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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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| name | scanpy |
| description | 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. |
| license | SD-3-Clause license |
| metadata | {"skill-author":"K-Dense Inc."} |
| risk | unknown |
| source | community |
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.
This skill should be used when:
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/'
# 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')
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
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
# 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)
# 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)
# 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}')
# 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')
# 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')
# 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')
# 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.
# 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')
# 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'])
# 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')
# ComBat batch correction
sc.pp.combat(adata, key='batch')
# Alternative: use Harmony or scVI (separate packages)
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%)target_sum: Target counts per cell (default 1e4)n_top_genes: Number of HVGs (typically 2000-3000)min_mean, max_mean, min_disp: HVG selection parametersn_pcs: Number of principal components (check variance ratio plot)n_neighbors: Number of neighbors (typically 10-30)resolution: Clustering granularity (0.4-1.2, higher = more clusters)adata.raw = adata before filtering genesuse_raw=True for gene expression plots: Shows original countsAutomated 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
Complete step-by-step workflow with detailed explanations and code examples for:
Read this reference when performing a complete analysis from scratch.
Quick reference guide for scanpy functions organized by module:
sc.read_*, adata.write_*)sc.pp.*)sc.tl.*)sc.pl.*)Use this for quick lookup of function signatures and common parameters.
Comprehensive visualization guide including:
Consult this when creating publication-ready figures.
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.
assets/analysis_template.py as a starting pointscripts/qc_analysis.py for initial filtering