Scanpy Single-Cell RNA-seq Analysis

Overview

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data built on the AnnData format. This skill covers the end-to-end standard workflow: quality control, normalization, highly variable gene selection, dimensionality reduction, clustering, marker gene identification, and cell type annotation. It produces annotated datasets and publication-quality visualizations.

When to Use

• Analyzing single-cell RNA-seq count matrices (10X Genomics, h5ad, CSV, loom)
• Performing quality control filtering on scRNA-seq datasets (mitochondrial %, gene counts)
• Running dimensionality reduction: PCA, UMAP, t-SNE
• Identifying cell clusters via Leiden community detection
• Finding differentially expressed marker genes per cluster (Wilcoxon, t-test, logistic regression)
• Annotating cell types from known marker gene panels
• Conducting trajectory inference and pseudotime analysis (PAGA, diffusion pseudotime)
• Generating publication-quality single-cell plots (dot plots, heatmaps, stacked violins)
• Comparing gene expression across experimental conditions within cell types
• Use omics-plotting SKILL for generic figures (volcano/heatmap/bar/KM) from exported tables; single-cell views stay in scanpy sc.pl.*
• Use Seurat (R/Bioconductor) instead for scRNA-seq analysis in an existing R workflow or when Seurat-specific assay types are required

Prerequisites

• Python packages: scanpy>=1.10, leidenalg, igraph, anndata
• Data requirements: Gene expression count matrix (cells x genes). Common formats: 10X Cell Ranger output (filtered_feature_bc_matrix/), .h5ad, .h5, .csv
• Environment: Python 3.9+; 16GB+ RAM recommended for >50k cells

pip install "scanpy[leiden]" anndata

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: pct_counts_mt
    kind: required
    source: data
    ask: "Dying cells leak cytoplasmic RNA and read as high-mitochondrial - where should the cut sit for this tissue?"
    default: "20% (PBMC ~5%, solid tumour ~20%; propose from the observed distribution)"

  - id: D2
    param: batch_key
    kind: required
    source: data
    ask: "Which variable separates samples that were processed apart (donor, run, 10X lane), so its effect is not read as biology?"
    default: null
    skip_if: "all cells come from one sample and one processing run"

  - id: D3
    param: resolution
    kind: optional
    source: user
    ask: "How finely should cells be split - broad lineages, or subtypes within them?"
    default: "0.8 (0.1-0.3 broad lineages, 1.0-2.0 fine subtypes)"

  - id: D4
    param: n_top_genes
    kind: optional
    source: user
    ask: "How many variable genes should drive the embedding? More captures subtle states but adds noise."
    default: 2000

  - id: D5
    param: n_pcs
    kind: optional_conditional
    source: data
    depends_on: [D4]
    ask: "How many principal components carry real structure, by the variance-ratio elbow?"
    default: 40

  - id: D6
    param: min_genes, min_cells
    kind: optional_conditional
    source: data
    ask: "Where should empty droplets and undetected genes be cut off?"
    default: "cells with <200 genes, genes in <3 cells"

  - id: D6b
    param: annotationStrategy
    kind: required
    source: user
    ask: "Markers (A), a CellTypist model (B), an ensemble on your labelled reference (C), or markers and transfer compared (D)?"
    default: null

  - id: D6c
    param: markerPanel
    kind: optional
    source: literature
    ask: "Which canonical markers define the expected populations?"
    default: "agent-drafted panel for the tissue and state, listed in the plan"
    skip_if: "label transfer only"

  - id: D7
    param: method (rank_genes_groups)
    kind: optional
    source: user
    ask: "Which test should rank marker genes per cluster?"
    default: "wilcoxon"

  - id: D8
    param: target_sum, flavor
    kind: optional
    source: user
    ask: "Should the standard library-size normalization and Seurat v3 highly-variable-gene selection be used?"
    default: "target_sum 1e4, flavor seurat_v3"

  - id: D9
    param: random_state
    kind: never_ask
    source: data
    reason: "Fixes the draw for reproducibility; does not change what the data supports"
    default: 0

D5 hangs on D4 because the elbow is read off a PCA computed over the selected variable genes — a different gene count moves where it sits. D2 shapes the pipeline rather than a single call: naming a batch adds a correction stage (harmony-batch-correction or ComBat) before the neighbor graph. D6b and D6c are the same questions as cell-type-annotation D2 and D4; ask each once.

