single-cell-rna-qc

What problem does it solve?

Analyzing single-cell RNA-seq data requires rigorous quality control to remove low-quality cells and technical artifacts. This process is often complex, time-consuming, and critical for accurate downstream analysis. This Skill automates this essential first step, ensuring your data is clean and ready for biological insights.

Core Features & Use Cases

• Automated QC Workflow: Automatically performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) following scverse best practices.
• Intelligent Filtering: Applies robust MAD-based (Median Absolute Deviation) filtering to identify and remove low-quality cells, ensuring your downstream analysis is based on high-quality data.
• Comprehensive Visualizations: Generates detailed plots (histograms, violin plots, scatter plots) before and after filtering, providing clear insights into data quality and the impact of the QC process.
• Use Case: A biologist has generated new single-cell RNA-seq data and needs to quickly assess its quality and filter out problematic cells before proceeding with cell type identification. This Skill provides a "just works" solution, saving days of manual scripting and debugging.

Quick Start

For standard QC on an AnnData file:

python3 scripts/qc_analysis.py input.h5ad

For 10X Genomics .h5 files:

python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5