Scanpy

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.

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 arrays (experimental):

uv pip install "scanpy[leiden]" dask

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/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
• 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. 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/.

Script	Purpose	Typical call
run_pipeline.py	Full workflow in one command: load → QC → normalize → HVG → PCA → (batch) → UMAP → Leiden → markers	python scripts/run_pipeline.py raw.h5ad -o processed.h5ad
inspect_data.py	Summarize an unknown dataset (shape, obs/var, layers, what's already computed, raw vs normalized)	python scripts/inspect_data.py data.h5ad
convert.py	Load any format (10x dir/.h5, csv, loom, mtx) and write .h5ad	python scripts/convert.py 10x_dir/ -o data.h5ad
qc_analysis.py	QC metrics, before/after plots, filtering, optional Scrublet doublets	python scripts/qc_analysis.py raw.h5ad -o qc.h5ad --scrublet
preprocess.py	Normalize, log1p, HVG, optional scale/regress (keeps counts layer + raw)	python scripts/preprocess.py qc.h5ad -o norm.h5ad
reduce_dimensions.py	PCA + variance plot, neighbors, UMAP, optional t-SNE	python scripts/reduce_dimensions.py norm.h5ad -o red.h5ad
batch_correct.py	Integration: harmony / bbknn / combat	python scripts/batch_correct.py red.h5ad -o int.h5ad --method harmony --batch-key sample
cluster.py	Leiden (or louvain) at one or many resolutions	python scripts/cluster.py red.h5ad -o clu.h5ad --resolution 0.3 0.6 1.0
find_markers.py	rank_genes_groups + per-group CSVs + marker plots	python scripts/find_markers.py clu.h5ad --groupby leiden -o clu.h5ad
annotate.py	Map clusters → cell types from JSON/CSV; optional marker reference dotplot	python scripts/annotate.py clu.h5ad -o ann.h5ad --mapping map.json
score_genes.py	Score gene signatures (JSON) and/or cell-cycle phase	python scripts/score_genes.py ann.h5ad -o scored.h5ad --gene-sets sigs.json
pseudobulk.py	Aggregate counts by sample × cell type → matrix for pydeseq2	python scripts/pseudobulk.py ann.h5ad --by sample cell_type --out-prefix pb
subset.py	Subset by obs values or gene list (optionally clear stale embeddings)	python scripts/subset.py ann.h5ad -o tcells.h5ad --obs cell_type --keep "T cells"
plot.py	Generate umap/tsne/pca/violin/dotplot/heatmap/etc. from a processed object	python scripts/plot.py ann.h5ad --kind dotplot --genes CD3D CD14 --groupby cell_type

One-shot end-to-end run

# Counts → clustered, marker-annotated object + figures + marker CSVs
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad \
    --resolution 0.5 --n-top-genes 2000 --scrublet
# With multi-sample integration:
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad --batch-key sample --batch-method harmony
# Reproducible parameters via JSON (keys mirror flag names with underscores):
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad --config params.json

Step-by-step chain (when you need to inspect/iterate between stages)

python scripts/qc_analysis.py        raw.h5ad  -o qc.h5ad   --scrublet
python scripts/preprocess.py         qc.h5ad   -o norm.h5ad --n-top-genes 2000
python scripts/reduce_dimensions.py  norm.h5ad -o red.h5ad  --n-pcs 40
python scripts/cluster.py            red.h5ad  -o clu.h5ad  --resolution 0.3 0.5 0.8
python scripts/find_markers.py       clu.h5ad  -o clu.h5ad  --groupby leiden --use-raw
# inspect results/markers/*.csv, decide labels, write a mapping JSON, then:
python scripts/annotate.py           clu.h5ad  -o ann.h5ad  --mapping celltypes.json

The sections below document the underlying scanpy calls each script performs — read them when customizing beyond the script flags.

Quick Start

Basic Import and Setup

import scanpy as sc
import pandas as pd
import numpy as np

# Configure settings
sc.settings.verbosity = 3
sc.settings.set_figure_params(dpi=80, facecolor='white')
sc.settings.figdir = './figures/'
sc.settings.autosave = True  # Preferred over per-plot save= (deprecated in scanpy 1.12)

Loading Data

# From 10X Genomics
adata = sc.read_10x_mtx('path/to/data/')
adata = sc.read_10x_h5('path/to/data.h5')

# From h5ad (AnnData format)
adata = sc.read_h5ad('path/to/data.h5ad')

# From CSV
adata = sc.read_csv('path/to/data.csv')

For R-native files, do not try to parse Seurat .rds directly in Python. Convert first:

# See references/r_interop.md for installing R and conversion packages.
Rscript convert_rds_to_h5ad.R input.rds output.h5ad

adata = sc.read_h5ad('output.h5ad')

