菜单
Differential expression for single-cell RNA-seq using exploratory Scanpy ranking, R-backed MAST, or replicate-aware pseudobulk DESeq2. The wrapper separates cluster/group marker ranking from sample-aware condition DE.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill sc-de命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | sc-de |
| description | Differential expression for single-cell RNA-seq using exploratory Scanpy ranking, R-backed MAST, or replicate-aware pseudobulk DESeq2. The wrapper separates cluster/group marker ranking from sample-aware condition DE. |
| version | 0.6.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["singlecell","differential-expression","markers","wilcoxon","deseq2"] |
| metadata | {"omicsclaw":{"domain":"singlecell","allowed_extra_flags":["--celltype-key","--group1","--group2","--groupby","--log2fc-threshold","--logreg-solver","--method","--n-top-genes","--padj-threshold","--pseudobulk-min-cells","--pseudobulk-min-counts","--sample-key","--r-enhanced"],"param_hints":{"wilcoxon":"[Truncated]","t-test":"[Truncated]","logreg":"[Truncated]","mast":"[Truncated]","deseq2_r":"[Truncated]"},"saves_h5ad":true,"requires_preprocessed":true,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"S","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["differential expression","marker genes","de analysis","wilcoxon","pseudo-bulk"]}} |
Implemented methods:
wilcoxont-testlogregmast for R-backed hurdle-model DEdeseq2_r for pseudobulk DE| Format | Extension / form | Current wrapper support | Notes |
|---|---|---|---|
| AnnData | .h5ad | yes | current direct input path |
| Demo | --demo | yes | bundled fallback |
.h5adwilcoxon, t-test, logreg, and mast expect X = normalized_expressiondeseq2_r expects raw counts, preferably in layers["counts"], otherwise aligned raw counts in adata.raw, otherwise count-like adata.Xobssc-clusteringsc-cell-annotation or another step that defines celltype_keyfigure_data, and processed.h5ad.python skills/singlecell/scrna/sc-de/sc_de.py \
--input <processed.h5ad> --groupby leiden --method wilcoxon --output <dir>
python skills/singlecell/scrna/sc-de/sc_de.py \
--input <processed.h5ad> --groupby condition \
--group1 treated --group2 control --method t-test --output <dir>
python skills/singlecell/scrna/sc-de/sc_de.py \
--input <processed.h5ad> --groupby leiden --method logreg \
--logreg-solver saga --output <dir>
python skills/singlecell/scrna/sc-de/sc_de.py \
--input <processed.h5ad> --method deseq2_r \
--groupby condition --group1 treated --group2 control \
--sample-key sample_id --celltype-key cell_type \
--pseudobulk-min-cells 10 --pseudobulk-min-counts 1000 --output <dir>
| Parameter | Role | Notes |
|---|---|---|
--method | DE backend | wilcoxon, t-test, logreg, mast, or deseq2_r |
--groupby | grouping column | core comparison axis |
--group1 / --group2 | comparison levels | especially important for mast and deseq2_r |
--n-top-genes | compact export size | ranking-output control |
--logreg-solver | logistic-regression optimizer | logreg only |
--sample-key | replicate/sample column | required by deseq2_r |
--celltype-key | pseudobulk aggregation label | used by deseq2_r |
--pseudobulk-min-cells | minimum cells per pseudobulk bin | deseq2_r only |
--pseudobulk-min-counts | minimum counts per pseudobulk bin | deseq2_r only |
--padj-threshold / --log2fc-threshold | figure summary thresholds | mostly affects exported volcano/summary figures |
Current OmicsClaw exploratory DE includes:
wilcoxont-testlogregmastThese paths answer the cluster-marker or group-ranking question on normalized expression from adata.X.
Current OmicsClaw deseq2_r:
layers["counts"]sample_key and celltype_keyImportant implementation note:
deseq2_r is the only path here that is explicitly replicate-aware in the current wrapper.Successful runs write:
processed.h5adreport.mdresult.jsontables/de_full.csvtables/markers_top.csvfigure_data/manifest.jsonreproducibility/commands.shThe current wrapper writes direct figure outputs rather than a recipe-driven gallery:
figures/marker_dotplot.pngfigures/rank_genes_groups.pngfigures/de_effect_summary.pngfigures/de_group_summary.pngfigures/pseudobulk_group_summary.png*_volcano.png*_ma.pngreport.mdtables/de_full.csvtables/markers_top.csvfigures/de_group_summary.png or pseudobulk summary figuresprocessed.h5admast requires an R environment with MAST, SingleCellExperiment, and zellkonverter.deseq2_r requires an R environment with DESeq2, SingleCellExperiment, and zellkonverter.README.md and notebook-style reproducibility artifacts when notebook export dependencies are available.sc-clustering first.mast and deseq2_r are real public methods but require their R stacks up front in the current wrapper.sample_key and celltype_key as part of the statistical design for deseq2_r, not as cosmetic metadata.knowledge_base/knowhows/KH-sc-de-guardrails.md.knowledge_base/skill-guides/singlecell/sc-de.md.| Flag | Type | Default | Description | Validation |
|---|---|---|---|---|
--input | str | — | Input .h5ad file | required unless --demo |
--output | str | — | Output directory | required |
--demo | flag | off | Run with bundled PBMC3k data | — |
--groupby | str | leiden | Group/condition column in obs | must exist in obs |
--method | str | wilcoxon | DE backend: wilcoxon, t-test, logreg, mast, deseq2_r | validated against METHOD_REGISTRY |
--n-top-genes | int | 10 | Number of top genes to export per group | min 1 |
--logreg-solver | str | lbfgs | Logistic-regression optimizer | choices: lbfgs, liblinear, newton-cg, sag, saga |
--group1 | str | None | Comparison group (vs --group2) | — |
--group2 | str | None | Reference group (vs --group1) | — |
--sample-key | str | None | Sample/replicate column for pseudobulk | used by deseq2_r |
--celltype-key | str | cell_type | Cell type column for pseudobulk aggregation | used by deseq2_r |
--pseudobulk-min-cells | int | 10 |
Activated by --r-enhanced. Files written to figures/r_enhanced/.
| Renderer | Output file | figure_data CSV | Plot description | Required R packages |
|---|---|---|---|---|
plot_de_volcano | r_de_volcano.png | de_top_markers.csv | Volcano plot with up/down/NS coloring and gene labels | ggplot2, ggrepel |
plot_de_heatmap | r_de_heatmap.png | de_top_markers.csv | Expression heatmap of top DE genes across groups | ggplot2, ComplexHeatmap |
plot_feature_violin | r_feature_violin.png | feature_expression.csv | Violin plots of top DE gene expression per group | ggplot2 |
plot_feature_cor | r_feature_cor.png | feature_correlation.csv | Gene-gene expression correlation scatter | ggplot2 |
plot_de_manhattan | r_de_manhattan.png | de_top_markers.csv | Manhattan-style plot of DE significance by gene rank | ggplot2 |
Upstream: sc-clustering, sc-cell-annotation, or sc-markers Downstream: sc-enrichment (pathway enrichment on DE results)
| Minimum cells per pseudobulk bin |
| non-negative |
--pseudobulk-min-counts | int | 1000 | Minimum total counts per pseudobulk bin | non-negative |
--padj-threshold | float | 0.05 | Adjusted p-value cutoff for summary figures | 0.0–1.0 |
--log2fc-threshold | float | 1.0 | log2FC cutoff for volcano/summary figures | non-negative |
--r-enhanced | flag | off | Also render R Enhanced ggplot2 figures | — |