Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis.

ABSTRACT: Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric

SUBMITTER: Lin KZ 

PROVIDER: S-EPMC10410705 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature