Bi-order multimodal integration of single-cell data.
Bi-order multimodal integration of single-cell data.
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DOI:
10.1186/s13059-022-02679-x
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发表时间:
2022-05-09
期刊:
影响因子:
12.3
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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. The online version contains supplementary material available at 10.1186/s13059-022-02679-x.
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影响因子:
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10.1126/science.1256271
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期刊:
Science (New York, N.Y.)
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