DOI: 10.18129/B9.bioc.gprege    

This is the development version of gprege; for the stable release version, see gprege.

Gaussian Process Ranking and Estimation of Gene Expression time-series

Bioconductor version: Development (3.15)

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>

Citation (from within R, enter citation("gprege")):


To install this package, start R (version "4.2") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:



PDF R Script gprege Quick Guide
PDF   Reference Manual
Text   NEWS


biocViews Bioinformatics, DifferentialExpression, Microarray, Preprocessing, Software, TimeCourse
Version 1.39.0
In Bioconductor since BioC 2.10 (R-2.15) (10 years)
License AGPL-3
Depends R (>= 2.10), gptk
Suggests spam
Depends On Me robin
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package gprege_1.39.0.tar.gz
Windows Binary
macOS 10.13 (High Sierra) gprege_1.39.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
Package Short Url
Package Downloads Report Download Stats

Documentation »


R / CRAN packages and documentation

Support »

Please read the posting guide. Post questions about Bioconductor to one of the following locations: