The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression timecourses 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 inversewidth, zero signal variance and noise variance as the observed variance. The logratio 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/14712105/12/180).
Package details 


Author  Alfredo Kalaitzis <[email protected]> 
Bioconductor views  Bioinformatics DifferentialExpression Microarray Preprocessing TimeCourse 
Maintainer  Alfredo Kalaitzis <[email protected]> 
License  AGPL3 
Version  1.16.1 
URL  
Package repository  View on GitHub 
Installation 
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