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single-cell-rna-qc

Filters low-quality cells from AnnData and 10X Genomics single-cell RNA-seq datasets using MAD-based thresholds.

7|Updated Feb 6, 2026
Search Tags:#quality control#scverse#bioinformatics#single-cell#scanpy#rna-seq
One-click install
npx skills add https://github.com/Epiphytic/ai-plugin-translator --skill single-cell-rna-qc-epiphytic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-rna-qc
Source: https://github.com/Epiphytic/ai-plugin-translator/tree/main/packages/core/test/fixtures/regression-output/knowledge-work-plugins/bio-research/skills/single-cell-rna-qc
Command: npx skills add https://github.com/Epiphytic/ai-plugin-translator --skill single-cell-rna-qc-epiphytic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines single-cell RNA-seq quality control by identifying low-quality cells, evaluating data quality metrics, and applying consistent filtering practices.

Core Features & Use Cases

  • Automated QC Analysis: Calculates count depth, detected genes, mitochondrial, ribosomal, and hemoglobin metrics for single-cell datasets.
  • MAD-Based Filtering: Applies scverse-inspired quality thresholds and generates before-and-after visualizations for informed filtering decisions.
  • Use Case: Analyze an AnnData or 10X Genomics dataset to remove poor-quality cells and prepare a cleaned dataset for downstream single-cell analysis.

Quick Start

Use the single-cell-rna-qc skill to perform quality control analysis on my single-cell RNA sequencing dataset and generate filtered outputs.

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 filtering on single-cell RNA-seq datasets?▼

Perform single-cell RNA-seq quality control by calculating count depth, detected genes, and mitochondrial metrics, then applying MAD-based thresholds to automatically filter low-quality cells and preserve annotated output datasets.

What is MAD-based filtering for identifying low-quality cells in scanpy?▼

MAD-based filtering applies scverse-inspired median absolute deviation thresholds to single-cell RNA-seq count depth and mitochondrial metrics, identifying and removing outlier cells to ensure consistent quality filtering for downstream analysis.

Can I use this quality control workflow with 10X Genomics files and AnnData objects?▼

Yes, the quality control workflow supports both AnnData objects and 10X Genomics files, calculating quality metrics and applying filtering thresholds within scverse and scanpy single-cell analysis environments.

How do I visualize before-and-after single-cell RNA-seq quality metrics?▼

Visualize before-and-after single-cell RNA-seq quality metrics by generating automated plots that compare pre-filtered and post-filtered datasets, showing the impact of MAD-based cell removal on count depth and gene detection distributions.

What metrics are calculated during single-cell RNA-seq quality control?▼

Single-cell RNA-seq quality control calculates count depth, detected genes, mitochondrial percentages, ribosomal percentages, and hemoglobin metrics to evaluate data quality and identify low-quality cells for removal.

Why should I automate cell filtering instead of manually setting thresholds for scRNA-seq data?▼

Automated MAD-based filtering for scRNA-seq data removes subjective manual thresholding, ensuring consistent quality control practices across datasets while objectively identifying low-quality cells based on statistical deviations from population medians.

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