single-cell-rna-qc

name	single-cell-rna-qc
description	Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations.
type	reference
version	1.0.0
category	science
last_updated	"2026-02-03T00:00:00.000Z"
source	https://github.com/anthropics/knowledge-work-plugins
related_skills	["scvi-tools","nextflow-pipelines","clinical-trial-protocol","instrument-data-allotrope","scientific-problem-selection"]
capabilities	[]
requires	[]
see_also	[]
tags	[]
scripts_exempt	true

Single-Cell RNA-seq Quality Control

Automated QC workflow for single-cell RNA-seq data following scverse best practices.

When to Use This Skill

Use when users:

• Request quality control or QC on single-cell RNA-seq data
• Want to filter low-quality cells or assess data quality
• Need QC visualizations or metrics
• Ask to follow scverse/scanpy best practices
• Request MAD-based filtering or outlier detection

Supported input formats:

• .h5ad files (AnnData format from scanpy/Python workflows)
• .h5 files (10X Genomics Cell Ranger output)

Default recommendation: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic.

Approach 1: Complete QC Pipeline (Recommended for Standard Workflows)

For standard QC following scverse best practices, use the convenience script scripts/qc_analysis.py:

python3 scripts/qc_analysis.py input.h5ad
# or for 10X Genomics .h5 files:
python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5

The script automatically detects the file format and loads it appropriately.

When to use this approach:

• Standard QC workflow with adjustable thresholds (all cells filtered the same way)
• Batch processing multiple datasets
• Quick exploratory analysis
• User wants the "just works" solution

Requirements: anndata, scanpy, scipy, matplotlib, seaborn, numpy

Parameters:

Customize filtering thresholds and gene patterns using command-line parameters:

• --output-dir - Output directory
• --mad-counts, --mad-genes, --mad-mt - MAD thresholds for counts/genes/MT%
• --mt-threshold - Hard mitochondrial % cutoff
• --min-cells - Gene filtering threshold
• --mt-pattern, --ribo-pattern, --hb-pattern - Gene name patterns for different species

Use --help to see current default values.

Outputs:

All files are saved to directory by default (or to the directory specified by ):