isobar (version 1.18.0)

NoiseModel-class: NoiseModel objects

Description

A NoiseModel represent the technical variation which is dependent on signal intensity.

Constructor

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.

Accessor methods

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.

Examples

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))