OmiTrans: generative adversarial networks based omics-to-omics translation framework

TL;DR

OmiTrans is a novel deep learning framework based on generative adversarial networks designed to translate between different types of omics data, such as from DNA methylation to gene expression, demonstrating high accuracy and generalization.

Contribution

This work introduces OmiTrans, the first framework applying GANs to genome-wide omics data translation, filling a gap in multi-omics data prediction research.

Findings

Faithful reconstruction of gene expression from methylation data

High accuracy and model generalization demonstrated

Potential for broad applications in multi-omics analysis

Abstract

With the rapid development of high-throughput experimental technologies, different types of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples. The correlations between different omics types attracts a lot of research interest, whereas the stduy on genome-wide omcis data translation (i.e, generation and prediction of one type of omics data from another type of omics data) is almost blank. Generative adversarial networks and the variants are one of the most state-of-the-art deep learning technologies, which have shown great success in image-to-image translation, text-to-image translation, etc. Here we proposed OmiTrans, a deep learning framework adopted the idea of generative adversarial networks to achieve omics-to-omics translation with promising results. OmiTrans was able to faithfully reconstruct gene expression profiles from DNA methylation data with high accuracy and great model generalisation, as demonstrated in the experiments.