Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments

In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to com...

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Vydané v:	Journal of the American Statistical Association Ročník 106; číslo 493; s. 73 - 88
Hlavní autori:	Sun, Wenguang, Wei, Zhi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria, VA American Statistical Association 01.03.2011 Taylor & Francis Ltd
Predmet:	Algebra Applications Applications and Case Studies Applied statistics Bioinformatics Chromosomes Commutative rings and algebras Computer analysis Data Data analysis Decision making Decision theory DNA Exact sciences and technology Experiments Gene expression General topics Genes Identification Immune response Inflammation Markov analysis Markov models Markovian processes Mathematics Model testing Modeling Oracles Probability and statistics Sciences and techniques of general use Statistical analysis Statistics Testing Time Human Correlation Microarray time-course data Statistical association Statistical estimation Statistical decision Multivariate analysis Microarray Conjunction and partial conjunction tests Multiple decision Statistical method Statistical test Compound decision problem Correlation analysis Decision theory Hidden Markov models Simultaneous set-wise testing Hidden Markov model False discovery rate P value
ISSN:	0162-1459, 1537-274X
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Popis
Shrnutí:	In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to combine the simultaneous inferences across multiple time points and (ii) how to control the multiplicity while accounting for the strong dependence. We formulate a compound decision-theoretic framework for set-wise multiple testing and propose a data-driven procedure that aims to minimize the missed set rate subject to a constraint on the false set rate. The hidden Markov model proposed in Yuan and Kendziorski (2006) is generalized to capture the temporal correlation in the gene expression data. Both theoretical and numerical results are presented to show that our data-driven procedure controls the multiplicity, provides an optimal way of combining simultaneous inferences across multiple time points, and greatly improves the conventional combined p-value methods. In particular, we demonstrate our method in an application to a study of systemic inflammation in humans for detecting early and late response genes.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0162-1459 1537-274X
DOI:	10.1198/jasa.2011.ap09587