Cross-platform normalization of microarray and RNA-seq data for machine learning applications

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1 Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
2 Quantitative Biomedical Sciences Program, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
3 Molecular and Cellular Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
4 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
5 Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
6 Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennslyvania, United States of America
DOI
10.7287/peerj.preprints.1460v1
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology, Genomics
Keywords
gene expression, normalization, rna-sequencing, microarray, machine learning, quantile normalization, cross-platform normalization, training, distribution
Copyright
© 2015 Thompson 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
Thompson JA, Tan J, Greene CS. 2015. Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ PrePrints 3:e1460v1

Abstract

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization and a simple log2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.

Author Comment

This is a submission to PeerJ for review.

Supplemental Information

Compiled Supplementary Figures and Legends

DOI: 10.7287/peerj.preprints.1460v1/supp-1