PeakQC

Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) has emerged as a leading method for studying chromatin accessibility and is now widely used at the single cell level. High data quality is critical for reliable downstream analyses, especially in single-cell studies where sparsity and low signal-to-noise ratios can obscure biological insights. While well-established quality control protocols exist for bulk ATAC-seq, adapting them to single-cell data presents unique challenges due to the sheer number of cells. Key features of ATAC-seq, such as enrichment of specific regions and periodic patterns in fragment length distributions, are well-established quality indicators. However, fragment length distributions cannot be manually assessed at single cell resolution.

To address this, we developed PEAKQC, a novel algorithm based on a single wavelet transform like convolution to automate quality assessment for single-cell ATAC-seq data. By analysing fragment length distributions at the single-cell level, PEAKQC overcomes the limitations of manual inspection and provides an efficient and scalable solution. We were able to show that its features extend and improve existing quality control approaches, leading to better clustering results.