recipe bioconductor-gprege

Gaussian Process Ranking and Estimation of Gene Expression time-series






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 bioconductor-gprege

(downloads) docker_bioconductor-gprege



Required By


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-gprege

and update with:

conda update bioconductor-gprege

or use the docker container:

docker pull<tag>

(see bioconductor-gprege/tags for valid values for <tag>)