pydeseq2 - Perform bulk RNA-seq differential expression analysis
Analyzes bulk RNA-seq count data with PyDESeq2 using DESeq2 workflows, Wald tests, FDR correction, optional LFC shrinkage, and plots.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load count matrices
- Validate and filter data
- Specify experimental designs
- Fit DESeq2 models
- Perform Wald tests
- Apply FDR correction
- Apply LFC shrinkage
- Export analysis results
- Generate volcano and MA plots
Inputs
- RNA-seq count matrix
- Sample metadata
- Design formula
- Contrast specification
- Filtering parameters
- Statistical thresholds
Outputs
- Differential expression result tables
- Significant gene tables
- Volcano plots
- MA plots
- Serialized analysis objects
- Analysis summaries
Requirements
- Python environment
- PyDESeq2 package
- pandas package
- Optional AnnData package
- File system access for exports