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COVID-19 severity is due to complications from SARS-Cov-2 but the clinicalcourse of the infection varies for individuals, emphasizing the need to betterunderstand the disease at the molecular level. We use clinical and multiplemolecular data (or views) obtained from patients with and without COVID-19 whowere (or not) admitted to the intensive care unit to shed light on COVID-19severity. Methods for jointly associating the views and separating the COVID-19groups (i.e., one-step methods) have focused on linear relationships. Therelationships between the views and COVID-19 patient groups, however, are toocomplex to be understood solely by linear methods. Existing nonlinear one-stepmethods cannot be used to identify signatures to aid in our understanding ofthe complexity of the disease. We propose Deep IDA (Integrative DiscriminantAnalysis) to address analytical challenges in our problem of interest. Deep IDAlearns nonlinear projections of two or more views that maximally associate theviews and separate the classes in each view, and permits feature ranking forinterpretable findings. Our applications demonstrate that Deep IDA hascompetitive classification rates compared to other state-of-the-art methods andis able to identify molecular signatures that facilitate an understanding ofCOVID-19 severity.

Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis  of Multi-View Data with Feature Ranking -- An Application to COVID-19  severity