Perform differential gene expression analysis on bulk RNA-seq count matrices with Wald tests.
npx skills add https://github.com/shushuzn/Rairos --skill pydeseq2-shushuzn
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/shushuzn/Rairos/tree/main/skills/pydeseq2 Command: npx skills add https://github.com/shushuzn/Rairos --skill pydeseq2-shushuzn
It streamlines differential gene expression analysis from bulk RNA-seq count matrices into interpretable, statistically tested results with FDR control.
Run a treated-vs-control bulk RNA-seq differential expression analysis from your counts.csv and metadata.csv by executing: python scripts/run_deseq2_analysis.py --counts counts.csv --metadata metadata.csv --design "~condition" --contrast condition treated control --output results --plots
High-intent search queries and answers about installing and using this skill.
Bulk RNA-seq differential expression analysis requires a samples × genes integer count matrix, matching metadata indices, a Wilkinson design formula, and contrast specification to estimate log2 fold changes and produce FDR-corrected results.
Yes, you can include batch effects and covariates by specifying a multi-factor Wilkinson design formula. This allows the model to normalize unwanted variance and accurately estimate condition-specific log2 fold changes during differential expression testing.
Wald testing evaluates contrast-based log2 fold changes against a null hypothesis to compute raw p-values, then applies Benjamini–Hochberg adjustment to produce padj values for FDR correction across all tested genes.
To generate volcano plots from RNA-seq differential expression results, apply optional LFC shrinkage during analysis to refine log2 fold change estimates, then plot the FDR-corrected padj values against the shrinkage-adjusted effect sizes.
Yes, the workflow exports results as CSV files containing significant differentially expressed genes with their log2 fold changes and FDR-corrected padj values, and can also generate volcano and MA plots for visualization.
LeonChaoXRun bulk RNA-seq differential expression with Wald statistics and FDR-adjusted p-values.
SologaPerforms differential gene expression analysis on bulk RNA-seq data using Python implementation of Ripser2.
Lord1EgyptPerform differential gene expression analysis on bulk RNA-seq count data with PyDESeq2.
HolobiomicsLabPerform end-to-end differential expression analysis on bulk RNA-seq count data with PyDESeq2 workflows.
