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).

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("gprege")
AuthorAlfredo Kalaitzis <alkalait@gmail.com>
Bioconductor views Bioinformatics DifferentialExpression Microarray Preprocessing TimeCourse
Date of publicationNone
MaintainerAlfredo Kalaitzis <alkalait@gmail.com>
LicenseAGPL-3
Version1.20.0

View on Bioconductor

Files

DESCRIPTION
NAMESPACE
R
R/compareROC.R R/demTp63Gp1.R R/exhaustivePlot.R R/gprege.R R/rocStats.R
build
build/vignette.rds
data
data/DGdat_p63.RData
data/FragmentDellaGattaData.RData
data/FullDellaGattaData.RData
data/datalist
inst
inst/CITATION
inst/NEWS
inst/doc
inst/doc/gprege_quick.R
inst/doc/gprege_quick.Rnw
inst/doc/gprege_quick.bib
inst/doc/gprege_quick.pdf
man
man/DGdat_p63.Rd man/DellaGattaData.Rd man/compareROC.Rd man/demTp63Gp1.Rd man/exhaustivePlot.Rd man/gprege-package.Rd man/gprege.Rd man/rocStats.Rd
vignettes
vignettes/gprege_quick.Rnw
vignettes/gprege_quick.bib

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