Single-Cell Multi-Omics Integration for Unpaired Data by a Siamese Network with Graph-Based Contrastive Loss

Authors

Chaozhong Liu
Linhua Wang
Zhandong Liu

Publication Date

1-4-2023

Journal

BMC Bioinformatics

DOI

10.1186/s12859-022-05126-7

PMID

36600199

PMCID

PMC9812356

PubMedCentral® Posted Date

1-4-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Multiomics, COVID-19, Transcriptome, Neural Networks, Computer, Single-Cell Analysis, Single-cell sequencing analysis, Data integration, Deep learning, COVID-19

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 downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research.

CONCLUSIONS: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Comments

This article has been corrected. See BMC Bioinformatics. 2023 Mar 29;24:123.