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 $\mathit{MB}$-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 $\mathit{MB}$-statistic and a one-sample $\widetilde{T}{}^2$-statistic, a variant of the one-sample Hotelling $T^2$-statistic. Both the $\mathit{MB}$-statistic and $\widetilde{T}^2$-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 $\mathit{MB}$-statistics and $\widetilde{T}^2$-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.