Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously

Taroni, J. N.; Greene, C. S.

doi:10.1101/118349

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bioRxiv · 10.1101/118349

Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously

Taroni, J. N.·Greene, C. S.

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Abstract

MotivationLarge compendia of gene expression data have proven valuable for the discovery of novel biological relationships. The majority of available RNA assays are run on microarray, while RNA-seq is becoming the platform of choice for new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them. We performed supervised and unsupervised machine learning evaluations, as well as differential expression analyses, to assess which normalization methods are best suited for combining microarray and RNA-seq data.\n\nResultsWe find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including differential expression analysis.\n\nAvailability and ImplementationThese analyses were performed in R and are available at https://www.github.com/greenelab/RNAseq_titration_results under a BSD-3 clause license.\n\nContactcsgreene@upenn.edu\n\nSupplementary Information is available.

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Taroni, J. N., Greene, C. S.. 2017-03-20. Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously. https://doi.org/10.1101/118349

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