A multivariate empirical Bayes statistic for replicated microarray time course data

Abstract

In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the $MB$-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray time course experiments. We first use conjugate priors to develop our one-sample multivariate empirical Bayes framework for the null hypothesis that the expected temporal profile stays at 0. This leads to our one-sample $MB$-statistic and a one-sample $\tilde{T}{}^2$-statistic, a variant of the one-sample Hotelling $T^2$-statistic. Both the $MB$-statistic and ${\tilde{T}}^2$-statistic can be used to rank genes in the order of evidence of nonzero mean, incorporating the correlation structure across time points, moderation and replication. We also derive the corresponding $MB$-statistics and ${\tilde{T}}^2$-statistics for the one-sample problem where the null hypothesis states that the expected temporal profile is constant, and for the two-sample problem where the null hypothesis is that two expected temporal profiles are the same.

Published at http://dx.doi.org/10.1214/009053606000000759 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• In silico prediction of protein-protein interactions in human macrophages
• A multivariate empirical Bayes statistic for replicated microarray time course data

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