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In the integrative analyses of omics data, it is often of interest to extractdata representation from one data type that best reflect its relations withanother data type. This task is traditionally fulfilled by linear methods suchas canonical correlation analysis (CCA) and partial least squares (PLS).However, information contained in one data type pertaining to the other datatype may be complex and in nonlinear form. Deep learning provides a convenientalternative to extract low-dimensional nonlinear data embedding. In addition,the deep learning setup can naturally incorporate the effects of clinicalconfounding factors into the integrative analysis. Here we report a deeplearning setup, named Autoencoder-based Integrative Multi-omics data Embedding(AIME), to extract data representation for omics data integrative analysis. Themethod can adjust for confounder variables, achieve informative data embedding,rank features in terms of their contributions, and find pairs of features fromthe two data types that are related to each other through the data embedding.In simulation studies, the method was highly effective in the extraction ofmajor contributing features between data types. Using a real microRNA-geneexpression dataset, and a real DNA methylation-gene expression dataset, we showthat AIME excluded the influence of confounders including batch effects, andextracted biologically plausible novel information. The R package based onKeras and the TensorFlow backend is available athttps://github.com/tianwei-yu/AIME.

Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments