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bio-single-cell-preprocessing

Automate QC, filtering, and normalization for single-cell RNA-seq data with Seurat and Scanpy.

Updated Mar 30, 2026
Search Tags:#normalization#scaling#quality-control#scanpy#seurat#scrna-seq#highly-variable-genes
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-preprocessing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-preprocessing
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-single-cell-preprocessing
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-preprocessing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficiently perform quality control, filtering, normalization, HVG detection, and scaling for single-cell RNA-seq data using Seurat (R) and Scanpy (Python). This workflow enables data-ready preprocessing for downstream analyses such as clustering, trajectory inference, and differential expression.

Core Features & Use Cases

  • QC metric calculation (n_genes, total_counts, percent_mt) and visualization for both Seurat and Scanpy pipelines
  • Filter cells and genes with user-defined thresholds to remove low-quality data and potential doublets
  • Normalize counts, identify highly variable genes, and scale data to prepare for dimensionality reduction
  • Support for both Seurat (R) and Scanpy (Python) workflows, enabling flexible, multi-tool preprocessing

Quick Start

Preprocess my scRNA-seq data by running QC, filtering, normalization, HVG selection, and scaling with Seurat or Scanpy.

Frequently Asked Questions about bio-single-cell-preprocessing

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

FAQPage Schema
How do I preprocess scRNA-seq data for quality control and normalization?▼

Preprocess scRNA-seq data by calculating QC metrics like n_genes and percent_mt, filtering low-quality cells, normalizing counts, detecting highly variable genes, and scaling data for downstream analyses.

Can I use both Seurat and Scanpy for single-cell RNA-seq preprocessing?▼

Yes, you can use both Seurat in R and Scanpy in Python for scRNA-seq preprocessing. The workflow supports QC filtering, normalization, HVG detection, and scaling across both frameworks.

What's the best way to identify highly variable genes in scRNA-seq data?▼

Identify highly variable genes during the scRNA-seq preprocessing workflow by normalizing counts and applying HVG detection algorithms available in either Seurat or Scanpy.

Do I need Python and R installed to run scRNA-seq quality control workflows?▼

Yes, you need Python with scanpy and R with Seurat installed to run the combined scRNA-seq quality control, normalization, and scaling workflows across both environments.

How do I regress out confounders when normalizing single-cell RNA-seq data?▼

Regress out confounders in single-cell RNA-seq data during the scaling step of the preprocessing workflow, which supports confounder regression across datasets using Seurat or Scanpy.

Why filter cells and genes before normalizing scRNA-seq data?▼

Filter cells and genes before normalizing scRNA-seq data to remove low-quality data and potential doublets using user-defined thresholds, ensuring accurate downstream clustering and differential expression.

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