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single-cell-rna-qc

Identify low-quality cells in single-cell RNA-seq data using MAD-based QC.

704|58|Updated Mar 20, 2026
Search Tags:#single-cell#plotting#quality-control#scanpy#rna-seq#mad#qc-metrics
One-click install
npx skills add https://github.com/openyak/desktop --skill single-cell-rna-qc-openyak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-rna-qc
Source: https://github.com/openyak/desktop/tree/main/backend/app/data/plugins/bio-research/skills/single-cell-rna-qc
Command: npx skills add https://github.com/openyak/desktop --skill single-cell-rna-qc-openyak

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates the quality control process for single-cell RNA-seq data to identify low-quality cells, flagged features, and potential artifacts, enabling reliable downstream analysis without manual guesswork.

Core Features & Use Cases

  • Compute comprehensive QC metrics (total counts, genes per cell, mitochondrial, ribosomal, and hemoglobin content) and annotate cells with QC scores.
  • Detect outliers with MAD-based thresholds and apply a hard MT% cutoff to filter cells, then filter genes detected in too few cells.
  • Generate visualization dashboards before and after filtering to aid interpretation and decision-making.

Quick Start

Run the QC analysis script on your input AnnData file to generate filtered data and QC visuals.

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 automate quality control for single-cell RNA-seq data?▼

Automate single-cell RNA-seq quality control by computing total counts, genes per cell, and mitochondrial content, then filter low-quality cells and artifacts using MAD-based thresholds and a hard MT% cutoff.

What is MAD-based filtering and how does it work for scRNA-seq?▼

MAD-based filtering identifies outliers in single-cell RNA-seq data by calculating median absolute deviations for QC metrics, flagging cells that deviate significantly from the median to isolate low-quality entries.

Can I use scanpy and anndata .h5ad files for single-cell RNA-seq QC?▼

Yes, this quality control workflow accepts scanpy anndata .h5ad and .h5 files as input, computing QC metrics and filtering cells and genes directly within the scanpy environment.

How do I detect mitochondrial, ribosomal, and hemoglobin patterns in scRNA-seq?▼

Detect mitochondrial, ribosomal, and hemoglobin patterns in single-cell RNA-seq data by applying pattern matching to compute content percentages, which are then used to flag potential artifacts.

Does this single-cell RNA-seq QC workflow generate before and after visualizations?▼

Yes, the workflow generates visualization dashboards displaying QC metrics before and after filtering, aiding interpretation and decision-making for your single-cell RNA-seq data.

What is the best way to filter genes detected in too few cells during scRNA-seq analysis?▼

Filter genes detected in too few cells by applying threshold parameters after cell filtering, ensuring only robustly expressed features remain for downstream single-cell RNA-seq analysis.

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