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

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,...

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Bibliographic Details
Main Authors: Gregory P. Way, Michael Zietz, Vincent Rubinetti, Daniel S. Himmelstein, Casey S. Greene
Format: Article
Language:English
Published: BMC 2020-05-01
Series:Genome Biology
Subjects:
Machine learning
Dimensionality reduction
Latent space
Gene expression
Autoencoders
Compression
Online Access:http://link.springer.com/article/10.1186/s13059-020-02021-3

Internet

http://link.springer.com/article/10.1186/s13059-020-02021-3

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