A Unified Bayesian Framework for Bi-overlapping-Clustering Multi-omics Data via Sparse Matrix Factorization

Fangting Zhou, Kejun He (), James J. Cai, Laurie A. Davidson, Robert S. Chapkin and Yang Ni ()
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Fangting Zhou: Renmin University of China
Kejun He: Renmin University of China
James J. Cai: Texas A&M University
Laurie A. Davidson: Texas A&M University
Robert S. Chapkin: Texas A&M University
Yang Ni: Texas A&M University

Statistics in Biosciences, 2023, vol. 15, issue 3, No 8, 669-691

Abstract: Abstract The advances of modern sequencing techniques have generated an unprecedented amount of multi-omics data which provide great opportunities to quantitatively explore functional genomes from different but complementary perspectives. However, distinct modalities/sequencing technologies generate diverse types of data which greatly complicate statistical modeling because uniquely optimized methods are required for handling each type of data. In this paper, we propose a unified framework for Bayesian nonparametric matrix factorization that infers overlapping bi-clusters for multi-omics data. The proposed method adaptively discretizes different types of observations into common latent states on which cluster structures are built hierarchically. The proposed Bayesian nonparametric method is able to automatically determine the number of clusters. We demonstrate the utility of the proposed method using simulation studies and applications to a single-cell RNA-sequencing dataset, a combination of single-cell RNA-sequencing and single-cell ATAC-sequencing dataset, a bulk RNA-sequencing dataset, and a DNA methylation dataset which reveal several interesting findings that are consistent with biological literature.

Keywords: Bayesian nonparametric prior; Data integration; Indian buffet process; Mixture model; Single-cell sequencing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09350-w

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