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.

When to Use This Skill

This skill should be used when:

• Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)

• 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

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/'

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')

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

Use the QC script for automated analysis:

python 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

Find marker genes for each cluster

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

Set high-quality defaults

sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5)) sc.settings.file_format_figs = 'pdf'

UMAP with custom styling

sc.pl.umap(adata, color='cell_type', palette='Set2', legend_loc='on data', legend_fontsize=12, legend_fontoutline=2, frameon=False, save='_publication.pdf')

Heatmap of marker genes

sc.pl.heatmap(adata, var_names=genes, groupby='cell_type', swap_axes=True, show_gene_labels=True, save='_markers.pdf')

Dot plot

sc.pl.dotplot(adata, var_names=genes, groupby='cell_type', save='_dotplot.pdf')

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')

Differential Expression Between Conditions

Compare treated vs control within cell types

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 over Louvain: More efficient and better results

• Try multiple clustering resolutions: Find optimal granularity

• Validate cell type annotations: Use multiple marker genes

• Use use_raw=True for gene expression plots: Shows original counts

• Check PCA variance ratio: Determine optimal number of PCs

• Save intermediate results: Long workflows can fail partway through

Bundled Resources

scripts/qc_analysis.py

Automated quality control script that calculates metrics, generates plots, and filters data:

python scripts/qc_analysis.py input.h5ad --output filtered.h5ad
--mt-threshold 5 --min-genes 200 --min-cells 3

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, Louvain)

• 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.

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.

Additional Resources

• Official scanpy documentation: https://scanpy.readthedocs.io/

• Scanpy tutorials: https://scanpy-tutorials.readthedocs.io/

• scverse ecosystem: https://scverse.org/ (related tools: squidpy, scvi-tools, cellrank)

• 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