Agent skill · research science · k-dense-ai
scanpy
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.
Why this skill is useful
Adds multiple executable scripts for single-cell RNA-seq analysis that streamline complex workflows and automate common tasks.
What it needs
Requires anndata, python installed locally. About 8k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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. 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 arrays (experimental): See the Using dask with Scanpy tutorial. For GPU-accelerated scanpy-like operations, use rapids-singlecell 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/rinterop.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 Generating publication-quality single-cell plots Script Toolkit (prefer these over writing code from scratch) This skill bundles ready-to-run CLI scripts in scripts/ for every common step. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scanpy