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Statistical Applications in Genetics and Molecular Biology

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

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Volume 10, Issue 1 (Sep 2011)

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Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays

Lianbo Yu / Parul Gulati / Soledad Fernandez / Michael Pennell / Lawrence Kirschner / David Jarjoura
Published Online: 2011-09-15 | DOI: https://doi.org/10.2202/1544-6115.1701

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

About the article

Published Online: 2011-09-15


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1701.

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