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数据与复现
TO
tooluniverse-single-cell
维护者 FreedomIntelligence · 最近更新 2026年4月1日
tooluniverse-single-cell：Production-ready single-cell 、 expression matrix analysis ，使用 scanpy，anndata，、 scipy。 执行 scRNA-seq QC，normalization，PCA，UMAP，Leiden/Louvain 聚类，differential expression (Wilcoxon，t-test，DESeq2)，cell type annotation，per-cell-type 统计分析，gene-expression correlation，batch correction (Harmony)，trajectory inference，、 cell-cell communication analysis。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-single-cell
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Comprehensive single-cell RNA-seq analysis 、 expression matrix processing ，使用 scanpy，anndata，scipy，、 ToolUniverse. Designed ，用于 both full scRNA-seq workflows (raw counts to annotated cell types) 、 targeted expression-level analyses (per-cell-type DE，correlation，ANOVA，聚类)。
- IMPORTANT：This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ ，用于 progressive disclosure. This document focuses on high-level decision-making 、 工作流编排。
- import scanpy as sc import anndata as ad import pandas as pd import numpy as np ，面向 scipy import stats ，面向 scipy.cluster.hierarchy import linkage，fcluster，dendrogram ，面向 scipy.spatial.distance import pdist ，面向 sklearn.decomposition import PCA ，面向 sklearn.preprocessing import StandardScaler ，面向 statsmodels.stats.multitest import multipletests。
原始文档
SKILL.md 摘录
When to Use This Skill
Apply when users:
- Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
- Ask about cell type identification, clustering, or annotation
- Need differential expression analysis by cell type or condition
- Want gene-expression correlation analysis (e.g., gene length vs expression by cell type)
- Ask about PCA, UMAP, t-SNE for expression data
- Need Leiden/Louvain clustering on expression matrices
- Want statistical comparisons between cell types (t-test, ANOVA, fold change)
- Ask about marker genes for cell populations
- Need batch correction (Harmony, combat)
- Want trajectory or pseudotime analysis
- Ask about cell-cell communication (ligand-receptor interactions)
- Questions mention "single-cell", "scRNA-seq", "cell type", "h5ad"
- Questions involve immune cell types (CD4, CD8, CD14, CD19, monocytes, etc.)
BixBench Coverage: 18+ questions across 5 projects (bix-22, bix-27, bix-31, bix-33, bix-36)
NOT for (use other skills instead):
- Bulk RNA-seq DESeq2 analysis only → Use
tooluniverse-rnaseq-deseq2 - Gene enrichment only (no expression data) → Use
tooluniverse-gene-enrichment - VCF/variant analysis → Use
tooluniverse-variant-analysis - Statistical modeling (regression, survival) → Use
tooluniverse-statistical-modeling
Core Principles
- Data-first approach - Load, inspect, and validate data before any analysis
- AnnData-centric - All data flows through anndata objects for consistency
- Cell type awareness - Many questions require per-cell-type subsetting and analysis
- Statistical rigor - Proper normalization, multiple testing correction, effect sizes
- Scanpy standard pipeline - Follow established best practices for scRNA-seq
- Flexible input - Handle h5ad, 10X, CSV/TSV, pre-processed and raw data
- Question-driven - Parse what the user is actually asking and extract the specific answer
- Enrichment integration - Chain DE results into GO/KEGG/Reactome enrichment when requested
- Large dataset support - Efficient handling of datasets with >100k cells
Optional
import harmonypy # batch correction bash pip install scanpy anndata leidenalg umap-learn harmonypy gseapy pandas numpy scipy scikit-learn statsmodels
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适用场景
- Have scRNA-seq data (h5ad，10X，CSV count matrices) 、 want analysis。
- Ask about cell type identification，聚类，或 annotation。
- Need differential expression analysis by cell type 或 condition。
- Want gene-expression correlation analysis (e.g.，gene length vs expression by cell type)。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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