scanpy
Scalable toolkit for analyzing single-cell gene expression data. Built on top of Anndata, focusing on clustering, trajectory inference, and visualization.
What this skill does
# 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.
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