QCatch: A framework for quality control assessment and analysis of single-cell sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell sequencing data analysis demands rigorous quality control (QC) to identify and remove technical artifacts.
• Existing tools like alevin-fry and simpleaf provide efficient single-cell data processing but lack standardized QC reporting.

Purpose of the Study:

• To introduce QCatch, a novel command-line tool for generating comprehensive QC reports for single-cell sequencing data.
• To provide standardized, interactive HTML reports tailored to the outputs of alevin-fry and simpleaf.

Main Methods:

• QCatch is a Python-based command-line tool that accepts output directories from alevin-fry or simpleaf.
• It performs automated cell calling and generates detailed QC reports with visualizations and statistics.
• Key QC metrics include unique molecular identifier (UMI) distributions, sequencing saturation, and splicing status.

Main Results:

• QCatch generates interactive HTML QC reports with essential visualizations and statistics for single-cell data.
• The tool integrates seamlessly into downstream analysis workflows by exporting processed data in H5AD format.
• QCatch ensures QC assurance by providing detailed insights into data quality.

Conclusions:

• QCatch enhances the single-cell sequencing analysis ecosystem by providing standardized, automated QC reporting.
• The tool facilitates reliable downstream analysis by ensuring high-quality input data.
• QCatch is readily available on GitHub and installable via Bioconda and PyPI.