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PEAKQC: periodicity evaluation in single-cell ATAC-seq data for quality assessment
Jan Detleffsen1,2, Brenton Bruns1, Mette Bentsen1
1Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, 61231 Bad Nauheim, Hessen, Germany.
Briefings in Bioinformatics
|September 18, 2025
Summary
PEAKQC is a new Python package that improves single-cell ATAC-seq data analysis by offering robust quality control. It uses fragment length distribution patterns to identify high-quality cells, enhancing downstream analyses.
Area of Science:
- Genomics and Molecular Biology
- Bioinformatics and Computational Biology
Background:
- Chromatin organization is crucial for gene regulation, studied via chromatin accessibility assays like ATAC-seq.
- Single-cell ATAC-seq (scATAC-seq) analysis faces challenges like data sparsity and lack of standardized quality control (QC).
- Fragment length distribution (FLD) is a common QC metric for bulk ATAC-seq but lacks algorithmic solutions for single-cell data.
Purpose of the Study:
- To introduce PEAKQC, a novel Python package for robust QC of scATAC-seq data.
- To develop a method utilizing FLD patterns for identifying high-quality single cells.
- To establish FLD patterns as a potential standard for scATAC-seq data quality assessment.
Main Methods:
- Developed the PEAKQC Python package for scATAC-seq data analysis.
- Implemented a wavelet transformation-based convolution approach to quantify FLD pattern deviations in individual cells.
- Benchmarked PEAKQC against alternative QC metrics.
Main Results:
- PEAKQC effectively identifies high-quality cells based on FLD patterns.
- The tool demonstrated superior performance in selecting high-quality cells compared to other metrics.
- Improved cell identification and cluster separation in downstream analyses were facilitated by PEAKQC.
Conclusions:
- PEAKQC provides a robust and scalable solution for scATAC-seq QC.
- FLD patterns offer a novel and effective metric for assessing scATAC-seq data quality.
- PEAKQC can be integrated into existing Python-based single-cell analysis workflows.