Understanding AnnData Structure

The AnnData object is the core data structure in scanpy:

adata.X          # Expression matrix (cells × genes)
adata.obs        # Cell metadata (DataFrame)
adata.var        # Gene metadata (DataFrame)
adata.uns        # Unstructured annotations (dict)
adata.obsm       # Multi-dimensional cell data (PCA, UMAP)
adata.raw        # Raw data backup

# Access cell and gene names
adata.obs_names  # Cell barcodes
adata.var_names  # Gene names

Standard Analysis Workflow

The seven steps, with code and the parameters that matter at each, are in references/analysis_workflow.md:

1. Quality control — filter cells and genes; inspect mitochondrial fraction and counts before choosing thresholds rather than copying defaults.
2. Normalization and preprocessing — normalize, log-transform, select highly variable genes, and keep .raw for later plotting.
3. Dimensionality reduction — PCA, then the neighbour graph, then UMAP.
4. Clustering — Leiden at a resolution chosen for the question, not the default.
5. Marker gene identification — ranked genes per cluster.
6. Cell type annotation — mapping clusters to types from markers.
7. Save results — writing the annotated AnnData.

Common follow-on tasks — publication plots, trajectory inference, pseudobulk differential expression between conditions, gene set scoring, and batch correction — are in the same file. See also references/standard_workflow.md and references/plotting_guide.md.

Key Parameters to Adjust

Quality Control

• min_genes: Minimum genes per cell (typically 200-500)
• min_cells: Minimum cells per gene (typically 3-10)
• pct_counts_mt: Mitochondrial threshold (typically 5-20%)

Normalization

• target_sum: Target counts per cell (default 1e4)

Feature Selection

• n_top_genes: Number of HVGs (typically 2000-3000)
• min_mean, max_mean, min_disp: HVG selection parameters

Dimensionality Reduction

• n_pcs: Number of principal components (check variance ratio plot)
• n_neighbors: Number of neighbors (typically 10-30)

Clustering

• resolution: Clustering granularity (0.4-1.2, higher = more clusters)

Common Pitfalls and Best Practices

1. Always save raw counts: adata.raw = adata before filtering genes
2. Check QC plots carefully: Adjust thresholds based on dataset quality
3. Use Leiden clustering: sc.tl.louvain is deprecated in scanpy 1.12
4. Try multiple clustering resolutions: Find optimal granularity
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
• qc_analysis.py, preprocess.py, reduce_dimensions.py, batch_correct.py, cluster.py — pipeline steps
• find_markers.py, annotate.py, score_genes.py, pseudobulk.py — markers, annotation, scoring, DE prep
• subset.py, plot.py — subset by metadata/genes; generate any standard plot

Default to these scripts before writing scanpy code from scratch.

references/standard_workflow.md

Complete step-by-step workflow with detailed explanations and code examples for:

• Data loading and setup
• Quality control with visualization
• Normalization and scaling
• Feature selection
• Dimensionality reduction (PCA, UMAP, t-SNE)
• Clustering (Leiden)
• Doublet detection (scrublet) and pseudobulk aggregation
• Marker gene identification
• Cell type annotation
• Trajectory inference
• Differential expression

Read this reference when performing a complete analysis from scratch.

references/api_reference.md

Quick reference guide for scanpy functions organized by module:

• Reading/writing data (sc.read_*, adata.write_*)
• Preprocessing (sc.pp.*)
• Tools (sc.tl.*)
• Plotting (sc.pl.*)
• AnnData structure and manipulation
• Settings and utilities

Use this for quick lookup of function signatures and common parameters.

references/plotting_guide.md

Comprehensive visualization guide including:

• Quality control plots
• Dimensionality reduction visualizations
• Clustering visualizations
• Marker gene plots (heatmaps, dot plots, violin plots)
• Trajectory and pseudotime plots
• Publication-quality customization
• Multi-panel figures
• Color palettes and styling

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:

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

Additional Resources

• Official scanpy documentation: https://scanpy.scverse.org/en/stable/
• Scanpy tutorials: https://scanpy.scverse.org/en/stable/tutorials/index.html
• Release notes: https://scanpy.scverse.org/en/stable/release-notes/index.html
• scverse ecosystem: https://scverse.org/ (related tools: squidpy, scvi-tools, cellrank)
• R interoperability: https://www.bioconductor.org/packages/release/bioc/html/zellkonverter.html and https://mojaveazure.github.io/seurat-disk/
• Best practices: Luecken & Theis (2019) "Current best practices in single-cell RNA-seq"

Tips for Effective Analysis

1. Start with the template: Use assets/analysis_template.py as a starting point
2. Run QC script first: Use scripts/qc_analysis.py for initial filtering
3. Consult references as needed: Load workflow and API references into context
4. Iterate on clustering: Try multiple resolutions and visualization methods
5. Validate biologically: Check marker genes match expected cell types
6. Document parameters: Record QC thresholds and analysis settings
7. Save checkpoints: Write intermediate results at key steps