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single-cell-rna-qc

Compute QC metrics and apply MAD-based filtering to single-cell RNA-seq data.

112|26|Updated Jan 25, 2026
Search Tags:#bioinformatics#single-cell#scanpy#qc#mad-filtering#adata
One-click install
npx skills add https://github.com/Yambr/openwebui-computer-use-community --skill single-cell-rna-qc-yambr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-rna-qc
Source: https://github.com/Yambr/openwebui-computer-use-community/tree/main/skills/examples/single-cell-rna-qc
Command: npx skills add https://github.com/Yambr/openwebui-computer-use-community --skill single-cell-rna-qc-yambr

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 quality control for single-cell RNA-seq data by computing standard QC metrics and applying MAD-based filtering, enabling reliable downstream analyses.

Core Features & Use Cases

  • MAD-based filtering for outliers in total counts, detected genes, and mitochondrial content, preserving rare cell populations.
  • Comprehensive QC metrics calculation (total counts, genes per cell, percent mitochondrial, ribosomal and hemoglobin markers) with annotated results.
  • Visualizations and outputs: before/after QC plots, filtered data, and an annotated original for traceability.
  • Use Case: preprocess scRNA-seq data prior to normalization and clustering to ensure high-quality input.

Quick Start

Provide an input AnnData file (e.g., data.h5ad) and run the qc_analysis.py script to generate QC reports.

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 scRNA-seq data using MAD filtering?▼

Quality control for scRNA-seq data is automated by computing standard metrics and applying MAD-based filtering on outliers in total counts, detected genes, and mitochondrial content. It outputs a filtered dataset and an annotated original for traceability.

What is MAD-based filtering in single-cell RNA-seq preprocessing?▼

MAD-based filtering in single-cell RNA-seq preprocessing identifies outlier cells based on median absolute deviations across total counts, gene counts, and mitochondrial percentage. This statistical approach preserves rare cell populations while removing low-quality entries.

Can I run scRNA-seq QC on 10X .h5 files directly in scanpy?▼

Yes, scRNA-seq QC can be run on 10X .h5 files or .h5ad inputs. The process reads AnnData objects directly, computes metrics like ribosomal and hemoglobin markers, and generates before and after QC visualizations.

What QC metrics are calculated for an AnnData object before normalization?▼

For an AnnData object before normalization, calculated QC metrics include total counts, genes per cell, percent mitochondrial content, ribosomal markers, and hemoglobin markers. These are annotated within the original object for full traceability.

Does applying scRNA-seq quality control remove rare cell populations?▼

Applying scRNA-seq quality control using MAD-based filtering specifically preserves rare cell populations. By dynamically detecting statistical outliers rather than using hard thresholds, it isolates true low-quality cells without accidentally discarding valid rare types.

What is the best way to prepare scRNA-seq data for clustering?▼

The best way to prepare scRNA-seq data for clustering is to compute QC metrics and apply MAD-based filtering. This ensures only high-quality cells remain, providing a reliable filtered AnnData object for downstream normalization and analysis.

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