Bi-order multimodal integration of single-cell data

• Main Authors: Basar, R. (Author), Chen, K. (Author), Chen, R. (Author), Cheng, X. (Author), Choi, J. (Author), Daher, M. (Author), Dou, J. (Author), Huang, Y. (Author), Kim, S. (Author), Li, L. (Author), Li, Y. (Author), Liang, Q. (Author), Liang, S. (Author), Miao, Q. (Author), Mohanty, V. (Author), Rezvani, K. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2022
• Subjects: article; Bi-order canonical correlation analysis; Cell type identity; correlation analysis; embedding; multiomics; muscle; Single-cell multi-omics
• Online Access: View Fulltext in Publisher
• ISBN: 14747596 (ISSN)
• DOI: 10.1186/s13059-022-02679-x

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. © 2022, The Author(s).