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single-cell-scrna-seq-analysis-scanpy

Process single-cell transcriptomes into AnnData objects with Scanpy.

1.1k|132|Updated Apr 13, 2023
Search Tags:#visualization#clustering#anndata#scanpy#cell-annotation#scrna-seq
One-click install
npx skills add https://github.com/PharMolix/OpenBioMed --skill single-cell-scrna-seq-analysis-scanpy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-scrna-seq-analysis-scanpy
Source: https://github.com/PharMolix/OpenBioMed/tree/main/skills/single-cell-scrna-seq-analysis-scanpy
Command: npx skills add https://github.com/PharMolix/OpenBioMed --skill single-cell-scrna-seq-analysis-scanpy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, pandas, numpy, matplotlib, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Complete end-to-end single-cell RNA-seq analysis workflow using Scanpy and AnnData, enabling researchers to load diverse data formats, perform QC, normalize, reduce dimensionality, cluster cells, and annotate cell types.

Core Features & Use Cases

  • Load data from 10X, h5ad, or CSV into an AnnData object.
  • QC metrics calculation, filtering, normalization, HVG selection, and visualization.
  • Dimensionality reduction (PCA, UMAP/t-SNE) and Leiden clustering across resolutions.
  • Marker gene identification and automated/manual cell-type annotation.
  • Generate publication-ready plots and trajectories for downstream interpretation.

Quick Start

Load your dataset (10X, h5ad, or CSV) and run the full Scanpy pipeline to obtain QC metrics, clustering, and annotated cell types.

Frequently Asked Questions about single-cell-scrna-seq-analysis-scanpy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform end-to-end scRNA-seq analysis from raw 10X or CSV files?▼

This workflow loads 10X, h5ad, or CSV files into an AnnData object, then executes QC, normalization, dimensionality reduction, and Leiden clustering to produce fully processed data and publication-ready plots.

What is the best way to identify cell types and marker genes in single-cell transcriptomes?▼

To identify cell types in single-cell transcriptomes, the workflow performs Leiden clustering across multiple resolutions, identifies marker genes, and generates automated or manual cell-type annotations within the processed AnnData object.

Can I use Scanpy to generate UMAP and t-SNE visualizations for my AnnData object?▼

Yes, Scanpy performs dimensionality reduction using PCA and generates UMAP or t-SNE visualizations from the AnnData object, producing publication-ready plots for downstream interpretation of your scRNA-seq data.

Do I need Python 3.9 and scikit-learn to run a Scanpy clustering workflow?▼

Yes, running this Scanpy clustering workflow requires Python 3.9+ along with libraries including scanpy, anndata, pandas, numpy, matplotlib, and scikit-learn to process the single-cell data and output cluster labels.

Does this scRNA-seq pipeline support calculating QC metrics and filtering cells?▼

Yes, the scRNA-seq pipeline calculates QC metrics and applies filtering to the AnnData object, selecting highly variable genes and performing normalization prior to dimensionality reduction and cluster discovery.

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