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dc.contributor.authorCoffey, Normaen
dc.contributor.authorHinde, Johnen
dc.identifier.citationCoffey, Norma and Hinde, John (2011) "Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review," Statistical Applications in Genetics and Molecular Biology: Vol. 10: Iss. 1, Article 23.en
dc.description.abstractGene expression over time can be viewed as a continuous process and therefore represented as a continuous curve or function. Functional data analysis (FDA) is a statistical methodology used to analyze functional data that has become increasingly popular in the analysis of time-course gene expression data. Several FDA techniques have been applied to gene expression profiles including functional regression analysis (to describe the relationship between expression profiles and other covariate(s)), functional discriminant analysis (to discriminate and classify groups of genes) and functional principal components analysis (for dimension reduction and clustering). This paper reviews the use of FDA and it¿s associated methods to analyze time-course microarray gene expression data.en
dc.publisherStatistical Applications in Genetics and Molecular Biologyen
dc.subjectFunctional data analysisen
dc.subjectTime-course microarray dataen
dc.subjectGene expressionen
dc.subjectSchool of Mathematics, Statistics and Applied Mathematicsen
dc.titleAnalyzing time-course microarray data using functional data analysis - a reviewen
dc.contributor.funderScience Foundation Ireland under Grant No. 07/MI/012en
dc.contributor.funderScience Foundation Irelanden

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