scanpy

# 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 ```python import scanpy as sc import pandas as pd import numpy as np ``` ### Basic Patterns ```python # 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 ```python 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 ```python # 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.