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.