ScMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics.

ABSTRACT: Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type. Moreover, as the majority of single-cell cohorts only encompass one omics, it becomes critical to transfer the knowledge learnt from multimodal omics to enhance unimodal omics analysis. Therefore, we proposed a novel framework that leverages masked autoencoder with cross-attention mechanism, called scMMAE (single-cell multimodal masked autoencoder), to fuse multimodal omics and enhan

SUBMITTER: Meng D 

PROVIDER: S-EPMC11757910 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature