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Genome Biol DOI:10.1186/s13059-020-02021-3

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

Publication TypeJournal Article
Year of Publication2020
AuthorsWay, GP, Zietz, M, Rubinetti, V, Himmelstein, DS, Greene, CS
JournalGenome Biol
Volume21
Issue1
Pages109
Date Published2020 May 11
ISSN1474-760X
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 space dimensionalities and observe various performance differences. We identify more curated pathway gene sets significantly associated with individual dimensions in denoising autoencoder and variational autoencoder models trained using an intermediate number of latent dimensionalities. Combining compressed features across algorithms and dimensionalities captures the most pathway-associated representations. When trained with different latent dimensionalities, models learn strongly associated and generalizable biological representations including sex, neuroblastoma MYCN amplification, and cell types. Stronger signals, such as tumor type, are best captured in models trained at lower dimensionalities, while more subtle signals such as pathway activity are best identified in models trained with more latent dimensionalities.

CONCLUSIONS: There is no single best latent dimensionality or compression algorithm for analyzing gene expression data. Instead, using features derived from different compression models across multiple latent space dimensionalities enhances biological representations.

DOI10.1186/s13059-020-02021-3
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/32393369?dopt=Abstract

Alternate JournalGenome Biol.
PubMed ID32393369
PubMed Central IDPMC7212571
Grant ListGBMF 4552 / / Gordon and Betty Moore Foundation /
HG010067 / NH / NIH HHS / United States
HG000046 / NH / NIH HHS / United States
CA237170 / CA / NCI NIH HHS / United States
CCDL / / Alex's Lemonade Stand Foundation for Childhood Cancer /