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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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IMPACT FACTOR 2013: 1.055
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Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays

Lianbo Yu1 / Parul Gulati2 / Soledad Fernandez3 / Michael Pennell4 / Lawrence Kirschner5 / David Jarjoura6

1The Ohio State University

2The Ohio State University

3The Ohio State University

4The Ohio State University

5The Ohio State University

6The Ohio State University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 10, Issue 1, Pages 1–22, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1701, September 2011

Publication History

Published Online:
2011-09-15

Gene expression microarray experiments with few replications lead to great variability in estimates of gene variances. Several Bayesian methods have been developed to reduce this variability and to increase power. Thus far, moderated t methods assumed a constant coefficient of variation (CV) for the gene variances. We provide evidence against this assumption, and extend the method by allowing the CV to vary with gene expression. Our CV varying method, which we refer to as the fully moderated t-statistic, was compared to three other methods (ordinary t, and two moderated t predecessors). A simulation study and a familiar spike-in data set were used to assess the performance of the testing methods. The results showed that our CV varying method had higher power than the other three methods, identified a greater number of true positives in spike-in data, fit simulated data under varying assumptions very well, and in a real data set better identified higher expressing genes that were consistent with functional pathways associated with the experiments.

Keywords: empirical Bayes; microarray data analysis; variance smoothing

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