Workflow

Step 1: Setup and Data Loading

Configure scanpy settings and read the count matrix into an AnnData object. Ensure gene names are unique.

import scanpy as sc
import numpy as np
import pandas as pd

# Configure settings
sc.settings.verbosity = 3  # errors=0, warnings=1, info=2, hints=3
sc.settings.set_figure_params(dpi=80, facecolor="white")

# Load data — pick the reader matching your format
adata = sc.read_10x_mtx(
    "path/to/filtered_feature_bc_matrix/",
    var_names="gene_symbols",
    cache=True,
)
# Alternatives:
# adata = sc.read_h5ad("data.h5ad")
# adata = sc.read_10x_h5("data.h5")

adata.var_names_make_unique()
print(f"Loaded: {adata.n_obs} cells x {adata.n_vars} genes")
print(f"Sparsity: {1 - adata.X.nnz / (adata.n_obs * adata.n_vars):.1%}")

Step 2: Quality Control

Annotate mitochondrial/ribosomal genes, compute QC metrics, visualize distributions, and filter low-quality cells.

# Annotate gene groups
adata.var["mt"] = adata.var_names.str.startswith("MT-")    # human; use "mt-" for mouse
adata.var["ribo"] = adata.var_names.str.startswith(("RPS", "RPL"))

# Calculate QC metrics
sc.pp.calculate_qc_metrics(
    adata, qc_vars=["mt", "ribo"], percent_top=None, log1p=False, inplace=True
)

# Visualize QC distributions
sc.pl.violin(
    adata,
    ["n_genes_by_counts", "total_counts", "pct_counts_mt"],
    jitter=0.4,
    multi_panel=True,
)
sc.pl.scatter(adata, x="total_counts", y="pct_counts_mt")
sc.pl.scatter(adata, x="total_counts", y="n_genes_by_counts")

# Filter — adjust thresholds based on the QC plots above
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = adata[adata.obs.n_genes_by_counts < 5000, :]  # remove potential doublets
adata = adata[adata.obs.pct_counts_mt < 20, :]         # remove dying cells

print(f"After QC: {adata.n_obs} cells x {adata.n_vars} genes")

Step 3: Normalization and Feature Selection

Normalize library sizes, log-transform, and identify highly variable genes (HVGs). Store raw counts for later differential expression.

# Preserve raw counts in a layer
adata.layers["counts"] = adata.X.copy()

# Normalize to 10,000 counts per cell, then log-transform
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# Save normalized data as .raw for visualization
adata.raw = adata

# Identify highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor="seurat_v3", layer="counts")
sc.pl.highly_variable_genes(adata)

# Subset to HVGs
adata = adata[:, adata.var.highly_variable]
print(f"HVG subset: {adata.n_obs} cells x {adata.n_vars} genes")

Step 4: Scaling and Regression

Regress out confounders and scale gene expression values. This prepares data for PCA.

# Regress out unwanted sources of variation
sc.pp.regress_out(adata, ["total_counts", "pct_counts_mt"])

# Scale to unit variance, clip extreme values
sc.pp.scale(adata, max_value=10)

print(f"Scaled matrix: mean={adata.X.mean():.4f}, std={adata.X.std():.4f}")

Step 5: Dimensionality Reduction

Compute PCA, inspect the variance ratio elbow plot, then build a neighborhood graph and embed with UMAP.

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

# Determine n_pcs from elbow plot (typically 30-50)
n_pcs = 40

# Compute k-nearest neighbor graph
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=n_pcs)

# UMAP embedding
sc.tl.umap(adata)
sc.pl.umap(adata, color=["n_genes_by_counts", "total_counts", "pct_counts_mt"])

print(f"UMAP shape: {adata.obsm['X_umap'].shape}")

Step 6: Clustering

Run Leiden clustering at multiple resolutions and select the best granularity by visual inspection.

