Scanpy Single-Cell RNA-seq Analysis

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