Individual skill
pydeseq
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs.
scientific-skills/Data Analysis/pydeseq2/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
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- engineering Derived category
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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
Use this skill when you need to run DESeq2-style differential expression in Python, especially in these scenarios:
1. **Case vs control bulk RNA-seq** from a raw integer count matrix (e.g., treated vs control).
2. **Multi-factor designs** to adjust for batch effects or covariates (e.g., `~ batch + condition`, `~ age + condition`).
3. **DESeq2 migration** when converting an R DESeq2 workflow into a Python pipeline.
4. **Pipeline integration** where results must stay in Python objects (pandas/AnnData) for downstream QC, plots, or reporting.
5. **Requests mentioning** “DESeq2”, “differential expression”, “Wald test”, “FDR/padj”, “volcano plot”, “MA plot”, or “PyDESeq2”.
## Key Features
- End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling.