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

ABSTRACT: 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 representa

SUBMITTER: Yu T 

PROVIDER: S-EPMC8820645 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature