Analyze bulk RNA-seq count data for differential gene expression with PyDESeq2.
npx skills add https://github.com/dralkh/seerai --skill pydeseq2-dralkh
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/dralkh/seerai/tree/main/skills/pydeseq2 Command: npx skills add https://github.com/dralkh/seerai --skill pydeseq2-dralkh
It removes the manual friction from bulk RNA-seq differential expression analysis by guiding you from raw count matrices and sample metadata to statistically tested gene-level results.
Use this skill to analyze your RNA-seq count matrix and metadata with PyDESeq2 and return the full differential expression results along with the most significant genes.
High-intent search queries and answers about installing and using this skill.
To run differential expression analysis on bulk RNA-seq count data, provide a samples-by-genes count matrix and matched metadata. The workflow applies normalization, dispersion fitting, and Wald tests to return statistically tested gene-level results.
Yes, you can adjust for batch effects in RNA-seq differential expression workflows. The analysis supports flexible experimental designs using formula-based design strings to handle batch-adjusted models, interaction effects, and continuous covariates.
LFC shrinkage in RNA-seq analysis visually stabilizes log fold-change estimates for genes with low counts or high dispersion. Apply it during differential expression testing to generate more accurate effect sizes for downstream ranking and visualization.
Yes, PyDESeq2 supports exporting results as H5AD files for downstream analysis. The workflow can output portable AnnData files alongside CSV files containing significant genes and ranked differential expression results.
Differential gene expression testing requires samples-by-genes count matrices and matched metadata as input. You must also provide explicit contrasts and formulaic design strings to define the statistical model for the workflow.
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