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Scanpy Single-Cell Analysis

An end-to-end Scanpy workflow skill for scRNA-seq: QC, normalization, PCA/UMAP, Leiden clustering, markers and cell-type annotation.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Runs the standard single-cell RNA-seq pipeline with Scanpy: quality control → normalization/log1p → highly variable genes → PCA/UMAP/t-SNE → Leiden clustering → marker genes → cell-type annotation.
  • Prefers a bundled CLI toolkit in scripts/ (run_pipeline.py, inspect_data.py, qc_analysis.py, preprocess.py, reduce_dimensions.py, batch_correct.py, cluster.py, find_markers.py, annotate.py, plot.py, …) — all .h5ad-in/.h5ad-out so steps chain cleanly.
  • Also covers batch integration (harmony/bbknn/combat), Scrublet doublet detection, gene-signature scoring, pseudobulk aggregation for DE, and publication-quality plotting.
  • Includes an R interop runbook for converting Seurat / SingleCellExperiment .rds or .RData objects into .h5ad.

Who it's for

  • Bioinformaticians and grad students analyzing scRNA-seq datasets.
  • Anyone who needs to explore 10x Genomics output or h5ad files quickly.
  • R/Seurat users migrating data into the Python scverse ecosystem.

Example uses

  1. One-shot analysis: python scripts/run_pipeline.py raw.h5ad -o processed.h5ad --resolution 0.5 --n-top-genes 2000 --scrublet produces a clustered object plus figures and marker CSVs.
  2. Multi-sample integration: add --batch-key sample --batch-method harmony to correct batch effects before UMAP/clustering.
  3. Iterative annotation: inspect per-cluster marker CSVs, write a celltypes.json mapping, run annotate.py, then plot.py --kind dotplot --genes CD3D CD14 --groupby cell_type for a figure.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/scanpy/SKILL.md

What I want: (describe your task here)

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↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

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Install the skill I found on AI Skill Mart.
Copy the skills/scanpy folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/scanpy/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sci-skills && mkdir -p ~/.claude/skills && cp -r /tmp/sci-skills/skills/scanpy ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/scanpy ~/.claude/skills/
  5. Install dependencies in a Python 3.12+ environment: uv pip install "scanpy[leiden]" (or pip install "scanpy[leiden]").
  6. Restart Claude Code and ask something like "use the scanpy skill to QC and cluster this h5ad file".
  7. If your input is an R object (.rds/.RData), follow references/r_interop.md to install R and the conversion packages first.
View source on GitHub ↗License: MIT