PEAKQC: periodicity evaluation in single-cell ATAC-seq data for quality assessment.

Abstract

Chromatin organization guides gene regulatory mechanisms and has been subject of extensive research using chromatin accessibility assays. ATAC-seq is commonly applied to elucidate regulatory regions of the genome at both bulk and single-cell resolutions. However, the analysis of single-cell ATAC-seq data is particularly challenging due to issues such as data sparsity, low signal-to-noise ratios, and the lack of standardized quality control (QC) protocols. While QC based on the fragment length distribution (FLD) represents common practice for bulk analyses, an algorithmic solution that utilizes the full potential of the FLD at the single-cell level is missing. To address this limitation, we introduce the python package PEAKQC, a novel tool that provides a robust metric for identifying high-quality cells. PEAKQC quantifies the deviation of individual cells' FLD patterns from the expected distribution using a wavelet transformation-based convolution approach. Benchmarking against alternative metrics revealed favorable selection of high-quality cells, facilitating accurate downstream analysis including cell type identification and cluster separation. PEAKQC is readily installable via the Python Package Index and can be seamlessly integrated into existing single-cell analysis frameworks that utilize Python. By providing a robust and scalable solution for single-cell ATAC-seq QC, PEAKQC addresses a significant knowledge gap in the field and proposes FLD patterns as a novel standard for data quality assessment.