fitgene.lin: Fits the optimal kinetic parameter values for a particular...
In rHVDM: Hidden Variable Dynamic Modeling

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

This method fits the three kinetic parameter values for a particular gene. It returns a list containing the results.

1	fitgene.lin(eset,gene,tHVDM,transforms,firstguess)

`eset`	an ExpressionSet object (Biobase)
`gene`	the gene identifier in character format
`transforms`	a vector containing the kinetic parameter identifiers that have to be transformed during optimisation (optional)
`tHVDM`	the output of the training set
`firstguess`	first guess for the fitting (optional, see details)

An exponential transform is set by default for both the basal (Bj) and degradation (Dj) rates (through the transforms argument). This forces the values for both these parameters to be positive. It also helps to reach a better fit. To turn this off let transforms=c(). Even in this case the degradation rate will not be allowed to take non positive values as it causes problems with the differential operator used internally. The value in the vector indicates the parameter to be transformed: "Bj": basal rate of transcription, "Sj": sensitivity, "Dj": degrdation rate. The entry label indicates the transform to be applied; presently, only log-transforms are implemented (ie "exp").

This fitgene() step can only be applied after a training() step. The output to the training() step has to be fed through the tHVDM argument.

The firstguess argument is optional (a first guess is generated internally by default). However a first guess can be supplied by the user which can take several forms. It can either be a vector with three entries containing a first guess for the basal rate, the sensitivity, the degradation rate (in that order). Alternatively, another output from the fitgene() function (for example from a gene that has a similar expression profile) can be supplied as a firstguess argument.

a list containing the results (see documentation for more details).

Obviously, the expression set given as a eset argument has to be the same as the one used for the training step.

Martino Barenco

M. Barenco, D. Tomescu, D. Brewer, R. Callard, J. Stark, M. Hubank (2006) Ranked predictions of p53 targets using Hidden Variable Dynamic Modelling. Genome Biology, V7(3), R25.

training,screening,HVDMreport

1
2
3

data(HVDMexample)
tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")
sHVDMcd38<-fitgene.lin(eset=fiveGyMAS5,gene="205692_s_at",tHVDM=tHVDMp53)