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Gaussian Process Ranking and Estimation of Gene Expression time-series

Bioconductor version: Release (2.14)

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 (

Author: Alfredo Kalaitzis <alkalait at>

Maintainer: Alfredo Kalaitzis <alkalait at>

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PDF R Script gprege Quick Guide
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biocViews DifferentialExpression, Microarray, Preprocessing, Software, TimeCourse
Version 1.8.0
In Bioconductor since BioC 2.10 (R-2.15)
License AGPL-3
Depends R (>= 2.8.0), gptk
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