NoiseModel-class {Umpire} | R Documentation |
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 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.
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)