gprege

Gaussian Process Ranking and Estimation of Gene Expression time-series

Bioconductor version: Release (3.5)

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 et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180).

Author: Alfredo Kalaitzis <alkalait at gmail.com>

Maintainer: Alfredo Kalaitzis <alkalait at gmail.com>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("gprege")

Documentation

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

browseVignettes("gprege")

Details

Package Archives

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