Gaussian Process Ranking and Estimation of Gene Expression time-
series // The gprege package implements the methodology
described in Kalaitzis Lawrence (2011) "A simple approach to
ranking differentially expressed gene expression time-courses
through Gaussian process regression". The software fits two GPs
with the an RBF (+ noise diagonal) kernel on each profile. One
GP kernel is initialised wih a short lengthscale
hyperparameter, signal variance as the observed variance and a
zero noise variance. It is optimised via scaled conjugate
gradients (netlab). A second GP has fixed hyperparameters: zero
inverse-width, zero signal variance and noise variance as the
observed variance. The log-ratio of marginal likelihoods of the
two hypotheses acts as a score of differential expression for
the profile. Comparison via ROC curves is performed against
BATS (Angelini et.al, 2007). A detailed discussion of the
ranking approach and dataset used can be found in the paper
(http://www.biomedcentral.com/1471-2105/12/180).