BioinformaticsBioinformaticsPeer-reviewed journal article
QCatch: A framework for quality control assessment and analysis of single-cell sequencing data
Abstract
MotivationSingle-cell sequencing data analysis requires robust quality control (QC) to mitigate technical artifacts and ensure reliable downstream results. While tools like alevin-fry and simpleaf (and augmented execution context for the alevin-fry), offer flexibility and computational efficiency to process single-cell data, this ecosystem will further benefit from a standardized QC reporting tailored for its outputs. ResultsWe introduce QCatch, a Python-based command-line tool that generates comprehensive and interactive HTML QC reports designed specifically for single-cell quantification results. Taking the output directory of alevin-fry or simpleaf as the input, QCatch is able to perform essential processing steps, like cell calling, and generate detailed QC reports that contain informative visualizations and statistics, including unique molecular identifier (UMI) count distributions, sequencing saturation estimates, and splicing status information, for QC assurance. Built for seamless integration into downstream analysis workflows, QCatch exports the processed results in a richly-annotated H5AD format file, a widely used data format common among many downstream single-cell data analysis tools. AvailabilityThe source code and documentation of QCatch are available on GitHub at https://github.com/COMBINE-lab/QCatch. QCatch can be installed via both Bioconda and PyPI.
- assay: scRNA-seq
- research focus: benchmarking
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- approach: benchmark study
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 0th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data
Rui Hong, Yusuke Koga, … Joshua David Campbell·Nature CommunicationsPeer-reviewed·Mar 30, 2022
- PEAKQC: Periodicity Evaluation in scATAC-seq data for quality assessment
Jan Detleffsen, Carsten Tobias Kuenne, … Mario Looso·bioRxiv·Feb 20, 2025
- Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data
Dongze He, Mohsen Zakeri, … Rob Patro·Nature MethodsPeer-reviewed·Mar 1, 2022
- SampleQC: robust multivariate, multi-celltype, multi-sample quality control for single cell data
Will Macnair, Mark D. Robinson·Genome biologyPeer-reviewed·Feb 10, 2023
- Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics
Ayshwarya Subramanian, Mikhail Alperovich, … Bo Li·Genome biologyPeer-reviewed·Dec 27, 2022
- Sharq, a versatile preprocessing and QC pipeline for Single Cell RNA-seq
Tito Candelli, Philip Lijnzaad, … Frank C. P. Holstege·bioRxiv·Feb 7, 2018