# Test multiple resolutions
for res in [0.3, 0.5, 0.8, 1.0, 1.2]:
    sc.tl.leiden(adata, resolution=res, key_added=f"leiden_res{res}", flavor="igraph", n_iterations=2)

# Compare resolutions side-by-side
sc.pl.umap(
    adata,
    color=["leiden_res0.3", "leiden_res0.5", "leiden_res0.8", "leiden_res1.0"],
    ncols=2,
    legend_loc="on data",
)

# Pick final resolution based on biological plausibility
adata.obs["leiden"] = adata.obs["leiden_res0.5"]
n_clusters = adata.obs["leiden"].nunique()
print(f"Selected resolution=0.5: {n_clusters} clusters")

Step 7: Marker Gene Identification

Find differentially expressed genes per cluster using Wilcoxon rank-sum test on raw counts.

# Rank genes per cluster
sc.tl.rank_genes_groups(adata, groupby="leiden", method="wilcoxon", use_raw=True)

# Visualization
sc.pl.rank_genes_groups(adata, n_genes=20, sharey=False)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5)
sc.pl.rank_genes_groups_heatmap(adata, n_genes=10, use_raw=True, show_gene_labels=True)

# Extract as DataFrame
markers_df = sc.get.rank_genes_groups_df(adata, group=None)
top_markers = markers_df[markers_df["pvals_adj"] < 0.05].groupby("group").head(10)
print(f"Significant markers: {len(top_markers)} genes across {top_markers['group'].nunique()} clusters")
print(top_markers[["group", "names", "logfoldchanges", "pvals_adj"]].head(15))

Step 8: Cell Type Annotation and Export

Assign cell types based on canonical marker genes, visualize, and save all results.

# Define canonical markers (example: human PBMC)
marker_genes = {
    "CD4 T cells":      ["CD3D", "CD3E", "IL7R"],
    "CD8 T cells":      ["CD8A", "CD8B", "GZMK"],
    "B cells":          ["MS4A1", "CD79A", "CD79B"],
    "NK cells":         ["NKG7", "GNLY", "KLRD1"],
    "CD14+ Monocytes":  ["CD14", "LYZ", "S100A9"],
    "FCGR3A+ Monocytes":["FCGR3A", "MS4A7"],
    "Dendritic cells":  ["FCER1A", "CST3"],
    "Platelets":        ["PPBP", "PF4"],
}

# Dot plot — rows = clusters, columns = markers
sc.pl.dotplot(adata, var_names=marker_genes, groupby="leiden", use_raw=True)

# Map clusters → cell types (adjust based on your dot plot)
cluster_to_celltype = {
    "0": "CD4 T cells",
    "1": "CD14+ Monocytes",
    "2": "B cells",
    "3": "CD8 T cells",
    "4": "NK cells",
    "5": "FCGR3A+ Monocytes",
    "6": "Dendritic cells",
    "7": "Platelets",
}
adata.obs["cell_type"] = adata.obs["leiden"].map(cluster_to_celltype).fillna("Unknown")
sc.pl.umap(adata, color="cell_type", legend_loc="on data", frameon=False)

# Save results
adata.write("results/annotated_data.h5ad", compression="gzip")
adata.obs.to_csv("results/cell_metadata.csv")
markers_df.to_csv("results/marker_genes.csv", index=False)
print(f"Saved: {adata.n_obs} cells with {adata.obs['cell_type'].nunique()} cell types")

Key Parameters

Common Recipes

Recipe: Batch Correction with ComBat

When to use: multiple samples/batches with visible batch effects on the UMAP.

# Requires 'batch' column in adata.obs
sc.pp.combat(adata, key="batch")
# Re-run PCA, neighbors, UMAP, clustering after correction
sc.tl.pca(adata, svd_solver="arpack")
sc.pp.neighbors(adata, n_pcs=40)
sc.tl.umap(adata)
sc.pl.umap(adata, color=["batch", "leiden"])

Recipe: Trajectory Inference with PAGA + Diffusion Pseudotime

When to use: cells follow a developmental continuum rather than discrete clusters.

