Description Usage Arguments Details Methods Author(s) References Examples
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.
1 2 3 4 5 | NoiseModel(nu, tau, phi)
## S4 method for signature 'NoiseModel'
blur(object, x, ...)
## S4 method for signature 'NoiseModel'
summary(object, ...)
|
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 that
the mean of the multiplicative noise is set to |
object |
object of class |
x |
The data matrix containing true signal from the gene expression |
... |
extra arguments affecting the blur method applied |
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 biological 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.
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,
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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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