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bio-workflows-scrnaseq-pipeline

Process 10X Cell Ranger scRNA-seq data into annotated cell types.

Updated Mar 30, 2026
Search Tags:#workflow#scanpy#seurat#cell-type-annotation#scrnaseq#sc_rna_seq#qc-clustering
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-scrnaseq-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-scrnaseq-pipeline
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-wf-scrnaseq-pipeline
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-scrnaseq-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This workflow processes single-cell RNA-seq data from 10X Cell Ranger outputs to annotated cell types, automating QC, normalization, clustering, marker detection, and annotation to reduce manual effort and improve reproducibility.

Core Features & Use Cases

  • End-to-end processing of 10X scRNA-seq data from raw counts to annotated cell types using Seurat (R) or Scanpy (Python).
  • Integrated QC, doublet detection, normalization, HVG selection, dimensionality reduction, clustering, and marker-based annotation.
  • Real-world use case: a lab processes multiple PBMC samples to identify cell-type composition and novel clusters across conditions.

Quick Start

Run this pipeline on your 10X Cell Ranger output to obtain annotated cell types.

Frequently Asked Questions about bio-workflows-scrnaseq-pipeline

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

FAQPage Schema
How do I process 10X Cell Ranger outputs to annotated cell types?▼

You can process 10X Cell Ranger outputs into annotated cell types by running an end-to-end scRNA-seq workflow that automates QC, normalization, clustering, and marker-based annotation.

What is the best way to perform scRNA-seq clustering and annotation across Seurat and Scanpy?▼

An end-to-end scRNA-seq workflow applies Seurat (R) and Scanpy (Python) pipelines to perform integrated doublet detection, dimensionality reduction, clustering, and marker-based cell-type annotation.

Do I need both R and Python environments to run scRNA-seq QC and clustering?▼

No, you can run the scRNA-seq workflow using either a Python environment with the Scanpy ecosystem or an R environment with the Seurat ecosystem to perform QC, normalization, and clustering.

How do I identify novel clusters in PBMC scRNA-seq samples?▼

To identify novel clusters in PBMC scRNA-seq samples, apply an end-to-end workflow that performs normalization, dimensionality reduction, clustering, and marker detection across multiple sample conditions.

Can I automate doublet detection and HVG selection for 10X scRNA-seq data?▼

Yes, an end-to-end scRNA-seq workflow automates doublet detection and HVG selection for 10X scRNA-seq data alongside QC, normalization, and dimensionality reduction before proceeding to clustering.

Why use an end-to-end scRNA-seq workflow instead of manual QC and clustering steps?▼

Using an end-to-end scRNA-seq workflow reduces manual effort and improves reproducibility by automating the entire pipeline from raw 10X Cell Ranger counts to annotated cell types.

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