gprege: Gaussian Process Ranking and Estimation of Gene Expression time-series
Version 1.20.0

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, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (

Package details

AuthorAlfredo Kalaitzis <[email protected]>
Bioconductor views Bioinformatics DifferentialExpression Microarray Preprocessing TimeCourse
MaintainerAlfredo Kalaitzis <[email protected]>
Package repositoryView on Bioconductor
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gprege documentation built on May 31, 2017, 3:15 p.m.