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).
|Author||Alfredo Kalaitzis <firstname.lastname@example.org>|
|Date of publication||None|
|Maintainer||Alfredo Kalaitzis <email@example.com>|
compareROC: Make ROC plots.
DellaGattaData: Fragment dataset of 13 time-point mouse microarray time...
demTp63Gp1: gprege on TP63 expression time-series.
DGdat_p63: BATS rankings (Angelini, 2007) Case 1: Delta error prior Case...
exhaustivePlot: Plot of the LML function by exhaustive search.
gprege: Gaussian process ranking and estimation of gene expression...
gprege-package: gprege - Gaussian Process Ranking and Estimation of Gene...
rocStats: Make ROC curve data.