Description Constructor Accessor methods Examples
A NoiseModel represent the technical variation which is dependent on signal intensity.
new(type,ibspectra,reporterTagNames=NULL,one.to.one=TRUE,min.spectra=10,plot=FALSE,
pool=FALSE)
:Creates a new NoiseModel object based on ibspectra object.
type
:A non-virtual class deriving from NoiseModel:
ExponentialNoiseModel
, ExponentialNoANoiseModel
,
InverseNoiseModel
, InverseNoANoiseModel
reporterTagNames
:When NULL, all channels from ibspectra are taken
(i.e. sampleNames(ibspectra)
). Otherwise, specify
subset of names, or a matrix which defines the desireed combination of channels (nrow=2).
one.to.one
:Set to false to learn noise model one a non one-to-one dataset
min.spectra
:When one.to.one=FALSE, only take proteins with min.spectra to learn noise model.
plot
:Set to true to plot data the noise model is learnt on.
pool
:If false, a NoiseModel is estimated on each combination of channels indivdually, and then the parameters are averaged. If true, the ratios of all channels are pooled and then a NoiseModel is estimated.
noiseFunction
:Gets the noise function.
parameter
:Gets and sets the parameters for the noise function.
variance
:Gets the variance for data points based on the noise function and parameters.
stddev
:Convenience function, sqrt(variance(...))
.
lowIntensity
:Gets and sets the low intensity slot, denoting the noise region.
naRegion
:Gets and sets the na.region slot.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(ibspiked_set1)
ceru.proteins <- protein.g(proteinGroup(ibspiked_set1),"CERU")
# normalize
ibspiked_set1 <- normalize(correctIsotopeImpurities(ibspiked_set1))
# remove spiked proteins
ibspiked_set1.noceru <- exclude(ibspiked_set1,ceru.proteins)
ibspiked_set1.justceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.proteins,direction="include")
# learn noise models
nm.i <- new("InverseNoiseModel",ibspiked_set1.noceru)
nm.e <- new("ExponentialNoiseModel",ibspiked_set1.noceru)
#learn on non-one.to.one data: not normalized, with spiked proteins
nm.n <- new("ExponentialNoiseModel",ibspiked_set1.justceru,one.to.one=FALSE)
maplot(ibspiked_set1,noise.model=c(nm.e,nm.i,nm.n),ylim=c(0.1,10))
|
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