maigesPack (version 1.36.0)

relNet2TGF: Transform Relevance Network analysis in TGF output

Description

This function receive an object of class maigesRelNetB or maigesRelNetM and write TGF files with the relevance networks produced.

Usage

relNet2TGF(...)
"relNet2TGF"(data, dir = "./", filename="group.tgf", corC=NULL, pValue=0.05, ...)
"relNet2TGF"(data, dir = "./", filenames=c("group1.tgf", "group2.tgf", "difPvalue.tgf"), pValue=0.05, ...)

Arguments

data

object of class maigesRelNetB or maigesRelNetM.

dir

character string specifying the folder to save the TGF files.

filename

character string specifying the file name, for objects of class maigesRelNetB.

filenames

character vector of length 3 with the file names to be saved, for objects of class maigesRelNetM.

corC

numeric in [0,1] specifying the cutoff for selecting absolute correlation. May also be 'max' to select the maximum correlation values in a permutation bootstrap strategy, as proposed by Butte et al. (2000).

pValue

numeric in [0,1] specifying the cutoff for selecting correlation values by p-values.

...

additional parameters.

Value

This function don't return any object.

Details

This function only picks the result of the relNetworkB or relNetworkM and display write TGF files. This files are interesting to be used with Yed graph visualisation and editing tool, wrote in Java (http://www.yworks.com/en/products_yed_about.htm).

References

Butte, A.J.; Tamayo, P.; Slonim, D.; Golub, T.R. and Kohane, I.S. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks, PNAS, 97, 12182-12186, 2000 (http://www.pnas.org/cgi/content/full/97/22/12182)

See Also

relNetworkB, relNetworkM, maigesRelNetB, maigesRelNetM.

Examples

## Loading the dataset
data(gastro)

## Constructing the relevance network for sample
## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
## The same is also true for objects of class maigesRelNetB
gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
  samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
  type="Rpearson")

relNet2TGF(gastro.net)