dc.contributor.author | Coffey, Norma | en |
dc.contributor.author | Hinde, John | en |
dc.date.accessioned | 2011-05-16T15:11:00Z | en |
dc.date.available | 2011-05-16T15:11:00Z | en |
dc.date.issued | 2011-05 | en |
dc.identifier.citation | Coffey, 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.identifier.uri | http://hdl.handle.net/10379/1903 | en |
dc.description.abstract | Gene 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.format | application/pdf | en |
dc.language.iso | en_US | en |
dc.publisher | Statistical Applications in Genetics and Molecular Biology | en |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | |
dc.subject | Functional data analysis | en |
dc.subject | Time-course microarray data | en |
dc.subject | Gene expression | en |
dc.subject | School of Mathematics, Statistics and Applied Mathematics | en |
dc.title | Analyzing time-course microarray data using functional data analysis - a review | en |
dc.type | Article | en |
dc.identifier.doi | 10.2202/1544-6115.1671 | en |
dc.local.publishedsource | http://www.bepress.com/sagmb/vol10/iss1/art23 | en |
dc.description.peer-reviewed | peer-reviewed | en |
dc.contributor.funder | Science Foundation Ireland under Grant No. 07/MI/012 | en |
dc.contributor.funder | Science Foundation Ireland | en |
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