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Read count matrix or import transcript-level estimates via tximport.\n2. Read sample metadata and validate that sample IDs match between counts and metadata.\n3. Build design formula (`~ condition`, or `~ batch + condition` if batch column is provided).\n4. If DESeq2: create `DESeqDataSetFromMatrix` (or `DESeqDataSetFromTximport`), run `DESeq()`, extract `results()`.\n5. If edgeR: create `DGEList`, `calcNormFactors`, `estimateDisp`, `glmQLFit`, `glmQLFTest`.\n6. Extract results: gene ID, log2 fold-change, p-value, adjusted p-value, baseMean (DESeq2) or logCPM (edgeR).\n7. Generate plots: PCA of samples, volcano plot, MA plot, heatmap of top DE genes.\n8. Write results table sorted by adjusted p-value.\n9. Report: total genes tested, number significant (up-regulated and down-regulated), summary of top hits.\n\n## Output Contract\n\n- DE results table (TSV): gene_id, log2FoldChange, pvalue, padj, baseMean\u002FlogCPM\n- PCA plot (PDF)\n- Volcano plot (PDF)\n- MA plot (PDF)\n- Heatmap of top DE genes (PDF)\n- Normalized count matrix (TSV)\n- R session info (text file)\n\n## Limits\n\n- Requires R with DESeq2 or edgeR installed (Bioconductor packages).\n- Minimum 3 biological replicates per condition recommended for reliable statistics.\n- Not suitable for single-cell RNA-seq without modifications.\n- Python alternative: PyDESeq2 for DESeq2-equivalent analysis.\n- Common failure cases: sample ID mismatch between counts and metadata, too few replicates, incorrect contrast specification.\n","# Skill: RNA-seq Differential Expression\n\n## Use When\n\n- User has a count matrix or transcript quantification and wants to find differentially expressed genes\n- User wants to perform statistical DE testing with proper normalization\n- User needs volcano plots, MA plots, PCA, and heatmaps\n- User wants to compare DESeq2 vs edgeR results\n\n## Inputs\n\n- Required:\n  - Count matrix (TSV with gene IDs as rows, samples as columns) OR Salmon\u002Fkallisto quantification directories plus a tx2gene mapping file\n  - Sample metadata (TSV with columns: sample_id, condition, and optionally batch)\n- Optional:\n  - Tool choice: `deseq2` or `edger` (default: `deseq2`)\n  - Contrast: condition levels to compare (e.g., `treated,control`)\n  - FDR threshold (default: `0.05`)\n  - log2 fold-change threshold (default: `1`)\n  - Output directory (default: `.\u002Fde_output`)\n\n## Workflow\n\n1. Read count matrix or import transcript-level estimates via tximport.\n2. Read sample metadata and validate that sample IDs match between counts and metadata.\n3. Build design formula (`~ condition`, or `~ batch + condition` if batch column is provided).\n4. If DESeq2: create `DESeqDataSetFromMatrix` (or `DESeqDataSetFromTximport`), run `DESeq()`, extract `results()`.\n5. If edgeR: create `DGEList`, `calcNormFactors`, `estimateDisp`, `glmQLFit`, `glmQLFTest`.\n6. Extract results: gene ID, log2 fold-change, p-value, adjusted p-value, baseMean (DESeq2) or logCPM (edgeR).\n7. Generate plots: PCA of samples, volcano plot, MA plot, heatmap of top DE genes.\n8. Write results table sorted by adjusted p-value.\n9. 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