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sc-preprocessing

Automate single-cell RNA sequencing preprocessing with Scanpy and Seurat workflows.

155|26|Updated Mar 14, 2026
Search Tags:#preprocessing#clustering#scanpy#seurat#rna-seq#qc#single cell
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-preprocessing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sc-preprocessing
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/sc-preprocessing
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-preprocessing

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the complex and time-consuming initial steps of single-cell RNA sequencing data analysis, ensuring consistency and reproducibility.

Core Features & Use Cases

  • Automated QC & Filtering: Applies standard quality control metrics to filter out low-quality cells and genes.
  • Normalization & Scaling: Performs library size normalization and log transformation.
  • Dimensionality Reduction & Clustering: Computes PCA, UMAP, and Leiden clustering for downstream analysis.
  • Use Case: Quickly process raw 10x Genomics data into a ready-to-analyze AnnData object, saving researchers from writing extensive boilerplate code.

Quick Start

Run the single-cell preprocessing skill on the provided PBMC3k demo data.

Frequently Asked Questions about sc-preprocessing

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

FAQPage Schema
How do I automate single-cell RNA sequencing preprocessing with scanpy?▼

Automated single-cell RNA sequencing preprocessing executes quality control, normalization, highly variable gene selection, PCA, UMAP, and Leiden clustering to generate a ready-to-analyze AnnData object.

What are the standard quality control steps for scRNA-seq data filtering?▼

Standard scRNA-seq quality control applies metrics to filter out low-quality cells and genes, followed by library size normalization and log transformation to prepare the data for clustering.

Does this single-cell preprocessing workflow support Seurat in R?▼

The single-cell preprocessing workflow supports both Scanpy in Python and Seurat in R, requiring Python 3.11+ with scanpy, anndata, numpy, pandas, and matplotlib libraries.

Can I process raw 10x Genomics data directly into an AnnData object?▼

Processing raw 10x Genomics data into a ready-to-analyze AnnData object is fully supported, saving researchers from writing extensive boilerplate code for dimensionality reduction and clustering.

What is the best way to run Leiden clustering on normalized single-cell data?▼

Running Leiden clustering on normalized single-cell data is best handled by automating the workflow from library size normalization through PCA and UMAP computation to generate clustering results.

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