RT Journal Article
SR Electronic
T1 scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.17.492336
DO 10.1101/2022.05.17.492336
A1 Ziqi Zhang
A1 Haoran Sun
A1 Ragunathan Mariappan
A1 Xi Chen
A1 Xinyu Chen
A1 Mika S Jain
A1 Mirjana Efremova
A1 Sarah A Teichmann
A1 Vaibhav Rajan
A1 Xiuwei Zhang
YR 2022
UL http://biorxiv.org/content/early/2022/08/08/2022.05.17.492336.abstract
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.