scanpy

# Scanpy: Single-Cell Analysis ## Overview Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. ## When to Use This Skill This skill should be used when: - Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats) - Performing quality control on scRNA-seq datasets - Creating UMAP, t-SNE, or PCA visualizations - Identifying cell clusters and finding marker genes - Annotating cell types based on gene expression - Conducting trajectory inference or pseudotime analysis - Generating publication-quality single-cell plots ## Quick Start ### Basic Import and Setup ```python import scanpy as sc import pandas as pd import numpy as np # Configure settings sc.settings.verbosity = 3 sc.settings.set_figure_params(dpi=80, facecolor='white') sc.settings.figdir = './figures/' ``` ### Loading Data ```python # From 10X Genomics adata = sc.read_10x_mtx('path/to/data/') adata = sc.read_10x_h5('path/to/data.h5') # From h5ad (AnnData format) adata = sc.read_h5ad('path/to/data.h5ad') # From CSV adata = sc.read_csv('path/to/data.csv') ``` ### Understanding AnnData Structure The AnnData object is the core data structure in scanpy: ```python adata.X # Expression matrix (cells × genes) adata.obs # Cell metadata (DataFrame) adata.var ...