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scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization

Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S Jain, Mirjana Efremova, Sarah A Teichmann, Vaibhav Rajan, View ORCID ProfileXiuwei Zhang
doi: https://doi.org/10.1101/2022.05.17.492336
Ziqi Zhang
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
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Haoran Sun
2School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332
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Ragunathan Mariappan
3Department of Information Systems and Analytics, National University of Singapore, Singapore
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Xi Chen
4Department of Biology, Southern University of Science and Technology, China
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Xinyu Chen
5Bioengineering Program, Georgia Institute of Technology, Atlanta, Georgia, USA, 30332
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Mika S Jain
6Wellcome Sanger Institute, United Kingdom
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Mirjana Efremova
7Cancer Research UK Barts Center
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Sarah A Teichmann
6Wellcome Sanger Institute, United Kingdom
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Vaibhav Rajan
3Department of Information Systems and Analytics, National University of Singapore, Singapore
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Xiuwei Zhang
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
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  • ORCID record for Xiuwei Zhang
  • For correspondence: xiuwei.zhang@gatech.edu
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ABSTRACT

Single cell data integration methods aim to integrate cells across data batches and modalities, and obtain a comprehensive view of the cells. Single cell data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. We also show that integrated cell embedding combined with learned bio-markers leads to cell type annotations of higher quality or resolution compared to their original annotations.

Competing Interest Statement

The authors have declared no competing interest.

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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 08, 2022.
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scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization
Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S Jain, Mirjana Efremova, Sarah A Teichmann, Vaibhav Rajan, Xiuwei Zhang
bioRxiv 2022.05.17.492336; doi: https://doi.org/10.1101/2022.05.17.492336
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scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization
Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S Jain, Mirjana Efremova, Sarah A Teichmann, Vaibhav Rajan, Xiuwei Zhang
bioRxiv 2022.05.17.492336; doi: https://doi.org/10.1101/2022.05.17.492336

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