Bi-order multimodal integration of single-cell data

Jinzhuang Dou; Shaoheng Liang; Vakul Mohanty; Qi Miao; Yuefan Huang; Qingnan Liang; Xuesen Cheng; Sangbae Kim; Jongsu Choi; Yumei Li; Li Li; May Daher; Rafet Basar; Katayoun Rezvani; Rui Chen; Ken Chen

doi:10.1186/s13059-022-02679-x

Bi-order multimodal integration of single-cell data

Genome Biol. 2022 May 9;23(1):112. doi: 10.1186/s13059-022-02679-x.

Authors

Jinzhuang Dou^#¹, Shaoheng Liang^#¹, Vakul Mohanty¹, Qi Miao¹, Yuefan Huang¹, Qingnan Liang², Xuesen Cheng², Sangbae Kim², Jongsu Choi², Yumei Li², Li Li³, May Daher³, Rafet Basar³, Katayoun Rezvani³, Rui Chen^{2

4}, Ken Chen⁵

Affiliations

¹ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA.
² Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
³ Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁴ Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
⁵ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA. kchen3@mdanderson.org.

^# Contributed equally.

Abstract

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Keywords: Bi-order canonical correlation analysis; Cell type identity; Single-cell multi-omics.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Abstract

Publication types

Grants and funding