gprege-package


  This package implements the method of Kalaitzis and Lawrence (2011)
  for Gaussian process modelling gene expression time-series data. The method can be
  used to filter quiet genes and quantify differential expression in time-series expression ratios.


 package

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).

gprege-package: gprege - Gaussian Process Ranking and Estimation of Gene Expression.

Description

Arguments

Details

References

See Also

Examples