gprege
This package is for version 3.14 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see gprege.
Gaussian Process Ranking and Estimation of Gene Expression time-series
Bioconductor version: 3.14
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>
citation("gprege")):
Installation
To install this package, start R (version "4.1") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("gprege")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("gprege")
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