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Source Title:
Statistical Applications in Genetics and Molecular Biology
|
Volume/Issue:
Volume 11: Issue 3

The Beta-Binomial SGoF method for multiple dependent tests

Jacobo de Uña-Alvarez 1
  • 1 University of Vigo
DOI:
https://doi.org/10.1515/1544-6115.1812
|
Published online:
08 May 2012

In this paper a correction of SGoF multitesting method for dependent tests is introduced. The correction is based in the beta-binomial model, and therefore the new method is called Beta-Binomial SGoF (or BB-SGoF). Main properties of the new method are established, and its practical implementation is discussed. BB-SGoF is illustrated through the analysis of two different real data sets on gene/protein expression levels. The performance of the method is investigated through simulations too. One of the main conclusions of the paper is that SGoF strategy may have much power even in the presence of possible dependences among the tests.

Keywords:
Keywords; cDNA microarray; ; ; empirical null distribution; ; ; false discovery rate; ; ; Hotelling’s T2 statistic; ; ; multiple testing; ; ; partially paired data; Keywords; Gaussian graphical model; ; ; model selection; ; ; penalized empirical risk; Keywords; Empirical Bayes; ; ; EM; ; ; ChIP-Seq; ; ; histone methylation; Keywords; random matrix theory; ; ; clustering; ; ; dimension reduction; ; ; inverse correlation estimation; Keywords; sample size calculations; ; ; data imbalance; ; ; heterogeneity; ; ; covariates; ; ; technical replicates; ; ; observational study; ; ; expected Fisher information; ; ; cancer; ; ; clinical proteomics; ; ; SELDI; ; ; designing a proteomic profiling experiment; Keywords; microarray; ; ; saturation; ; ; multiscan; Keywords; ChIP-seq; ; ; wavelets; ; ; regression; ; ; normalization; ; ; order statistics; Keywords; Cox regression; ; ; phase ambiguity; ; ; prospective study; ; ; unphased genotypes; Keywords; confidence distribution; ; ; confidence posterior; ; ; credible interval; ; ; empirical Bayes; ; ; high-dimensional biology; ; ; hybrid inference; ; ; large-scale inference; ; ; local false discovery rate; ; ; multiple comparison procedure; ; ; multiple testing; ; ; objective Bayes factor; ; ; objective Bayesian analysis; ; ; observed confidence level; Keywords; batch effects; ; ; prediction; ; ; microarrays; ; ; reproducibility; ; ; research design; Keywords; hierarchical Bayes modelling; ; ; HIV combination therapies; ; ; statistical models; ; ; classification; Keywords; error detection; ; ; genome-wide association studies; ; ; known genotype-phenotype associations; ; ; outlier detection; Keywords; gene; ; ; enrichment; ; ; annotation; ; ; method; Keywords; BAGE variance estimator; ; ; empirical Bayes; ; ; false discovery rate; ; ; permutation test; ; ; shrinkage estimator; Keywords
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Journal + Issues

Statistical Applications in Genetics and Molecular Biology

Volume 11: Issue 3
SAGMB publishes significant research on the application of statistical ideas to problems arising from computational biology. The range of topics includes linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarrary data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies.
Online ISSN:
1544-6115
First published:
01 May 2002
Language:
English
Publisher:
De Gruyter

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