Tooluniverse Rnaseq Deseq2

The tooluniverse-rnaseq-deseq2 skill executes DESeq2, a robust R package for differential analysis of count data, specifically tailored for RNA-seq experiments. It automates the workflow to identify genes with statistically significant expression changes between biological conditions.

When to use it

• Analyzing bulk RNA-seq datasets to find differentially expressed genes (DEGs) between treatment and control groups.
• Performing statistical normalization and variance stabilization on raw count matrices before hypothesis testing.
• Generating standard DESeq2 output tables containing log2 fold changes, p-values, and adjusted p-values for downstream interpretation.

Key capabilities

• Runs the full DESeq2 pipeline including data import, design matrix creation, and model fitting.
• Calculates Wald or LRT test statistics to assess gene significance.
• Produces results objects containing shrinkage estimates for fold changes to improve reliability with low counts.

Example prompts

• "Run DESeq2 on my RNA-seq count matrix to find genes upregulated in the disease group compared to healthy controls."
• "Perform differential expression analysis using DESeq2 and return a table of results sorted by adjusted p-value."
• "Execute a standard DESeq2 workflow including normalization and result extraction for my paired sample experiment."

Tips & gotchas

Ensure your input data is a properly formatted count matrix with row names as gene identifiers and column names as sample IDs. The skill requires an active R environment configured within the agent's execution context to process the statistical models correctly.