Bioconductor-mirror/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 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).

Getting started

Package details

AuthorAlfredo Kalaitzis <[email protected]>
Bioconductor views Bioinformatics DifferentialExpression Microarray Preprocessing TimeCourse
MaintainerAlfredo Kalaitzis <[email protected]>
LicenseAGPL-3
Version1.21.0
URL
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("Bioconductor-mirror/gprege")
Bioconductor-mirror/gprege documentation built on June 1, 2017, 9:47 a.m.