gprege

Gaussian Process Ranking and Estimation of Gene Expression time-series

Bioconductor version: Release (2.12)

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 <a.kalaitzis at ucl.ac.uk>

Maintainer: Alfredo Kalaitzis <a.kalaitzis at ucl.ac.uk>

To install this package, start R and enter:

    source("http://bioconductor.org/biocLite.R")
    biocLite("gprege")

To cite this package in a publication, start R and enter:

    citation("gprege")

Documentation

Details

Package Downloads