  In many longitudinal microarray studies, the gene expression levels in a
random sample are observed repeatedly over time under two or more conditions.
The resulting time courses are generally very short, high-dimensional, and may
have missing values. Moreover, for every gene, a certain amount of variability
in the temporal profiles, among biological replicates, is generally observed.
We propose a functional mixed-effects model for estimating the temporal pattern
of each gene, which is assumed to be a smooth function. A statistical test
based on the distance between the fitted curves is then carried out to detect
differential expression. A simulation procedure for assessing the statistical
power of our model is also suggested. We evaluate the model performance using
both simulations and a real data set investigating the human host response to
BCG exposure.
