
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.
NoiseModel(nu, tau, phi)
# S4 method for NoiseModel
blur(object, x, ...)
# S4 method for NoiseModel
summary(object, ...)
The mean value for the additive noise
The standard deviation for the additive noise
The standard deviation for the multiplicative noise. Note that
the mean of the multiplicative noise is set to 0
.
object of class NoiseModel
The data matrix containing true signal from the gene expression
extra arguments affecting the blur method applied
Adds and multiplies random noise to the
data matrix x
containing the true signal from the gene expression.
Prints a summary of the object.
Kevin R. Coombes krc@silicovore.com, Jiexin Zhang jiexinzhang@mdanderson.org,
We model both additive and multiplicative noise, so that the observed
expression of gene g in sample i is given by:
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.
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)
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