Representation Learning to Effectively Integrate and Interpret Omics Data

Sara Masarone

Keywords: generative models, omics, integration, biological data science, VAEs, multi-omics

Abstract: The last decade has seen an increase in the amount of high throughput data available to researchers. While this has allowed scientists to explore various hypotheses and research questions, it has also highlighted the importance of data integration in order to facilitate knowledge extraction and discovery. Although many strategies have been developed over the last few years, integrating data whilst generating an interpretable embedding still remains challenging due to difficulty in regularisation, especially when using deep generative models. As using one data type only provides a partial view to the condition of interest, we suggest a synergistic approach between different omics data types to infer knowledge and better stratify patients. We introduce a framework called Regularised Multi-View Variational Autoencoder (RMV-VAE) to integrate different omics data types whilst allowing researchers to obtain more biologically meaningful embeddings.

TL;DR: We introduce a framework called Regularised Multi-View Variational Autoencoder (RMV-VAE) to integrate different omics data types whilst allowing researchers to obtain more biologically meaningful embeddings