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Load when comparing gene expression between two conditions in bulk RNA-seq count data. Skip when the data is single-cell (use sc-de) or spatial (use spatial-de), or when you need exon-level alternative splicing (use bulkrna-splicing).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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| name | bulkrna-de |
| description | Load when comparing gene expression between two conditions in bulk RNA-seq count data. Skip when the data is single-cell (use sc-de) or spatial (use spatial-de), or when you need exon-level alternative splicing (use bulkrna-splicing). |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","differential-expression","DESeq2","volcano","MA-plot","fold-change"] |
| requires | ["numpy","pandas","matplotlib","scipy"] |
The user has a bulk RNA-seq count matrix (genes × samples) and wants to know which genes change between two groups (control vs treatment, tumour vs normal, etc.). PyDESeq2 is preferred when ≥2 replicates per condition exist; Welch's t-test is the fallback for the single-replicate / no-PyDESeq2 case.
| Input | Format | Required |
|---|---|---|
| Count matrix | .csv (gene id col + sample count cols) | yes |
| Output | Path | Notes |
|---|---|---|
| DE results | tables/de_results.csv | DESeq2-style columns (gene, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj) |
| Significant DEGs | tables/de_significant.csv | filtered by padj + |
| Volcano | figures/volcano_plot.png | top genes labelled |
| MA plot | figures/ma_plot.png | threshold lines drawn |
| p-value diagnostic | figures/pvalue_histogram.png | check for inflation |
| Report | report.md + result.json | always written |
--padj-cutoff and --lfc-cutoff.pydeseq2 is not importable. Check result.json["method_used"] to confirm which engine actually ran — the volcano-plot title alone does not surface the fallback.--control-prefix (default ctrl) or --treat-prefix (default treat); columns matching neither prefix are silently dropped.result.json["n_tested"] is authoritative.# Demo run (synthetic 200-gene × 12-sample dataset)
python omicsclaw.py run bulkrna-de --demo
# Realistic run with custom prefixes and stricter cutoffs
python omicsclaw.py run bulkrna-de \
--input counts.csv --output results/ \
--control-prefix wt --treat-prefix ko \
--padj-cutoff 0.01 --lfc-cutoff 1.5
references/parameters.md — every CLI flag and tuning hintreferences/methodology.md — PyDESeq2 vs t-test, design validation, LFC shrinkage and transformation guidancereferences/output_contract.md — exact output directory layoutbulkrna-qc (upstream count-matrix QC), bulkrna-enrichment (downstream pathway enrichment of DEG lists), bulkrna-coexpression (parallel WGCNA), bulkrna-splicing (exon-level alternative splicing)