tooluniverse-single-cell

Single-Cell Genomics and Expression Matrix Analysis

RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:

• *_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
• Pre-computed result files (CSV/TSV with names like *results*, *deseq*, *enrich*, *stats*, *_simplified.csv) → read directly and report the requested value
• Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).

---

Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse.

---

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

---

When to Use This Skill

Apply when users:

• Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
• Need scRNA-seq quality control / QC gating: deciding cell filters by mito % (pct_counts_mt), gene/UMI counts, doublets, ambient RNA, empty droplets
• 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, batch correction, trajectory, or cell-cell communication

NOT for (use other skills instead):

• Bulk RNA-seq DESeq2 only -> tooluniverse-rnaseq-deseq2
• Gene enrichment only -> tooluniverse-gene-enrichment
• VCF/variant analysis -> tooluniverse-variant-analysis
• Deep-learning integration/batch correction (scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI) -> tooluniverse-scvi-tools