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Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

Ruohan Wang, View ORCID ProfileJianping Wang, View ORCID ProfileShuai Cheng Li
doi: https://doi.org/10.1101/2022.08.26.505382
Ruohan Wang
1City University of Hong Kong, Department of Computer Science, Hong Kong, 999077, China
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Jianping Wang
1City University of Hong Kong, Department of Computer Science, Hong Kong, 999077, China
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  • ORCID record for Jianping Wang
Shuai Cheng Li
1City University of Hong Kong, Department of Computer Science, Hong Kong, 999077, China
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  • ORCID record for Shuai Cheng Li
  • For correspondence: shuaicli@cityu.edu.hk
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ABSTRACT

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e., the higher-rank matrices. However, the proposed analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. To deal with sparse, noisy, and heterogeneous single-cell data, we incorporate various distributions in SCOIT, including Gaussian, Poisson, and negative binomial distributions. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to seven single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to seven state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming conventional imputation methods with the Pearson correlation coefficient increased by 0.03-0.28.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* jianwang{at}cityu.edu.hk

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 26, 2022.
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Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data
Ruohan Wang, Jianping Wang, Shuai Cheng Li
bioRxiv 2022.08.26.505382; doi: https://doi.org/10.1101/2022.08.26.505382
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Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data
Ruohan Wang, Jianping Wang, Shuai Cheng Li
bioRxiv 2022.08.26.505382; doi: https://doi.org/10.1101/2022.08.26.505382

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