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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Veröffentlicht in:Journal of the American Statistical Association Jg. 106; H. 493; S. 73 - 88
Hauptverfasser: Sun, Wenguang, Wei, Zhi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria, VA American Statistical Association 01.03.2011
Taylor & Francis Ltd
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
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ISSN:0162-1459
1537-274X
DOI:10.1198/jasa.2011.ap09587