Individual skill
tooluniverse-single-cell
Single-cell RNA-seq analysis with scanpy/anndata — h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.
plugins/tooluniverse/skills/tooluniverse-single-cell/SKILL.md
Source commit 5598a9fc97e32964fe9b703818e7d0747142f179
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- Last checked
- Nov 5, 2026
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- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
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- agents Repository topic
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- reasoning-language-models Repository topic
- tool-use Repository topic
- ai-agents Repository topic
- ai-scientists Repository topic
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.env.templateasset · 499 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesSKILL.mdcore · 23,192 bytesanalysis_patterns.mddoc · 6,416 bytesreferences/cell_communication.mddoc · 20,360 bytesreferences/clustering_guide.mddoc · 9,616 bytesreferences/marker_identification.mddoc · 5,540 bytesreferences/scanpy_workflow.mddoc · 10,327 bytesreferences/scrna_qc.mddoc · 10,722 bytesreferences/seurat_workflow.mddoc · 4,510 bytesreferences/trajectory_analysis.mddoc · 2,842 bytesreferences/troubleshooting.mddoc · 8,701 bytesscripts/find_markers.pyruns · 3,626 bytesscripts/normalize_data.pyruns · 2,608 bytesscripts/qc_metrics.pyruns · 4,296 bytesscripts/scrna_qc.pyruns · 6,904 bytes
Read excerpt
# 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).
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Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse.