PT  - JOURNAL ARTICLE
AU  - Ziqi Zhang
AU  - Haoran Sun
AU  - Ragunathan Mariappan
AU  - Xi Chen
AU  - Xinyu Chen
AU  - Mika S Jain
AU  - Mirjana Efremova
AU  - Sarah A Teichmann
AU  - Vaibhav Rajan
AU  - Xiuwei Zhang
TI  - scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization
AID  - 10.1101/2022.05.17.492336
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.17.492336
4099  - http://biorxiv.org/content/early/2022/08/08/2022.05.17.492336.short
4100  - http://biorxiv.org/content/early/2022/08/08/2022.05.17.492336.full
AB  - 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 StatementThe authors have declared no competing interest.