Adversarial deconfounding autoencoder for learning robust gene expression embeddings.

ABSTRACT:

Motivation

Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g. batch effects) and uninteresting biological variables (e.g. age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e. an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically info

SUBMITTER: Dincer AB 

PROVIDER: S-EPMC7773484 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature