Package: gprege
Version: 1.32.0
Date: 2016-10-07
Title: Gaussian Process Ranking and Estimation of Gene Expression
        time-series
Authors@R: c(person(c("Alfredo"), "Kalaitzis", role =
        c("aut","cre","trl"), email = "alkalait@gmail.com"))
Author: Alfredo Kalaitzis <alkalait@gmail.com>
Maintainer: Alfredo Kalaitzis <alkalait@gmail.com>
Depends: R (>= 2.10), gptk
Suggests: spam
Description: 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).
License: AGPL-3
biocViews: Microarray, Preprocessing, Bioinformatics,
        DifferentialExpression, TimeCourse
URL:
BugReports: alkalait@gmail.com
Packaged: 2020-04-28 00:36:14 UTC; biocbuild
git_url: https://git.bioconductor.org/packages/gprege
git_branch: RELEASE_3_11
git_last_commit: 479ebc3
git_last_commit_date: 2020-04-27
Date/Publication: 2020-04-27
NeedsCompilation: no
Built: R 4.0.0; ; 2020-05-09 07:10:17 UTC; unix
