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- Title
Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS.
- Authors
Miao, Zhen; Wang, Jianqiao; Park, Kernyu; Kuang, Da; Kim, Junhyong
- 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. scATAC-seq data pose statistical challenges due to sparsity and cell-specific sequence capture. Here, the authors present PACS, a zero-adjusted statistical model that enables complex hypothesis testing of accessibility-modulating factors while addressing sparse and incomplete data.
- Subjects
MISSING data (Statistics); STATISTICAL models; TASK analysis; CHROMATIN; STATISTICS
- Publication
Nature Communications, 2025, Vol 16, Issue 1, p1
- ISSN
2041-1723
- Publication type
Academic Journal
- DOI
10.1038/s41467-024-55580-5