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')