title: Cross-platform normalization of microarray and RNA-seq data for machine learning applications.
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, nonparanormal transformation, and a simple log 2 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.
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America; Quantitative Biomedical Sciences Program, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.
authors: Jeffrey A
authors: JA
authors: Thompson
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America; Molecular and Cellular Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.
authors: Jie
authors: J
authors: Tan
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America; Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennslyvania, United States of America.
authors: Casey S
authors: 0000-0001-8713-9213
authors: CS
authors: Greene
keywords: Cross-platform normalization; Distribution; Gene expression; Machine learning; Microarray; Nonparanormal transformation; Normalization; Quantile normalization; RNA-sequencing; Training