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.
Original language | English (US) |
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Article number | 112 |
Journal | Genome biology |
Volume | 23 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
Keywords
- Bi-order canonical correlation analysis
- Cell type identity
- Single-cell multi-omics
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
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