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single-cell-rna-qc

Automate single-cell RNA-seq quality control on .h5ad or .h5 files with MAD-based filtering.

Updated Mar 5, 2026
Search Tags:#quality control#bioinformatics#single-cell#data filtering#scanpy#rna-seq
One-click install
npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill single-cell-rna-qc-8gg-git
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-rna-qc
Source: https://github.com/8GG-Git/knowledge-work-plugins/tree/main/bio-research/skills/single-cell-rna-qc
Command: npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill single-cell-rna-qc-8gg-git

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the critical quality control (QC) process for single-cell RNA-seq data, ensuring that only high-quality cells and genes are used for downstream analysis, thereby improving the reliability of research findings.

Core Features & Use Cases

  • Automated QC Pipeline: Runs a comprehensive QC workflow using scverse best practices, including calculating metrics, filtering low-quality cells, and generating visualizations.
  • MAD-Based Filtering: Employs robust Median Absolute Deviation (MAD) methods for outlier detection, adapting to dataset-specific variations.
  • Flexible Approaches: Offers a complete pipeline script for standard use and modular functions for custom analysis workflows.
  • Use Case: A researcher has generated single-cell RNA-seq data and needs to filter out dead cells, empty droplets, and genes with low detection rates before proceeding to clustering and cell type annotation. This Skill provides an automated and best-practice-compliant solution.

Quick Start

Use the single-cell rna qc skill to perform a complete quality control pipeline on the file 'my_data.h5ad'.

Frequently Asked Questions about single-cell-rna-qc

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

FAQPage Schema
How do I perform quality control on single-cell RNA-seq data using scanpy?▼

To perform single-cell RNA-seq quality control using scanpy, this Skill automates metric calculation and filtering. It applies scverse best practices to remove low-quality cells and genes from your data.

What is MAD-based filtering for single-cell RNA-seq data?▼

MAD-based filtering is a robust statistical method using Median Absolute Deviation for outlier detection in single-cell RNA-seq data. It adapts to dataset-specific variations to accurately identify and remove low-quality cells.

Can I use an h5ad file for automated single-cell data filtering?▼

Yes, automated single-cell data filtering accepts .h5ad or .h5 files as input. The pipeline calculates QC metrics and generates visualizations to identify low-quality cells directly from these formats.

Does this single-cell QC pipeline follow scverse best practices?▼

Yes, the single-cell QC pipeline follows scverse best practices. It calculates standard quality control metrics, applies MAD-based filtering, and generates comprehensive visualizations to ensure high-quality data.

Do I need to manually threshold low-quality cells in RNA-seq data?▼

No, you do not need to manually threshold low-quality cells in RNA-seq data. The pipeline employs robust MAD-based methods to automatically detect outliers and adapt to dataset-specific variations.

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