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Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays

Müller, Peter ; Parmigiani, Giovanni ; Robert, Christian ; Rousseau, Judith

Journal of the American Statistical Association, 2004-12, Vol.99 (468), p.990-1001 [Periódico revisado por pares]

Alexandria, VA: Taylor & Francis

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Título:
Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays
Autor: Müller, Peter ; Parmigiani, Giovanni ; Robert, Christian ; Rousseau, Judith
Assuntos: Applications ; Data analysis ; DNA microarrays ; Error rates ; Exact sciences and technology ; Expected utility ; False-discovery rate ; Gene expression ; General topics ; Genetics ; Genomic data analysis ; Genomics ; Inference ; Linear inference, regression ; Mathematics ; Modeling ; Monte Carlo methods ; Multiple comparison ; Probabilities ; Probability and statistics ; Sample size ; Sampling ; Sampling theory, sample surveys ; Sciences and techniques of general use ; Simulations ; Statistical analysis ; Statistical methods ; Statistics ; Test design ; Theory and Methods
É parte de: Journal of the American Statistical Association, 2004-12, Vol.99 (468), p.990-1001
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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Descrição: We consider the choice of an optimal sample size for multiple-comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about differential gene expression. However, the approach is valid in any application that involves multiple comparisons in a large number of hypothesis tests. We discuss two decision problems in the context of this setup: the sample size selection and the decision about the multiple comparisons. We adopt a decision-theoretic approach, using loss functions that combine the competing goals of discovering as many differentially expressed genes as possible, while keeping the number of false discoveries manageable. For consistency, we use the same loss function for both decisions. The decision rule that emerges for the multiple-comparison problem takes the exact form of the rules proposed in the recent literature to control the posterior expected falsediscovery rate. For the sample size selection, we combine the expected utility argument with an additional sensitivity analysis, reporting the conditional expected utilities and conditioning on assumed levels of the true differential expression. We recognize the resulting diagnostic as a form of statistical power facilitating interpretation and communication. As a sampling model for observed gene expression densities across genes and arrays, we use a variation of a hierarchical gamma/gamma model. But the discussion of the decision problem is independent of the chosen probability model. The approach is valid for any model that includes positive prior probabilities for the null hypotheses in the multiple comparisons and that allows for efficient marginal and posterior simulation, possibly by dependent Markov chain Monte Carlo simulation.
Editor: Alexandria, VA: Taylor & Francis
Idioma: Inglês

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Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays

Müller, Peter ; Parmigiani, Giovanni ; Robert, Christian ; Rousseau, Judith

Alexandria, VA: Taylor & Francis

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