Bi-order multimodal integration of single-cell data

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 ca...

Descripción completa

Detalles Bibliográficos
Autores principales: Dou, Jinzhuang, Liang, Shaoheng, Mohanty, Vakul, Miao, Qi, Huang, Yuefan, Liang, Qingnan, Cheng, Xuesen, Kim, Sangbae, Choi, Jongsu, Li, Yumei, Li, Li, Daher, May, Basar, Rafet, Rezvani, Katayoun, Chen, Rui, Chen, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082907/
https://www.ncbi.nlm.nih.gov/pubmed/35534898
http://dx.doi.org/10.1186/s13059-022-02679-x
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02679-x.

Ejemplares similares