tooluniverse-rnaseq-deseq2

Core Principles

1. Data-first approach - Load and validate count data and metadata BEFORE any analysis
2. Statistical rigor - Always use proper normalization, dispersion estimation, and multiple testing correction
3. Flexible design - Support single-factor, multi-factor, and interaction designs
4. Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
5. Reproducible - Set random seeds, document all parameters, output complete results
6. Question-driven - Parse what the user is actually asking and extract the specific answer
7. Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested
8. English-first queries - Use English gene/pathway names in all tool calls

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When to Use This Skill

Apply when users:

• Have RNA-seq count matrices and want differential expression analysis
• Ask about DESeq2, DEGs, differential expression, padj, log2FC
• Need dispersion estimates or diagnostics
• Want enrichment analysis (GO, KEGG, Reactome) on DEGs
• Ask about specific gene expression changes between conditions
• Need to compare multiple strains/conditions/treatments
• Ask about batch effect correction in RNA-seq
• Questions mention "count data", "count matrix", "RNA-seq", "transcriptomics"

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ToolUniverse (optional, for gene annotation)

from tooluniverse import ToolUniverse bash pip install pydeseq2 gseapy pandas numpy scipy anndata

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