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/.

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

1. Quality Control

Identify and filter low-quality cells and genes:

# Identify mitochondrial genes
adata.var['mt'] = adata.var_names.str.startswith('MT-')

# Calculate QC metrics
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)

# Visualize QC metrics
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
             jitter=0.4, multi_panel=True)

# Filter cells and genes
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = adata[adata.obs.pct_counts_mt < 5, :]  # Remove high MT% cells

Doublet detection (optional, on raw counts before normalization):

sc.pp.scrublet(adata)  # Core API since scanpy 1.10 (was scanpy.external.pp)
adata = adata[~adata.obs['predicted_doublet'], :].copy()

Use the QC script for automated analysis (run from the skill directory or pass the full path):

python skills/scanpy/scripts/qc_analysis.py input_file.h5ad --output filtered.h5ad

2. Normalization and Preprocessing

# Normalize to 10,000 counts per cell
sc.pp.normalize_total(adata, target_sum=1e4)

# Log-transform
sc.pp.log1p(adata)

# Save raw counts for later
adata.raw = adata

# Identify highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pl.highly_variable_genes(adata)

# Subset to highly variable genes
adata = adata[:, adata.var.highly_variable]

# Regress out unwanted variation
sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])

# Scale data
sc.pp.scale(adata, max_value=10)

3. Dimensionality Reduction

# PCA
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_variance_ratio(adata, log=True)  # Check elbow plot

# Compute neighborhood graph
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)

# UMAP for visualization
sc.tl.umap(adata)
sc.pl.umap(adata, color='leiden')

# Alternative: t-SNE
sc.tl.tsne(adata)

4. Clustering

# Leiden clustering (recommended)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color='leiden', legend_loc='on data')

# Try multiple resolutions to find optimal granularity
for res in [0.3, 0.5, 0.8, 1.0]:
    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_{res}')

5. Marker Gene Identification

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.

# Find marker genes for each cluster (exploratory)
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')

# Visualize results
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
sc.pl.rank_genes_groups_heatmap(adata, n_genes=10)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5)

# Get results as DataFrame
markers = sc.get.rank_genes_groups_df(adata, group='0')

6. Cell Type Annotation

# Define marker genes for known cell types
marker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']

# Visualize markers
sc.pl.umap(adata, color=marker_genes, use_raw=True)
sc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden')

# Manual annotation
cluster_to_celltype = {
    '0': 'CD4 T cells',
    '1': 'CD14+ Monocytes',
    '2': 'B cells',
    '3': 'CD8 T cells',
}
adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_to_celltype)

# Visualize annotated types
sc.pl.umap(adata, color='cell_type', legend_loc='on data')

7. Save Results

# Save processed data
adata.write('results/processed_data.h5ad')

# Export metadata
adata.obs.to_csv('results/cell_metadata.csv')
adata.var.to_csv('results/gene_metadata.csv')

Common Tasks

Creating Publication-Quality Plots

Prefer sc.settings.autosave and sc.settings.figdir for saving figures. The per-plot save= parameter is deprecated in scanpy 1.12.

# Set high-quality defaults
sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5))
sc.settings.file_format_figs = 'pdf'
sc.settings.figdir = './figures/'
sc.settings.autosave = True

# UMAP with custom styling (saved as figures/umap.pdf via autosave)
sc.pl.umap(adata, color='cell_type',
           palette='Set2',
           legend_loc='on data',
           legend_fontsize=12,
           legend_fontoutline=2,
           frameon=False)

# Heatmap of marker genes
sc.pl.heatmap(adata, var_names=genes, groupby='cell_type',
              swap_axes=True, show_gene_labels=True)

# Dot plot
sc.pl.dotplot(adata, var_names=genes, groupby='cell_type')

Refer to references/plotting_guide.md for comprehensive visualization examples.

Trajectory Inference

# PAGA (Partition-based graph abstraction)
sc.tl.paga(adata, groups='leiden')
sc.pl.paga(adata, color='leiden')

# Diffusion pseudotime
adata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == '0')[0]
sc.tl.dpt(adata)
sc.pl.umap(adata, color='dpt_pseudotime')

Pseudobulk and Differential Expression Between Conditions

Pseudobulk by sample and cell type, then run proper DE (e.g., pydeseq2) rather than per-cell rank_genes_groups:

# Aggregate counts by sample and cell type (dask-compatible in scanpy 1.12)
pb = sc.get.aggregate(
    adata,
    by=['sample', 'cell_type'],
    func='sum',
    layer='counts',  # Use raw counts layer if available
)
# Downstream: export pb and use pydeseq2 for condition comparisons

For quick exploratory comparisons within a cluster, rank_genes_groups is acceptable but interpret p-values cautiously:

adata_subset = adata[adata.obs['cell_type'] == 'T cells']
sc.tl.rank_genes_groups(adata_subset, groupby='condition',
                         groups=['treated'], reference='control')
sc.pl.rank_genes_groups(adata_subset, groups=['treated'])

Gene Set Scoring

# Score cells for gene set expression
gene_set = ['CD3D', 'CD3E', 'CD3G']
sc.tl.score_genes(adata, gene_set, score_name='T_cell_score')
sc.pl.umap(adata, color='T_cell_score')

Batch Correction

# ComBat batch correction
sc.pp.combat(adata, key='batch')

# Alternative: use Harmony or scVI (separate packages)

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

Always save raw counts: adata.raw = adata before filtering genes

Check QC plots carefully: Adjust thresholds based on dataset quality

Use Leiden clustering: sc.tl.louvain is deprecated in scanpy 1.12

Try multiple clustering resolutions: Find optimal granularity

Validate cell type annotations: Use multiple marker genes

Use use_raw=True for gene expression plots: Shows normalized counts from .raw

Check PCA variance ratio: Determine optimal number of PCs

Save intermediate results: Long workflows can fail partway through

Pseudobulk for DE: Do not treat rank_genes_groups p-values as rigorous DE between conditions

Save plots via settings: Use sc.settings.autosave instead of deprecated save= on plot functions

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

Start with the template: Use assets/analysis_template.py as a starting point

Run QC script first: Use scripts/qc_analysis.py for initial filtering

Consult references as needed: Load workflow and API references into context

Iterate on clustering: Try multiple resolutions and visualization methods

Validate biologically: Check marker genes match expected cell types

Document parameters: Record QC thresholds and analysis settings

Save checkpoints: Write intermediate results at key steps