Version 1.4.0 Date 2012-02-27 Title Gaussian Process Ranking and Estimation of Gene Expression time-series

Description 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 vari-ance 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 i