demTp63Gp1

(Logical) TRUE downloads and uses the full dataset.

fulldataset


  Demo script of Gaussian Process Regression and Estimation of Gene Expression on TP63 time-series data (see gprege.m).
  See Kalaitzis & Lawrence (2011) for a detailed discussion of the ranking algorithm and dataset used.


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).

demTp63Gp1: gprege on TP63 expression time-series.

Description

Usage

Arguments

See Also

Examples