Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS

Zhen Miao, Jianqiao Wang, Kernyu Park, Da Kuang and Junhyong Kim ()
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Zhen Miao: University of Pennsylvania
Jianqiao Wang: Harvard T.H. Chan School of Health
Kernyu Park: University of Pennsylvania
Da Kuang: University of Pennsylvania
Junhyong Kim: University of Pennsylvania

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Single cell ATAC-seq (scATAC-seq) experimental designs have become increasingly complex, with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current scATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that allows complex hypothesis testing of accessibility-modulating factors while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves a 17% to 122% higher power on average than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks, including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to datasets from various tissues and show its ability to reveal previously undiscovered insights in scATAC-seq data.

Date: 2025
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DOI: 10.1038/s41467-024-55580-5

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