scanpy-singlecell

Research

Single-cell RNA-seq analysis with scanpy and anndata. Use when: (1) scRNA-seq preprocessing and QC, (2) clustering and cell type annotation, (3) differential expression analysis, (4) trajectory and pseudotime analysis, (5) UMAP/tSNE visualization. NOT for: bulk RNA-seq differential expression (use DESeq2/edgeR), protein structure analysis (use pdb-structure), or imaging data.

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Scanpy Single-Cell Analysis

Single-cell RNA-seq analysis using scanpy and anndata for preprocessing, clustering, differential expression, and visualization.

When to Use

scRNA-seq data preprocessing and quality control
Cell clustering and cell type annotation
Differential expression between cell groups
Trajectory inference and pseudotime analysis
Dimensionality reduction visualization (UMAP, tSNE)
Integration of multiple scRNA-seq datasets

When NOT to Use

Bulk RNA-seq differential expression (use DESeq2 or edgeR)
Protein structure prediction or analysis (use pdb-structure)
Imaging or spatial transcriptomics without companion tools
General-purpose statistics (use scipy-analysis)

Reading Data and Preprocessing

import scanpy as sc

# Read data: h5ad, 10x mtx, or 10x h5
adata = sc.read_h5ad('dataset.h5ad')
adata = sc.read_10x_mtx('filtered_feature_bc_matrix/', var_names='gene_symbols')

# QC filtering
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, inplace=True)
adata = adata[adata.obs.pct_counts_mt < 20, :]

# Normalize, log-transform, select HVGs
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata  # preserve full gene set for DE
sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
adata = adata[:, adata.var.highly_variable]
sc.pp.scale(adata, max_value=10)

Dimensionality Reduction and Clustering

# PCA
sc.tl.pca(adata, svd_solver='arpack', n_comps=50)
sc.pl.pca_variance_ratio(adata, n_pcs=50, log=True)

# Neighborhood graph and clustering
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=40)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

# Alternative: Louvain clustering
sc.tl.louvain(adata, resolution=0.8)

# tSNE (alternative to UMAP)
sc.tl.tsne(adata, n_pcs=40)

Differential Expression

# Rank genes per cluster (Wilcoxon is recommended)
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon', use_raw=True)
sc.pl.rank_genes_groups(adata, n_genes=20, sharey=False)

# Extract DE results as DataFrame
result = adata.uns['rank_genes_groups']
de_df = sc.get.rank_genes_groups_df(adata, group='0')
de_df_filtered = de_df[(de_df['pvals_adj'] < 0.05) & (de_df['logfoldchanges'].abs() > 1)]

# Compare specific groups
sc.tl.rank_genes_groups(adata, groupby='leiden', groups=['0'], reference='1',
                        method='wilcoxon', use_raw=True)

Visualization

# UMAP colored by cluster
sc.pl.umap(adata, color=['leiden'], frameon=False, save='_clusters.pdf')

# UMAP colored by gene expression
sc.pl.umap(adata, color=['CST3', 'NKG7', 'MS4A1'], frameon=False)

# Dot plot for marker genes across clusters
marker_genes = ['CD3D', 'CD79A', 'CST3', 'NKG7', 'PPBP']
sc.pl.dotplot(adata, marker_genes, groupby='leiden', save='_markers.pdf')

# Stacked violin plot
sc.pl.stacked_violin(adata, marker_genes, groupby='leiden', rotation=90)

# Heatmap of top DE genes
sc.pl.rank_genes_groups_heatmap(adata, n_genes=5, groupby='leiden', show_gene_labels=True)

# Matrix plot
sc.pl.matrixplot(adata, marker_genes, groupby='leiden', standard_scale='var')

Trajectory Analysis

# Diffusion map and pseudotime
sc.tl.diffmap(adata)
sc.tl.dpt(adata, n_dcs=10)

# PAGA (partition-based graph abstraction)
sc.tl.paga(adata, groups='leiden')
sc.pl.paga(adata, plot=True, threshold=0.03)
sc.tl.umap(adata, init_pos='paga')

Best Practices

Always start with QC: filter low-quality cells and doublets before analysis.
Use adata.raw to preserve full gene set for DE testing after subsetting HVGs.
Prefer Leiden over Louvain clustering (better modularity optimization).
Use Wilcoxon rank-sum for DE; it is robust and non-parametric.
Save intermediate results with adata.write('checkpoint.h5ad').
Set sc.settings.figdir and use save= parameter for reproducible figures.
Use sc.logging.print_versions() to record environment for reproducibility.
Adjust resolution parameter in Leiden to control cluster granularity.