# PAGA graph abstraction
sc.tl.paga(adata, groups="leiden")
sc.pl.paga(adata, color="leiden", threshold=0.03)

# Reinitialize UMAP with PAGA layout
sc.tl.umap(adata, init_pos="paga")

# Diffusion pseudotime — set root cell
adata.uns["iroot"] = np.flatnonzero(adata.obs["leiden"] == "0")[0]
sc.tl.diffmap(adata)
sc.tl.dpt(adata)
sc.pl.umap(adata, color=["dpt_pseudotime", "leiden"])

Recipe: Publication-Quality Figures

When to use: generating figures for manuscripts or presentations.

sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5))

# UMAP with custom palette
sc.pl.umap(
    adata, color="cell_type",
    palette="Set2",
    legend_loc="on data",
    legend_fontsize=10,
    legend_fontoutline=2,
    title="",
    save="_celltype_publication.pdf",
)

# Stacked violin plot of top markers
flat_markers = [g for genes in marker_genes.values() for g in genes[:2]]
sc.pl.stacked_violin(
    adata, var_names=flat_markers, groupby="cell_type",
    use_raw=True, swap_axes=True,
    save="_markers_violin.pdf",
)

Recipe: Differential Expression Between Conditions

When to use: comparing treated vs control within a specific cell type.

# Subset to cell type of interest
adata_t = adata[adata.obs["cell_type"] == "CD4 T cells"].copy()

# DE between conditions
sc.tl.rank_genes_groups(adata_t, groupby="condition", groups=["treated"], reference="control", method="wilcoxon", use_raw=True)
sc.pl.rank_genes_groups_volcano(adata_t, groups=["treated"])

de_results = sc.get.rank_genes_groups_df(adata_t, group="treated")
sig_genes = de_results[de_results["pvals_adj"] < 0.05]
print(f"Significant DE genes: {len(sig_genes)}")

Expected Outputs

• results/annotated_data.h5ad — Full AnnData with embeddings (PCA, UMAP), cluster labels, cell type annotations, and raw counts layer
• results/cell_metadata.csv — Per-cell table: barcode, cluster, cell type, QC metrics (n_genes, total_counts, pct_mt)
• results/marker_genes.csv — DE genes per cluster: gene name, log fold change, adjusted p-value
• QC figures: violin plots (n_genes, total_counts, pct_mt), scatter plots
• Embedding figures: UMAP colored by cluster, cell type, QC metrics, pseudotime
• Marker figures: dot plot, heatmap, stacked violin of canonical markers

Troubleshooting

Bundled Resources

This skill includes reference files for deeper lookup. Read these on demand when the main SKILL.md needs more detail.

references/api_reference.md

Quick-lookup table of all scanpy functions organized by module (sc.pp., sc.tl., sc.pl.), with full signatures, common parameters, and AnnData structure reference. Use when*: you need the exact function name or parameter for a specific operation.

references/plotting_guide.md

Comprehensive visualization guide: QC plots, UMAP styling, marker gene plots (dot plot, heatmap, stacked violin), trajectory plots, multi-panel figures, publication customization, and color palette recommendations. Use when: creating figures for publications or presentations.

references/standard_workflow.md

Detailed step-by-step reference for each pipeline stage with decision points, parameter rationale, and alternative approaches (MAD-based filtering, scran normalization, automated annotation). Use when: performing a full analysis from scratch and needing deeper guidance than the Workflow section above.

References

• Scanpy documentation — Official API reference and tutorials (BSD-3)
• Scanpy PBMC3k tutorial — Canonical walkthrough (BSD-3)
• Galaxy Training: Clustering 3K PBMCs with Scanpy — Step-by-step tutorial (CC-BY)
• Luecken & Theis (2019) — "Current best practices in single-cell RNA-seq analysis: a tutorial", Mol Syst Biol
• Heumos et al. (2023) — "Best practices for single-cell analysis across modalities", Nat Rev Genet
• scverse ecosystem — Related tools: anndata, scvi-tools, squidpy, cellrank