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Oxford University Press, Genetics, 4(204), p. 1391-1396, 2016

DOI: 10.1534/genetics.116.193714

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Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets

Journal article published in 2016 by Brian C. Searle ORCID, Rachel M. Gittelman ORCID, Ohad Manor, Joshua M. Akey
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract Gene expression levels are dynamic molecular phenotypes that respond to biological, environmental, and technical perturbations. Here we use a novel replicate-classifier approach for discovering transcriptional signatures and apply it to the Genotype-Tissue Expression data set. We identified many factors contributing to expression heterogeneity, such as collection center and ischemia time, and our approach of scoring replicate classifiers allows us to statistically stratify these factors by effect strength. Strikingly, from transcriptional expression in blood alone we detect markers that help predict heart disease and stroke in some patients. Our results illustrate the challenges and opportunities of interpreting patterns of transcriptional variation in large-scale data sets.