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