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single-cell-rna-qc

Automate single-cell RNA-seq quality control with MAD-based filtering and QC metrics.

1|Updated Mar 9, 2026
Search Tags:#visualization#single-cell#quality-control#anndata#scanpy#rna-seq#mad-filtering
One-click install
npx skills add https://github.com/kiryteo/opencode-setup --skill single-cell-rna-qc-kiryteo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-rna-qc
Source: https://github.com/kiryteo/opencode-setup/tree/main/skills/single-cell-rna-qc
Command: npx skills add https://github.com/kiryteo/opencode-setup --skill single-cell-rna-qc-kiryteo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill automates quality control for single-cell RNA-seq data by computing QC metrics and filtering out low-quality cells, enabling robust downstream analyses.

Core Features & Use Cases

  • MAD-based filtering: detect outliers in total counts, detected genes, and mitochondrial content.
  • QC metrics & visualizations: generate comprehensive plots before/after filtering and summaries.
  • End-to-end workflow: filter cells, filter genes, and produce outputs with QC annotations for downstream analysis.
  • Use Case: When analyzing a scRNA-seq dataset, run QC to identify high-quality cells and obtain a clean AnnData for normalization and downstream analyses.

Quick Start

Run the QC pipeline on your dataset by invoking the provided script with your input file.

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 anndata?▼

Perform single-cell RNA-seq quality control by running an automated pipeline that calculates QC metrics, generates visualizations, and filters out low-quality cells using MAD-based thresholds on your anndata objects to produce clean outputs for downstream analysis.

What is MAD-based filtering for scRNA-seq and when should I use it?▼

MAD-based filtering is a statistical method that detects outlier cells based on deviations in total counts, detected genes, and mitochondrial content. Use it during scRNA-seq quality control to objectively identify and remove low-quality cells or genes.

Can I use scanpy to filter low-quality cells from a 10X h5 file?▼

Yes, this quality control workflow supports both standard .h5ad and 10X .h5 file formats. It leverages scanpy and anndata to compute metrics, apply configurable MAD-based filtering thresholds, and output filtered datasets with QC annotations.

How do I generate QC visualizations before and after filtering cells in scRNA-seq?▼

Generate QC visualizations by executing the end-to-end pipeline, which automatically creates comprehensive plots summarizing total counts, detected genes, and mitochondrial content before and after applying MAD-based filtering to your single-cell RNA-seq data.

Are the filtering thresholds for scRNA-seq quality control configurable?▼

Yes, the quality control pipeline provides configurable thresholds for MAD-based filtering. You can adjust these parameters and utilize modular building blocks to customize metric calculations and filtering criteria for your specific scRNA-seq dataset.

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