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Multi-view Omics Translation with Multiplex Graph Neural Networks

The rapid development of high-throughput experimental technologies for biological sampling has made the collection of omics data (e.g., genomics, epigenomics, transcriptomics and metabolomics) possible at a small cost. While multi-view approaches to omics data have a long history, omics-to-omics translation is a relatively new strand of research with useful applications such as recovering missing or censored data and finding new correlations between samples. As the relations between omics can be non-linear and exhibit long-range dependencies between parts of the genome, deep neural networks can be an effective tool. Graph neural networks have been applied successfully in many different areas of research, especially in problems where annotated data is sparse, and have recently been extended to the heterogeneous graph case, allowing for the modelling of multiple kinds of similarities and entities. Here, we propose a meso-scale approach to construct multiplex graphs from multi-omics data, which can construct several graphs per omics and cross-omics graphs. We also propose a neural network architecture for omics-to-omics translation from these multiplex graphs, featuring a graph neural network encoder, coupled with an attention layer. We evaluate the approach on the open The Cancer Genome Atlas dataset (N=3023), showing that for MicroRNA expression prediction our approach has lower prediction error than regularized linear regression or modern generative adversarial networks.