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The Beta-Binomial SGoF method for multiple dependent tests
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Jacobo de Uña-Alvarez
Published/Copyright:
May 8, 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; cDNA microarray; ; empirical null distribution; false discovery rate; Hotelling’s T2 statistic; multiple testing; partially paired data; Gaussian graphical model; model selection; penalized empirical risk; Empirical Bayes; EM; ChIP-Seq; histone methylation; random matrix theory; clustering; dimension reduction; inverse correlation estimation; sample size calculations; data imbalance; heterogeneity; covariates; technical replicates; observational study; expected Fisher information; cancer; clinical proteomics; SELDI; designing a proteomic profiling experiment; microarray; saturation; multiscan; ChIP-seq; wavelets; regression; normalization; order statistics; Cox regression; phase ambiguity; prospective study; unphased genotypes; confidence distribution; confidence posterior; credible interval; empirical Bayes; high-dimensional biology; hybrid inference; large-scale inference; local false discovery rate; multiple comparison procedure; objective Bayes factor; objective Bayesian analysis; observed confidence level; batch effects; prediction; microarrays; reproducibility; research design; hierarchical Bayes modelling; HIV combination therapies; statistical models; classification; error detection; genome-wide association studies; known genotype-phenotype associations; outlier detection; gene; enrichment; annotation; method; BAGE variance estimator; permutation test; shrinkage estimator
Published Online: 2012-5-8
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Exploring Multicollinearity Using a Random Matrix Theory Approach
- The Beta-Binomial SGoF method for multiple dependent tests
- Detecting Sample Misidentifications in Genetic Association Studies
- Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis
- Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies
- The practical effect of batch on genomic prediction
- Normalization, bias correction, and peak calling for ChIP-seq
- Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model
- Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals
- Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set
- Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes
- Graph Selection with GGMselect
- Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry
- A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification
- Software Communication
- GENOVA: Gene Overlap Analysis of GWAS Results
