tools-only Scanpy API Quick Reference
Quick reference for commonly used scanpy functions organized by module.
Import Convention
import scanpy as sc
Reading and Writing Data (sc.read_*)
Reading Functions
sc.read_10x_h5(filename) # Read 10X HDF5 file
sc.read_10x_mtx(path) # Read 10X mtx directory
sc.read_h5ad(filename) # Read h5ad (AnnData) file
sc.read_csv(filename) # Read CSV file
sc.read_excel(filename) # Read Excel file
sc.read_loom(filename) # Read loom file
sc.read_text(filename) # Read text file
sc.read_visium(path) # Read Visium spatial data
Writing Functions
adata.write_h5ad(filename) # Write to h5ad format
adata.write_csvs(dirname) # Write to CSV files
adata.write_loom(filename) # Write to loom format
adata.write_zarr(filename) # Write to zarr format
Preprocessing (sc.pp.*)
Quality Control
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
Normalization and Transformation
sc.pp.normalize_total(adata, target_sum=1e4) # Normalize to target sum
sc.pp.log1p(adata) # Log(x + 1) transformation
sc.pp.sqrt(adata) # Square root transformation
Feature Selection
sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
sc.pp.highly_variable_genes(adata, flavor='seurat_v3', n_top_genes=2000)
Scaling and Regression
sc.pp.scale(adata, max_value=10) # Scale to unit variance
sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt']) # Regress out unwanted variation
Dimensionality Reduction (Preprocessing)
sc.pp.pca(adata, n_comps=50) # Principal component analysis
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40) # Compute neighborhood graph
Batch Correction
sc.pp.combat(adata, key='batch') # ComBat batch correction
Tools (sc.tl.*)
Dimensionality Reduction
sc.tl.pca(adata, svd_solver='arpack') # PCA
sc.tl.umap(adata) # UMAP embedding
sc.tl.tsne(adata) # t-SNE embedding
sc.tl.diffmap(adata) # Diffusion map
sc.tl.draw_graph(adata, layout='fa') # Force-directed graph
Clustering
sc.tl.leiden(adata, resolution=0.5) # Leiden clustering (recommended)
sc.tl.louvain(adata, resolution=0.5) # Louvain clustering
sc.tl.kmeans(adata, n_clusters=10) # K-means clustering
Marker Genes and Differential Expression
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')
sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test')
sc.tl.rank_genes_groups(adata, groupby='leiden', method='logreg')
# Get results as dataframe
sc.get.rank_genes_groups_df(adata, group='0')
Trajectory Inference
sc.tl.paga(adata, groups='leiden') # PAGA trajectory
sc.tl.dpt(adata) # Diffusion pseudotime
Gene Scoring
sc.tl.score_genes(adata, gene_list, score_name='score')
sc.tl.score_genes_cell_cycle(adata, s_genes, g2m_genes)
Embeddings and Projections
sc.tl.ingest(adata, adata_ref) # Map to reference
sc.tl.embedding_density(adata, basis='umap', groupby='leiden')
Plotting (sc.pl.*)
Basic Embeddings
sc.pl.umap(adata, color='leiden') # UMAP plot
sc.pl.tsne(adata, color='gene_name') # t-SNE plot
sc.pl.pca(adata, color='leiden') # PCA plot
sc.pl.diffmap(adata, color='leiden') # Diffusion map plot
Heatmaps and Dot Plots
sc.pl.heatmap(adata, var_names=genes, groupby='leiden')
sc.pl.dotplot(adata, var_names=genes, groupby='leiden')
sc.pl.matrixplot(adata, var_names=genes, groupby='leiden')
sc.pl.stacked_violin(adata, var_names=genes, groupby='leiden')
Violin and Scatter Plots
sc.pl.violin(adata, keys=['gene1', 'gene2'], groupby='leiden')
sc.pl.scatter(adata, x='gene1', y='gene2', color='leiden')
Marker Gene Visualization
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
sc.pl.rank_genes_groups_violin(adata, groups='0')
sc.pl.rank_genes_groups_heatmap(adata, n_genes=10)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5)
Trajectory Visualization
sc.pl.paga(adata, color='leiden') # PAGA graph
sc.pl.dpt_timeseries(adata) # DPT timeseries
QC Plots
sc.pl.highest_expr_genes(adata, n_top=20)
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'])
sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')
Advanced Plots
sc.pl.dendrogram(adata, groupby='leiden')
sc.pl.correlation_matrix(adata, groupby='leiden')
sc.pl.tracksplot(adata, var_names=genes, groupby='leiden')
Common Parameters
Color Parameters
color: Variable(s) to color by (gene name, obs column)use_raw: Use.rawattribute of adatapalette: Color palette to usevmin,vmax: Color scale limits
Layout Parameters
basis: Embedding basis ('umap', 'tsne', 'pca', etc.)legend_loc: Legend location ('on data', 'right margin', etc.)size: Point sizealpha: Point transparency
Saving Parameters
save: Filename to save plotshow: Whether to show plot
AnnData Structure
adata.X # Expression matrix (cells × genes)
adata.obs # Cell annotations (DataFrame)
adata.var # Gene annotations (DataFrame)
adata.uns # Unstructured annotations (dict)
adata.obsm # Multi-dimensional cell annotations (e.g., PCA, UMAP)
adata.varm # Multi-dimensional gene annotations
adata.layers # Additional data layers
adata.raw # Raw data backup
# Access
adata.obs_names # Cell barcodes
adata.var_names # Gene names
adata.shape # (n_cells, n_genes)
# Slicing
adata[cell_indices, gene_indices]
adata[:, adata.var_names.isin(gene_list)]
adata[adata.obs['leiden'] == '0', :]
Settings
sc.settings.verbosity = 3 # 0=error, 1=warning, 2=info, 3=hint
sc.settings.set_figure_params(dpi=80, facecolor='white')
sc.settings.autoshow = False # Don't show plots automatically
sc.settings.autosave = True # Autosave figures
sc.settings.figdir = './figures/' # Figure directory
sc.settings.cachedir = './cache/' # Cache directory
sc.settings.n_jobs = 8 # Number of parallel jobs
Useful Utilities
sc.logging.print_versions() # Print version information
sc.logging.print_memory_usage() # Print memory usage
adata.copy() # Create a copy of AnnData object
adata.concatenate([adata1, adata2]) # Concatenate AnnData objects