Description Usage Arguments Value See Also Examples
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). The other GP has fixed hyperparameters with a 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). See Kalaitzis & Lawrence (2011) for a detailed discussion of the ranking algorithm and dataset used.
1 |
data |
The matrix of gene expression profiles; one profile per row. |
inputs |
Inputs (timepoints) to the GP. |
gpregeOptions |
Options list for gprege with fields
|
gpregeOutput |
Output list with fields:
|
gpOptions, gpCreate, gpExpandParam, gpOptimise,
gpExtractParam, gpLogLikelihood, gpPosteriorMeanVar.
1 2 3 4 5 6 7 8 9 10 11 12 | ## see demTp63Gp1.R
data(FragmentDellaGattaData) ## Load demo data.
## Setup other gprege options.
gpregeOptions = list(indexRange=(1:2), explore=TRUE, exhaustPlotRes=30, exhaustPlotLevels=10,
exhaustPlotMaxWidth=100, iters=100, labels=DGatta_labels_byTSNI, display=FALSE)
## Matrix of different hyperparameter configurations as rows:
## [inverse-lengthscale percent-signal-variance percent-noise-variance].
gpregeOptions$inithypers <- matrix( c(
1/1000, 1e-3, 0.999
,1/20, 0.999, 1e-3
), ncol=3, byrow=TRUE)
gpregeOutput <- gprege(data=exprs_tp63_RMA, inputs=matrix(seq(0,240,by=20), ncol=1), gpregeOptions=gpregeOptions)
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