Analyze bulk RNA-seq count matrices with DESeq2 workflows and FDR control.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pydeseq2-jasrajtulsi
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/pydeseq2 Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pydeseq2-jasrajtulsi
This Skill automates bulk RNA-seq differential expression analysis so you can move from raw count tables to statistically tested gene-level results without assembling the workflow by hand.
Use the pydeseq2 skill to analyze the attached count matrix with the provided metadata and a treated-versus-control contrast, then return significant genes and saved result files.
High-intent search queries and answers about installing and using this skill.
To identify differentially expressed genes from bulk RNA-seq count data, you need an integer samples × genes count matrix and sample metadata. The analysis fits size factors, dispersions, and applies Wald tests with Benjamini-Hochberg FDR correction to quantify condition effects.
Yes, you can control for batch effects in RNA-seq differential expression analysis by using flexible formulaic design strings. This allows you to handle batch-adjusted models alongside single-factor comparisons, continuous covariates, and interaction terms across samples.
You perform LFC shrinkage and generate volcano plots for RNA-seq results during the result refinement stage. After fitting log fold changes and running Wald tests, apply optional LFC shrinkage to prioritize significant genes and export visual MA and volcano plots for interpretation.
Yes, pydeseq2 supports multi-factor experimental designs with interaction terms. It handles treatment-versus-control comparisons, continuous covariates, and batch correction using explicit formulaic design strings and contrasts to quantify specific condition effects.
The best way to export statistically tested gene-level results from RNA-seq workflows is to output CSV and H5AD files after running the analysis. This refines the data by applying Benjamini-Hochberg FDR control and optional LFC shrinkage for downstream biology validation.
dralkhAnalyze bulk RNA-seq count data for differential gene expression with PyDESeq2.
ya-wayIdentify differentially expressed genes from RNA-seq count data using edgeR.
MubasherMohammedExecute differential expression analysis on RNA-seq count matrices with quality control, PCA/volcano plots, and markdown reports.
OwnLabAIIdentify differentially expressed genes from bulk RNA-seq counts with PyDESeq2.
