Discovering pathway and cell-type signatures in transcriptomic compendia with machine learning

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  Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
DOI
10.7287/peerj.preprints.27229v1
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology, Genomics, Data Mining and Machine Learning
Keywords
machine learning, transcriptome, signatures, pathway, cell-types, unsupervised learning, supervised learning, model interpretation
Copyright
© 2018 Way et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Way GP, Greene CS. 2018. Discovering pathway and cell-type signatures in transcriptomic compendia with machine learning. PeerJ Preprints 6:e27229v1

Abstract

Pathway and cell-type signatures are patterns present in transcriptome data that are associated with biological processes or phenotypic consequences. These signatures result from specific cell-type and pathway expression, but can require large transcriptomic compendia to detect. Machine learning techniques can be powerful tools in a practitioner’s toolkit for signature discovery through their ability to provide accurate and interpretable results. In the following review, we discuss various machine learning applications to extract pathway and cell-type signatures from transcriptomic compendia. We focus on the biological motivations and interpretation for both supervised and unsupervised learning approaches in this setting. We consider recent advances, including deep learning, and their applications to expanding bulk and single cell RNA data. As data and compute resources increase, opportunities for machine learning to aid in revealing biological signatures will continue to grow.

Author Comment

This is a preprint submission to PeerJ Preprints.