Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseases

Abstract

Heterogeneity in human diseases presents challenges in diagnosis and treatments due to the broad range of manifestations and symptoms. With the rapid development of labelled multi-omic data, integrative machine learning methods have achieved breakthroughs in treatments by redefining these diseases at a more granular level. These approaches often have limitations in scalability, oversimplification, and handling of missing data. In this study, we introduce Multi-Omic Graph Diagnosis (MOGDx), a flexible command line tool for the integration of multi-omic data to perform classification tasks for heterogeneous diseases. MOGDx is a network integrative method that combines patient similarity networks with a reduced vector representation of genomic data. The reduced vector is derived from the shared latent embedding of a multi-modal encoder and the combined network is fed into a graph convolutional network for classification. The multi-modal encoder and graph convolutional network are trained simultaneously making a fully supervised pipeline. MOGDx was evaluated on three datasets from the cancer genome atlas for breast invasive carcinoma, kidney cancer, and low grade glioma. MOGDx demonstrated state-of-the-art performance and an ability to identify relevant multi-omic markers in each task. It did so while integrating more genomic measures with greater patient coverage compared to other network integrative methods. MOGDx is available to download from https://github.com/biomedicalinformaticsgroup/MOGDx. Overall, MOGDx is a promising tool for integrating multi-omic data, classifying heterogeneous diseases, and interpreting genomic markers.