e09-NoiseModel-class: The "NoiseModel" Class

Description Usage Arguments Details Methods Author(s) References Examples

Description

A NoiseModel represents the additional machine noise that is layered on top of any biological variabilty when measuring the gene expression in a set of samples.

Usage

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NoiseModel(nu, tau, phi)
## S4 method for signature 'NoiseModel'
blur(object, x, ...)

Arguments

nu

The mean value for the additive noise

tau

The standard deviation for the additive noise

phi

The standard deviation for the multiplicative noise. Note the mean of multiplicative noise is set to 0.

object

object of class NoiseModel

x

The data matrix containing true signal from the gene expression

...

extra arguments affecting blur applied

Details

We model both additive and multiplicative noise, so that the observed expression of gene g in sample i is given by: Y_gi = S_gi exp(H_gi) + E_gi, where Y_gi = observed expression, S_gi = true bilogical signal, H_gi ~ N(0, phi) defines the multiplicative noise, and E_gi ~ N(nu,tau) defines the additive noise. Note that we allow a systematic offset/bias in the additive noise model.

Methods

blur(object, x, ...)

Adds and multiplies random noise to the data matrix x containing the true signal from the gene expression.

Author(s)

Kevin R. Coombes [email protected], Jiexin Zhang [email protected],

References

Zhang J, Coombes KR.
Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups.
BMC Bioinformatics. 2012; 13 Suppl 13:S1.

Examples

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showClass("NoiseModel")
nComp <- 10
nGenes <- 100
comp <- list()
for (i in 1:nComp){
  comp[[i]] <- IndependentLogNormal(rnorm(nGenes/nComp, 6, 1.5),
                                    1/rgamma(nGenes/nComp, 44, 28))
}
myEngine <- Engine(comp)
myData <- rand(myEngine, 5)
summary(myData)

nu <- 10
tau <- 20
phi <- 0.1
nm <- NoiseModel(nu, tau, phi)
realData <- blur(nm, myData)
summary(realData)

Umpire documentation built on July 12, 2017, 3:01 a.m.