tooluniverse-single-cell
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor co
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-single-cell --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Single-Cell Genomics and Expression Matrix Analysis Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse. Designed for both full scRNA-seq workflows (raw counts to annotated cell types) and targeted expression-level analyses (per-cell-type DE, correlation, ANOVA, clustering). **IMPORTANT**: This skill handles complex multi-workflow analysis. Most implementation details have been moved to `references/` for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration. --- ## 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) -
- When to Use This Skill
- Core Principles
- Required Python Packages
- High-Level Workflow Decision Tree
- Common Analysis Patterns (BixBench)
- Pattern 1: Per-Cell-Type Differential Expression
- Pattern 2: Gene Property vs Expression Correlation
- Pattern 3: PCA on Expression Matrix
- Pattern 4: Statistical Comparison Between Cell Types
- Pattern 5: ANOVA Across Cell Types
- Pattern 6: Cell-Cell Communication Analysis
- Scanpy vs Seurat Equivalents
- When to Use ToolUniverse Tools
- Gene Annotation and Validation
pip install scanpy anndata leidenalg umap-learn harmonypy gseapy pandas numpy scipy scikit-learn statsmodels
What does the tooluniverse-single-cell skill do?
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor co
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-single-cell --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.