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

Authors

Chaozhong Liu
Linhua Wang
Zhandong Liu

Language

English

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

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.

Keywords

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

Comments

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

Published Open-Access

yes

Recommended Citation

Chaozhong Liu, Linhua Wang, and Zhandong Liu, "Single-Cell Multi-Omics Integration for Unpaired Data by a Siamese Network with Graph-Based Contrastive Loss" (2023). Faculty and Staff Publications. 3434.
https://digitalcommons.library.tmc.edu/baylor_docs/3434