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

`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 |

`object` |
object of class |

`x` |
The data matrix containing true signal from the gene expression |

`...` |
extra arguments affecting blur 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 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.

- blur(object, x, ...)
Adds and multiplies random noise to the data matrix

`x`

containing the true signal from the gene expression.

Kevin R. Coombes krc@silicovore.com, Jiexin Zhang jiexinzhang@mdanderson.org, P. Roebuck proebuck@mdanderson.org

OOMPA

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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