutoff controls the expected share of false discoveries in the list you keep, rather than the chance of any single error.

DESeq2 labels the column as BH adjusted p-values, and its results function uses an FDR target of 0.1 by default. Before adjusting, DESeq2 applies independent filtering. Genes with very low mean counts have almost no power, so they are left out of the adjustment and get NA in padj.

That raises the number of genes that pass at the chosen FDR. NA can also mean every sample had zero counts, or a sample had an extreme count outlier. For example, a list of 400 genes at padj below 0.1 should be read as roughly 360 real changes and 40 false ones, without knowing which.

The guarantee also assumes the model is right. A confounded batch or a missing covariate can make every number in the column optimistic.

A strong answer covers

  • padj controls the false discovery rate of the whole list kept
  • DESeq2 uses Benjamini-Hochberg and an FDR target of 0.1 by default
  • Independent filtering sets padj to NA for genes with very low counts
  • padj says nothing about effect size or biological importance

Common mistakes

  • Reading padj of 0.01 as a 99 percent chance that this particular gene changed
  • Treating genes with NA in padj as tested and not significant
  • Assuming FDR control still holds when batch is confounded with treatment