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scanpy

scanpy

github.com/tondevrelData & databases2451 installsUpdated 39w ago
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About scanpy

Scanpy is a Python toolkit for single-cell RNA sequencing analysis, aimed at researchers studying cell populations and states. It combines AnnData-based data handling with dimensionality reduction, clustering, and trajectory analysis.

Key features

  1. 01AnnData expression and metadata handling
  2. 02Quality control and normalization
  3. 03PCA, UMAP, and t-SNE reduction
  4. 04Cell clustering and marker-gene analysis
  5. 05Developmental trajectory inference

Use cases

  1. 01Analyze single-cell RNA sequencing data
  2. 02Identify cell types in heterogeneous tissue
  3. 03Compare cell populations across conditions
  4. 04Investigate developmental trajectories

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tondevrel/scientific-agent-skills --skill scanpy

After installing, type/scanpyin Claude Code to confirm it loaded.

Details

From the project README

Scanpy - Single-Cell Analysis

Scanpy processes high-dimensional biological data, reducing it via PCA/UMAP to identify rare cell populations in tissues or microbiomes.

When to Use

  • Analyzing single-cell RNA sequencing (scRNA-seq) data.
  • Identifying cell types and states in heterogeneous tissues.
  • Reconstructing developmental trajectories.
  • Comparing cell populations between conditions.
  • Discovering rare cell types.

Core Principles

AnnData Format

Scanpy uses AnnData objects that store expression matrix, cell metadata, and gene annotations together.

Dimensionality Reduction

High-dimensional gene expression (20,000+ genes) is reduced to 2D/3D for visualization (PCA → UMAP/t-SNE).

Clustering

Cells are grouped by similarity in gene expression space to identify cell types.

Quick Reference

Standard Imports
import scanpy as sc
import pandas as pd
import numpy as np
Basic Patterns
# 1. Load dataset (AnnData object)
adata = sc.read_h5ad("cells.h5ad")
# Or: adata = sc.read_10x_mtx("path/to/mtx")

# 2. QC and Normalization
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# 3. Dimensionality Reduction & Visualization
sc.pp.highly_variable_genes(adata)
sc.tl.pca(adata)
sc.tl.umap(adata)
sc.pl.umap(adata, color=['cell_type', 'gene_A'])

# 4. Clustering
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color='leiden')

Critical Rules

✅ DO
  • Set scanpy settings - Use sc.settings.verbosity = 3 for progress info.
  • Filter low-quality cells - Remove cells with too few genes or high mitochondrial content.
  • Normalize before analysis - Account for sequencing depth differences.
  • Use highly variable genes - Focus analysis on informative genes.
❌ DON'T
  • Don't skip QC - Low-quality cells can dominate clustering.
  • Don't use raw counts for PCA - Always normalize and log-transform first.
  • Don't ignore batch effects - Use batch correction (e.g., sc.pp.harmony_integrate) when combining datasets.

Advanced Patterns

Trajectory Inference
import cellrank as cr

# Reconstruct developmental trajectories
sc.tl.paga(adata)
sc.pl.paga(adata)
adata.uns['iroot'] = np.flatnonzero(adata.obs['cell_type'] == 'stem')[0]
sc.tl.dpt(adata)
Differential Expression
# Find marker genes for each cluster
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
sc.pl.rank_genes_groups(adata, n_genes=20)

Scanpy has revolutionized single-cell biology, enabling researchers to map the cellular diversity of tissues and understand how cells differentiate and function.

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