gprege {gprege} | R Documentation |
Gaussian process ranking and estimation of gene expression time-series
Description
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.
Usage
gprege(data, inputs, gpregeOptions)
Arguments
data |
The matrix of gene expression profiles; one profile per row.
|
inputs |
Inputs (timepoints) to the GP.
|
gpregeOptions |
Options list for gprege with fields
- explore
Logical. TRUE operates in a user interactive mode. Used for examining individual gene expression profiles.
- labels
A binary vector. TRUE specifies whether the corresponding profile comes from a differentially expressed gene (usually from a ground truth).
- indexRange
A numeric vector. Range of indices of profiles on which the function should operate. Useful for selective exploration of specific profiles, e.g. only genes marked as differentially expressed in a ground truth list.
- interpolatedT
A numeric vector. New timepoints to interpolate for each profile, based on the estimated function values.
- iters
A scalar. The number of iterations for scaled-conjugate gradients (SCG) optimisation.
- display
Logical. Display gradient and LML information on each SCG iteration.
- inithypers
The matrix of hyperparameter configurations as its rows. Each row has the following format:
[inverse-lengthscale percent-signal-variance percent-noise-variance]
The first row corresponds to a (practically constant) function with a very large lengthscale. Such a function will account for 0 percent of the observed variance in the expression profile (hence 0 for signal) and explain it as noise (hence 1 for noise). Subsequent rows (initialisations for SCG optimisation) correspond to functions of various lengthscales that explain all the observed variance as signal. A reasonable lengthscale would be roughly in line with the time-point sampling intervals.
- exhaustPlotRes
A scalar. The search resolution. Used for interactive mode (explore == 1).
- exhaustPlotLevels
A scalar. Number of contour levels in the exhaustive plot. Used for interactive mode (explore == 1).
- exhaustPlotMaxWidth
A scalar. the maximum lengthscale to search for. Used for interactive mode (explore == 1).
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Value
gpregeOutput |
Output list with fields:
- signalvar
A numeric vector of the vertical lengthscales of the optimised RBF kernel; one for each profile.
- noisevar
A numeric vector. Similar to signalvar, but for the noise hyperparameter.
- width
A numeric vector. Similar to signalvar and noisever, but for the horizontal lengthscales of the RBF.
- LMLs
A numeric vector of log-marginal likelihoods of the GP; one for each profile.
- interpolatedData
A matrix of the extended dataset with interpolated values as the augmenting columns.
- rankingScores
A numeric vector of the ranking scores, based on the log-ratio of marginal likelihoods.
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See Also
gpOptions, gpCreate, gpExpandParam, gpOptimise,
gpExtractParam, gpLogLikelihood, gpPosteriorMeanVar.
Examples
## 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)
[Package
gprege version 1.26.0
Index]