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

Area of Science:

• Single-cell genomics
• Bioinformatics
• Computational biology

Background:

• Robust quality control (QC) is essential for accurate single-cell sequencing data analysis.
• Existing tools like alevin-fry and simpleaf offer efficient single-cell data processing but lack standardized QC reporting.
• A tailored QC reporting solution is needed to enhance the alevin-fry and simpleaf ecosystem.

Purpose of the Study:

• Introduce QCatch, a novel command-line tool for generating comprehensive QC reports.
• Provide standardized, interactive HTML QC reports specifically for single-cell quantification results.
• Facilitate seamless integration of QC into downstream single-cell analysis workflows.

Main Methods:

• Developed QCatch as a Python-based command-line tool.
• Designed QCatch to accept output from alevin-fry or simpleaf.
• Implemented essential QC steps including cell calling, UMI count distributions, sequencing saturation, and splicing status analysis.
• Enabled export of processed results in H5AD format.

Main Results:

• QCatch generates detailed, interactive HTML QC reports with informative visualizations and statistics.
• The tool performs essential processing steps like cell calling.
• Reports include key QC metrics such as UMI count distributions, sequencing saturation, and splicing status.
• Processed data is exported in the widely adopted H5AD format.

Conclusions:

• QCatch offers a standardized and comprehensive QC solution for single-cell data processed by alevin-fry and simpleaf.
• The tool enhances data reliability and facilitates downstream analysis.
• QCatch is readily available on GitHub and installable via Bioconda and PyPI.