Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

ABSTRACT:

Background

Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss.

Results

By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts do

SUBMITTER: Liu C 

PROVIDER: S-EPMC9812356 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature