scanpy

name	scanpy
description	Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

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

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

# From 10X Genomics
adata = sc.read_10x_mtx('path/to/data/')
adata = sc.read_10x_h5()


adata = sc.read_h5ad()


adata = sc.read_csv()