Learning data representation using modified autoencoder for the integrative analysis of multi-omics data

In integrative analyses of omics data, it is often of interest to extract data embedding from one data type that best reflect relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation and partial least squares. However, information contained in one data type pertaining to the other data type may not be in the linear form. Deep learning provides a convenient alternative to extract nonlinear information. Here we develop a method Autoencoder-based Integrative Multi-omics data Embedding (AIME) to extract such information. Using a real gene expression - methylation dataset, we show that AIME extracted meaningful information that the linear approach could not find. The R implementation is available at http://web1.sph.emory.edu/users/tyu8/AIME/.