skills.rest
zongtingwei
zongtingweiCOMMUNITY
Explore all skills by this author →
GitHub

scrna-preprocessing-clustering

Preprocess single-cell RNA-seq data into analysis-ready AnnData objects.

25|5|Updated Mar 22, 2026
Search Tags:#normalization#quality-control#clustering#anndata#scanpy#scrna-seq
One-click install
npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill scrna-preprocessing-clustering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scrna-preprocessing-clustering
Source: https://github.com/zongtingwei/Bioclaw_Skills_Hub/tree/main/skills/single-cell-and-spatial/scrna-preprocessing-clustering
Command: npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill scrna-preprocessing-clustering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automatically preprocess scRNA-seq data to produce analysis-ready AnnData objects by performing quality control, normalization, variable gene selection, dimensionality reduction, neighborhood graph construction, and clustering.

Core Features & Use Cases

  • QC metrics computation and filtering to identify high-quality cells
  • Normalization and HVG selection for robust downstream analysis
  • Dimensionality reduction (PCA/UMAP) and neighborhood graph construction
  • Leiden clustering and export of an analysis-ready AnnData object
  • Use Case: streamline end-to-end scRNA-seq preprocessing in scripted pipelines

Quick Start

Feed a raw or partially processed AnnData object to obtain a processed AnnData with QC metrics, HVGs, reduced dimensions, and Leiden clusters.

Frequently Asked Questions about scrna-preprocessing-clustering

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

FAQPage Schema
How do I preprocess raw scRNA-seq counts into an analysis-ready AnnData object?▼

Preprocessing raw scRNA-seq counts involves applying quality control, normalization, HVG selection, PCA, UMAP, and Leiden clustering to generate an analysis-ready AnnData object with reduced dimensions and cluster labels.

What steps are required for scRNA-seq quality control and normalization in Scanpy?▼

scRNA-seq quality control and normalization require computing QC metrics to filter high-quality cells, followed by normalization and variable gene selection to ensure robust downstream analysis within the Scanpy workflow.

Can I use this scRNA-seq preprocessing pipeline on an existing h5ad file?▼

Yes, scRNA-seq preprocessing can be applied to raw counts, filtered matrices, or existing h5ad objects, automatically processing them into analysis-ready AnnData objects with documented QC thresholds and clustering parameters.

Does the scRNA-seq clustering workflow support Leiden clustering and UMAP visualization?▼

Yes, the scRNA-seq clustering workflow supports Leiden clustering and UMAP visualization by constructing neighborhood graphs and performing dimensionality reduction to identify and project cell clusters.

What is the best way to perform end-to-end scRNA-seq preprocessing and clustering reproducibly?▼

The best way to perform end-to-end scRNA-seq preprocessing reproducibly is to automate QC, normalization, HVG selection, and Leiden clustering while exporting artifacts with clearly documented threshold and parameter criteria.

Related Skills

Explore More Data & Analytics Skills→
omicverseomicverse
32

scenic-gene-regulatory-network

Infer transcription factor regulatory networks and score regulon activities from single-cell RNA-seq data.

Official
Advanced
ovachieverovachiever
52

scanpy

Analyze scRNA-seq data with Scanpy workflows for QC through trajectory inference.

Community
Advanced
LeonChaoXLeonChaoX
783

scanpy

Run end-to-end single-cell RNA-seq analysis with QC, clustering, and visualization.

Community
Advanced
jimmc414jimmc414
557

scvi-tools

Analyze single-cell omics data with probabilistic models using PyTorch.

Community
Advanced