SMILE: mutual information learning for integration of single-cell omics data.

ABSTRACT:

Motivation

Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single-cell omics data to be integrated across sources, types and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning).

Results

Using a unique cell-pairing design, SMILE successfully integrates multisource single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also

SUBMITTER: Xu Y 

PROVIDER: S-EPMC10060712 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature