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Differential Expression and Network Inferences through Functional Data Modeling
Author(s) -
Telesca Donatello,
Inoue Lurdes Y.T.,
Neira Mauricio,
Etzioni Ruth,
Gleave Martin,
Nelson Colleen
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01159.x
Subject(s) - microarray analysis techniques , microarray databases , expression (computer science) , gene regulatory network , gene expression , computational biology , computer science , gene expression profiling , dna microarray , transformation (genetics) , data set , data mining , functional data analysis , gene , biology , artificial intelligence , genetics , machine learning , programming language
Summary Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene‐expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene‐specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time‐transformation functions allow us to examine gene networks while accounting for features of the gene‐expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.