title: META-ANALYSIS OF CONTINUOUS PHENOTYPES IDENTIFIES A GENE SIGNATURE THAT CORRELATES WITH COPD DISEASE STATUS.
abstract: The utility of multi-cohort two-class meta-analysis to identify robust differentially expressed gene signatures has been well established. However, many biomedical applications, such as gene signatures of disease progression, require one-class analysis. Here we describe an R package, MetaCorrelator, that can identify a reproducible transcriptional signature that is correlated with a continuous disease phenotype across multiple datasets. We successfully applied this framework to extract a pattern of gene expression that can predict lung function in patients with chronic obstructive pulmonary disease (COPD) in both peripheral blood mononuclear cells (PBMCs) and tissue. Our results point to a disregulation in the oxidation state of the lungs of patients with COPD, as well as underscore the classically recognized inammatory state that underlies this disease.
authors.affiliation: Stanford University School of Medicine, Stanford University Stanford, CA 94305, USA*authors contributed equally to this work.
authors.forename: Madeleine
authors.initials: M
authors.lastname: Scott
authors.forename: Francesco
authors.initials: F
authors.lastname: Vallania
authors.forename: Purvesh
authors.initials: P
authors.lastname: Khatri
mesh_terms: D015331:Cohort Studies; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005541:Forced Expiratory Volume; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000378:metabolism; D008168:Lung / Q000503:physiopathology; D010641:Phenotype; D029424:Pulmonary Disease, Chronic Obstructive / Q000235:genetics* / Q000503:physiopathology; D059467:Transcriptome