Multi-view confounder detection for biomedical studies

Abstract

In many biomedical studies an important first step is checking for confounding factors. For association studies, confounding can for example be caused by ethnic differences in the case and control groups. In many other settings there might be confounding factors like batch effects or founder effects that also need to be detected and controlled for. Detecting confounding for data from one data source is well established (e.g., genomics data). Since more and more studies are now based on data from multiple data modalities (e.g., multi-omics), we evaluated whether multi-view confounder detection can benefit from state-of-the-art methods for multi-view data integration. Especially for clustering of multi-omics data, it has been shown that these methods can perform better than methods that treat the data modalities separately. Our results show that multi-view confounder analysis is possible and that building on multi-view data integration methods is better than treating the different data modalities separately.