title: Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets.
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.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195.
authors.forename: Brian C
authors.identifier: http://orcid.org/0000-0001-8760-6731
authors.initials: BC
authors.lastname: Searle
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195.
authors.forename: Rachel M
authors.initials: RM
authors.lastname: Gittelman
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195.
authors.forename: Ohad
authors.initials: O
authors.lastname: Manor
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195 akeyj@uw.edu.
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Akey
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic / Q000592:standards*; D020869:Gene Expression Profiling / Q000592:standards*; D018740:Genetic Heterogeneity; D006801:Humans; D009928:Organ Specificity; D010641:Phenotype; D059467:Transcriptome
keywords: GTEx Consortium; Random Forest classification; gene expression normalization; transcriptional heterogeneity