scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data

Songming Tang, Xuejian Cui, Rongxiang Wang, Sijie Li, Siyu Li, Xin Huang and Shengquan Chen ()
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Songming Tang: Nankai University
Xuejian Cui: Tsinghua University
Rongxiang Wang: University of Virginia
Sijie Li: Nankai University
Siyu Li: Nankai University
Xin Huang: Beijing Institute of Radiation Medicine
Shengquan Chen: Nankai University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement performance.

Date: 2024
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DOI: 10.1038/s41467-024-46045-w

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