e30-clinicalNoiseModel: A Noise Model for Clinical Data
In Umpire: Simulating Realistic Gene Expression and Clinical Data

Description Usage Arguments Details Value Author(s) See Also Examples

A ClinicalNoiseModel represents the additional human and measurement noise that is layered on top of any biological variabilty when measuring clinical variables.

1	ClinicalNoiseModel(nFeatures, shape = 1.02, scale = 0.05/shape)

`nFeatures`	An integer; the number of additive scale parameters to sample from the gamma distribution.
`shape`	The `shape` gamma hyperparameter describing the standard deviation of additive noise.
`scale`	The `scale` gamma hyperparameter describing the standard deviation of additive noise.

We model both additive and multiplicative noise, so that the observed expression of clinical variable c in sample i is given by: Y_ci = S_ci + E_ci, where Y_ci = observed expression, S_ci = true biological signal. In the ClinicalNoiseModel (as opposed to the NoiseModel), we model the additive noise as E_ci ~ N(0,tau), without multiplicative noise or an additive bias/offset in the clinical model. The standard deviation hyperparameters of the additive noise tau is modeled by the gamma distribution tau ~ Gamma(shape, scale)

An object of class NoiseModel.

Kevin R. Coombes krc@silicovore.com, Caitlin E. Coombes caitlin.coombes@osumc.edu

NoiseModel-class,

showClass("NoiseModel")

## generate a ClinicalEngine with 20 features and 4 clusters
ce <- ClinicalEngine(20, 4, TRUE)
## generate 300 simulated patients
set.seed(194718)
dset <- rand(ce, 300)

cnm <- ClinicalNoiseModel(nrow(ce@localenv$eng), shape=2, scale=0.1)
cnm

noisy <- blur(cnm, dset$data)
hist(noisy)