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

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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.

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Acknowledgements

Yang Ni is partially supported by the National Science Foundation, NSF DMS-1918851 and NSF DMS-2112943. Robert S. Chapkin is partially supported by the Allen Endowed Chair in Nutrition & Chronic Disease Prevention, and the National Institutes of Health (Grant Nos. R01-ES025713, R01-CA202697, R35-CA197707, and T32-CA090301). Kejun He is partially supported by the National Natural Science Foundation of China under Grant 11801560.

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Authors and Affiliations

  1. Institute of Statistics and Big Data, Renmin University of China, Beijing, China

    Fangting Zhou & Kejun He

  2. Department of Statistics, Texas A&M University, College Station, USA

    Fangting Zhou & Yang Ni

  3. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, USA

    James J. Cai

  4. Department of Nutrition and Food Science, Texas A&M University, College Station, USA

    Laurie A. Davidson & Robert S. Chapkin

  5. Program in Integrative Nutrition and Complex Diseases, Texas A &M University, College Station, USA

    Laurie A. Davidson & Robert S. Chapkin

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  1. Fangting Zhou
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Correspondence to Kejun He or Yang Ni.

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Zhou, F., He, K., Cai, J.J. et al. A Unified Bayesian Framework for Bi-overlapping-Clustering Multi-omics Data via Sparse Matrix Factorization. Stat Biosci 15, 669–691 (2023). https://doi.org/10.1007/s12561-022-09350-w

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