rHVDM (version 1.38.0)

training: Performs the HVDM training step and returns a list containing the results

Description

This method performs the training step of the HVDM algorithm. It returns a list that will then be used in the subsequent screening step.

Usage

training(eset,genes,transforms,degrate,actname,pdata,forcetransforms)

Arguments

eset

an ExpressionSet object (Biobase)

genes

a vector containg the gene identifiers of the training genes

transforms

a vector containing the kintetic parameter identifiers that have to be transformed during optimisation (optional)

degrate

value of the anchoring gene degradation rate, expressed in inverse unit time (optional)

pdata

a data frame, phenoData to be used for the training (optional)

actname

name of the transcription factor of interest (optional)

forcetransforms

Boolean, whether the transformation in argument transforms have to be applied

Value

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

Details

The first entry in the genes vector is the anchoring gene. This means that the sensitivity (Sj) for this genes is set at 1.0 by default and that if a degradation rate is supplied it applies to that gene.

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, set the forcetransforms switch to FALSE. 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-tranforms are implemented (ie "exp").

The degrate argument is optional, but it is recommended to provide the algorithm with an externally measured degradation rate, as this greatly improves the accuracy and robustness of the outcome.

The pdata argument is also optional. By default the method will use the phenoData contained in the expression set. This argument can be used for excluding a time point, or an entire replicate. To extract the phenoData from the expression set, use dataframe<-pData(eset). The dataframe object obtained can then be manipulated as desired.

The default name of the transcription factor is "trfact1".

References

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.

See Also

HVDMcheck,screening,fitgene,HVDMreport

Examples

data(HVDMexample)
tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")