scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection

Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S. Jain, Mirjana Efremova, Sarah A. Teichmann, Vaibhav Rajan and Xiuwei Zhang ()
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Ziqi Zhang: Georgia Institute of Technology
Haoran Sun: Georgia Institute of Technology
Ragunathan Mariappan: National University of Singapore
Xi Chen: Southern University of Science and Technology
Xinyu Chen: Georgia Institute of Technology
Mika S. Jain: Wellcome Sanger Institute
Mirjana Efremova: Cancer Research UK Barts Center
Sarah A. Teichmann: Wellcome Sanger Institute
Vaibhav Rajan: National University of Singapore
Xiuwei Zhang: Georgia Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Single cell data integration methods aim to integrate cells across data batches and modalities, and 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. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.

Date: 2023
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DOI: 10.1038/s41467-023-36066-2

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