pydeseq2 - Analyze differential expression in bulk RNA-seq
Performs PyDESeq2 differential expression analysis on bulk RNA-seq counts with multifactor designs, Wald tests, FDR correction, and optional plots.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load count matrices
- Validate sample metadata
- Filter low-count genes
- Specify multifactor designs
- Fit DESeq2 models
- Run Wald tests
- Apply FDR correction
- Shrink log fold changes
- Export analysis results
- Generate volcano and MA plots
Inputs
- Bulk RNA-seq count matrix
- Sample metadata
- Design formula
- Contrast specification
- Analysis parameters
- AnnData dataset
Outputs
- Differential expression results
- Significant gene tables
- Volcano plots
- MA plots
- Serialized analysis objects
- Analysis status messages
Requirements
- Python environment
- PyDESeq2 package
- pandas package
- Compatible count and metadata tables