Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for
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Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for
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scanpy.SKILL.md
---name: scanpy
description: Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
license: BSD-3-Clause
metadata: {"version": "1.3", "skill-author": "K-Dense Inc."}
---# Scanpy: Single-Cell Analysis
## Overview
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. Current stable release: **scanpy 1.12.x** (January 2026).
## Installation
Requires Python **3.12+** (scanpy 1.12 dropped Python ≤3.11) and anndata **≥0.10**.
```bash
uv pip install "scanpy[leiden]"
```
The `[leiden]` extra installs `python-igraph` and `leidenalg`, required for Leiden clustering. For reproducible environments, pin a version: `uv pip install "scanpy[leiden]==1.12.1"`.
For large or out-of-core datasets, many functions support [Dask](https://docs.dask.org/) arrays (experimental):
See the [Using dask with Scanpy](https://scanpy.scverse.org/en/stable/tutorials/experimental/dask.html) tutorial. For GPU-accelerated scanpy-like operations, use [rapids-singlecell](https://rapids-singlecell.readthedocs.io/) as a separate package.
If the input is an R-native single-cell object (`.rds`, `.RData`, Seurat, or SingleCellExperiment), first convert it to `.h5ad` with R tooling, then load it with Scanpy. Read `references/r_interop.md` for agent-run installation and conversion instructions across macOS, Linux, and Windows.
For AnnData structure and I/O details, use the **anndata** skill. For probabilistic models and batch correction, use **scvi-tools**.
## When to Use This Skill
This skill should be used when:
- Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)
- Working with R-friendly single-cell datasets (`.rds`, `.RData`, Seurat, SingleCellExperiment) that need conversion to `.h5ad`
- Performing quality control on scRNA-seq datasets
- Creating UMAP, t-SNE, or PCA visualizations
- Identifying cell clusters and finding marker genes
- Annotating cell types based on gene expression
- Conducting trajectory inference or pseudotime analysis
## Script Toolkit (prefer these over writing code from scratch)
This skill bundles ready-to-run CLI scripts in `scripts/` for every common step. **Run these instead of hand-writing scanpy code** — they handle file loading by extension, figure setup, sensible defaults, raw-count preservation, and progress logging. Each reads and writes `.h5ad`, so they chain together, and each has its own `--help`. Only drop down to writing scanpy code when a task isn't covered by a script or needs unusual customization.
All scripts use a shared `scripts/_common.py` helper (loading, saving, figure config) — keep it alongside the others. Run from the skill directory or pass full paths; figures default to `./figures/`.
Use `rank_genes_groups` for **exploratory cluster markers** only. Per-cell statistical tests inflate p-values because cells are not independent observations. For rigorous differential expression between conditions or samples, pseudobulk first (see below) and use **pydeseq2** or similar tools.
```python
# Find marker genes for each cluster (exploratory)
5. **Validate cell type annotations**: Use multiple marker genes
6. **Use `use_raw=True` for gene expression plots**: Shows normalized counts from `.raw`
7. **Check PCA variance ratio**: Determine optimal number of PCs
8. **Save intermediate results**: Long workflows can fail partway through
9. **Pseudobulk for DE**: Do not treat `rank_genes_groups` p-values as rigorous DE between conditions
10. **Save plots via settings**: Use `sc.settings.autosave` instead of deprecated `save=` on plot functions
11. **Convert R objects before Scanpy**: Use R packages to convert Seurat or SingleCellExperiment `.rds` files to `.h5ad`, preserving counts, metadata, and gene identifiers
## Bundled Resources
### scripts/ (CLI toolkit)
A composable set of `.h5ad`-in/`.h5ad`-out scripts covering the whole workflow plus a one-command end-to-end pipeline. See the **Script Toolkit** section above for the full table and chaining examples. Each script has `--help`. Files:
- `_common.py` — shared loading/saving/figure helpers imported by the others (not a CLI)
- `run_pipeline.py` — full pipeline in one command (flags or `--config` JSON)
- `inspect_data.py`, `convert.py` — explore and load/convert any input format
Consult this when creating publication-ready figures.
### references/r_interop.md
Agent runbook for installing R on macOS, Linux, and Windows, installing CRAN/Bioconductor conversion packages, inspecting `.rds`/`.RData` inputs, converting Seurat or SingleCellExperiment objects to `.h5ad`, and validating the result in Scanpy.
### assets/analysis_template.py
Complete analysis template providing a full workflow from data loading through cell type annotation. Copy and customize this template for new analyses:
```bash
cp assets/analysis_template.py my_analysis.py
# Edit parameters and run
python my_analysis.py
```
The template includes all standard steps with configurable parameters and helpful comments.
### assets/ JSON templates
Edit-and-pass templates so you don't author config/mappings from scratch:
- `assets/pipeline_config.json` — parameter set for `run_pipeline.py --config`
- `assets/celltype_mapping.json` — cluster → cell-type map for `annotate.py --mapping`
- `assets/gene_signatures.json` — gene-set signatures for `score_genes.py --gene-sets`
