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Load when running pathway / GO term enrichment on a bulk RNA-seq DE result list. Skip if the input is single-cell (use sc-enrichment), spatial (use spatial-enrichment), or for metabolite pathways (use metabolomics-pathway-enrichment).
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。
SOC 職業分類に基づく
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Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.
Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.
Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.
Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.
Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.
| name | bulkrna-enrichment |
| description | Load when running pathway / GO term enrichment on a bulk RNA-seq DE result list. Skip if the input is single-cell (use sc-enrichment), spatial (use spatial-enrichment), or for metabolite pathways (use metabolomics-pathway-enrichment). |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","enrichment","GSEA","ORA","GO","KEGG","Reactome","pathway"] |
Run after bulkrna-de to ask "which biological pathways are enriched
in the DEG list?". Two modes: ORA (over-representation analysis on a
significance-filtered gene list) and pre-ranked GSEA (full ranked
list, no threshold needed). Backed by GSEApy with R clusterProfiler
and a built-in hypergeometric implementation as fallbacks.
| Input | Format | Required |
|---|---|---|
| DE results | .csv (gene + log2FoldChange + padj cols) | yes (or --demo) |
| Custom gene set | --gene-set-file GMT/JSON | optional |
| Output | Path | Notes |
|---|---|---|
| Enrichment table | tables/enrichment_results.csv | per-term padj, NES, leading-edge genes |
| Top terms barplot | figures/enrichment_barplot.png | top N by padj |
| Dotplot | figures/enrichment_dotplot.png | size = gene count, colour = padj |
| Report | report.md + result.json | always |
log2FoldChange with a warning at bulkrna_enrichment.py:67 if the heuristic finds no preferred metric.--method: ORA, GSEA, or auto. Hard-fails at :370 for unknown methods.:379).:436 ORA path, :479 GSEA path).logger.warning-only (bulkrna_enrichment.py:379, :436, :479); the chosen backend is in result.json["method_used"]. Built-in is the least feature-rich (no permutation-based GSEA p-values) — verify which engine actually ran before claiming a particular method.result.json. :67 warns when it falls back to log2FoldChange, but if your DE table uses a non-standard column name (e.g. lfc instead of log2FoldChange), the heuristic may pick the wrong column without complaint. The chosen metric is logged at INFO (:450) but does NOT make it into the summary dict (which carries only n_input_genes, n_significant, method_used, n_terms_tested, n_enriched_terms, enrichment_df). Grep the run's stderr for "Using gseapy for pre-ranked GSEA (metric: ...)" to confirm.--padj-cutoff and --lfc-cutoff only apply to ORA. Pre-ranked GSEA uses the full ranked list and ignores both flags — passing them on a GSEA run silently does nothing. This is correct GSEA behaviour, but easy to mistake for a bug.:516 and :525 warn ("No enrichment results to plot" / "No terms with valid padj") and skip plotting; the run still exits 0 with empty figures and an empty tables/enrichment_results.csv. Loosen thresholds or pre-filter the input if your DE list is sparse.python omicsclaw.py run bulkrna-enrichment --demo
python omicsclaw.py run bulkrna-enrichment \
--input de_results.csv --output results/ --method ora
python omicsclaw.py run bulkrna-enrichment \
--input de_results.csv --output results/ --method gsea \
--gene-set-file hallmark.gmt
references/parameters.md — every CLI flag and tuning hintreferences/methodology.md — ORA vs GSEA, three-tier engine fallback, ranking-metric selectionreferences/output_contract.md — exact output directory layoutbulkrna-de (upstream — DE table input), bulkrna-ppi-network (parallel: same DEG list → STRING network), sc-enrichment / spatial-enrichment (single-cell / spatial siblings), metabolomics-pathway-enrichment (metabolite-side sibling)