bio-differential-expression-edger-basics - Perform bulk RNA-seq differential expression with edgeR
Analyzes bulk RNA-seq count data with edgeR negative-binomial GLMs, quasi-likelihood tests, contrasts, TREAT, voom, and edgeR v4 workflows.
Tags
Updated: 2026-10-15RedgeRbulk RNA-seqdifferential expressionnegative binomial GLMquasi-likelihoodtranscript-level analysisvoom
Capabilities
What this skill does
- Construct DGEList objects
- Filter low-expression genes
- Normalize library sizes
- Estimate robust dispersions
- Fit quasi-likelihood GLMs
- Test differential expression
- Define design contrasts
- Apply fold-change thresholds
- Analyze heterogeneous sample quality
- Handle transcript-level counts
Inputs
- Bulk RNA-seq count matrix
- Sample group labels
- Experimental design
- Contrast specifications
- Fold-change threshold
- Installed R packages
Outputs
- Ranked differential expression table
- Dispersion estimates
- Normalization factors
- Differential expression test results
- Diagnostic plots
Requirements
- R environment
- edgeR 4.0 or later
- limma 3.58 or later
- statmod 1.5 or later for voom internals
- tximport 1.30 or later for catchSalmon
- Compatible package APIs