differential-expression

To run differential expression analysis, you provide a raw integer count matrix and sample metadata to the skill, which then applies count-aware modeling using PyDESeq2 to yield ranked genes and diagnostic plots.

Yes, PyDESeq2 supports bulk RNA-seq differential expression analysis by validating design factors like condition and batch, checking for confounding variables before fitting models to generate accurate gene rankings.

You generate volcano plots by specifying an explicit contrast for your count matrix; the skill automatically produces publication-ready volcano and MA plots alongside pathway-ready result tables.

The best way to validate sample metadata is using the skill's built-in design validation, which checks replicate counts and identifies confounding variables in your design factors before model fitting begins.

You need raw integer counts for bulk RNA-seq differential expression, as the skill applies count-aware modeling that requires unnormalized count matrices along with associated sample metadata.

Explicit contrasts and thresholding are required to filter and define specific comparisons between sample conditions, ensuring the differential expression results accurately reflect the biological question of interest.