scanpy-scrna-seq
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 Seurat (R/Bioconductor) instead for scRNA-seq analysis in an existing R workflow or when Seurat-specific assay types are required
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