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dc.creatorWang, Daifengen
dc.creatorArapostathis, Arien
dc.creatorWilke, Claus O.en
dc.creatorMarkey, Mia K.en
dc.date.accessioned2013-05-23T15:29:49Zen
dc.date.available2013-05-23T15:29:49Zen
dc.date.issued2012-01-10en
dc.identifier.citationWang D, Arapostathis A, Wilke CO, Markey MK (2012) Principal-Oscillation-Pattern Analysis of Gene Expression. PLoS ONE 7(1): e28805. doi:10.1371/journal.pone.0028805en
dc.identifier.urihttp://hdl.handle.net/2152/20142en
dc.description.abstractPrincipal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches.en
dc.language.isoengen
dc.publisherPublic Library of Scienceen
dc.rightsAttribution 3.0 United Statesen
dc.rightsCC-BYen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/en
dc.subjectCell cycle and cell divisionen
dc.subjectDistribution curvesen
dc.subjectEigenvaluesen
dc.subjectEigenvectorsen
dc.subjectGene expressionen
dc.subjectGenetic oscillatorsen
dc.subjectMultivariate data analysisen
dc.subjectProbability distributionen
dc.titlePrincipal-Oscillation-Pattern Analysis of Gene Expressionen
dc.typeArticleen
dc.description.departmentElectrical and Computer Engineeringen
dc.identifier.doi10.1371/journal.pone.0028805en


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States