gprege

DOI: 10.18129/B9.bioc.gprege    

Gaussian Process Ranking and Estimation of Gene Expression time-series

Bioconductor version: Release (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>

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")

 

PDF	R Script	gprege Quick Guide
PDF		Reference Manual
Text		NEWS

Details

biocViews	Bioinformatics, DifferentialExpression, Microarray, Preprocessing, Software, TimeCourse
Version	1.38.0
In Bioconductor since	BioC 2.10 (R-2.15) (10 years)
License	AGPL-3
Depends	R (>= 2.10), gptk
Imports
LinkingTo
Suggests	spam
SystemRequirements
Enhances
URL
BugReports	alkalait@gmail.com
Depends On Me
Imports Me
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Build Report

Package Archives

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

Source Package	gprege_1.38.0.tar.gz
Windows Binary	gprege_1.38.0.zip
macOS 10.13 (High Sierra)	gprege_1.38.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/gprege
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/gprege
Package Short Url	https://bioconductor.org/packages/gprege/
Package Downloads Report	Download Stats
Old Source Packages for BioC 3.14	Source Archive