NoiseModel-class {isobar} | R Documentation

## 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))

[Package isobar version 1.33.0 Index]