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Building classification models that predict a binary class label on the basisof high dimensional multi-omics datasets poses several challenges, due to thetypically widely differing characteristics of the data layers in terms ofnumber of predictors, type of data, and levels of noise. Previous research hasshown that applying classical logistic regression with elastic-net penalty tothese datasets can lead to poor results (Liu et al., 2018). We implement atwo-step approach to multi-omic logistic regression in which variable selectionis performed on each layer separately and a predictive model is then builtusing the variables selected in the first step. Here, our approach is comparedto other methods that have been developed for the same purpose, and we adaptexisting software for multi-omic linear regression (Zhao and Zucknick, 2020) tothe logistic regression setting. Extensive simulation studies show that ourapproach should be preferred if the goal is to select as many relevantpredictors as possible, as well as achieving prediction performances comparableto those of the best competitors. Our motivating example is a cardiometabolicsyndrome dataset comprising eight 'omic data types for 2 extreme phenotypegroups (10 obese and 10 lipodystrophy individuals) and 185 blood donors. Ourproposed approach allows us to identify features that characterisecardiometabolic syndrome at the molecular level. R code is available athttps://github.com/acabassi/logistic-regression-for-multi-omic-data.

Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome