OmiTrans: Generative Adversarial Networks Based Omics-to-omics Translation Framework

With the significant development of high-throughput experimental technologies, different types of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data are produced from clinical samples at an unprecedented speed. The correlations between different types of omics data attract enormous research interest. In contrast, the study on genome-wide omics-to-omics 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 variants are among 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 propose OmiTrans, a deep learning framework that adopted the idea of generative adversarial networks to achieve omics-to-omics translation with promising results. OmiTrans can faithfully reconstruct genome-wide gene expression profiles from DNA methylation data with high accuracy and excellent model generalisation, as demonstrated in the experiments.