Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.

ABSTRACT:

Background

Unsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality. In practice, most researchers fit a single algorithm and latent dimensionality. We sought to determine the extent by which selecting only one fit limits the biological features captured in the latent representations and, consequently, limits what can be discovered with subsequent analyses.

Results

We compress gene expression data from three large datasets consisting of adult normal tissue, adult cancer tissue, and pediatric cancer tissue. We train many different models across a large range of latent s

SUBMITTER: Way GP 

PROVIDER: S-EPMC7212571 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature