relNetworkM: Relevance Network analysis
In maigesPack: Functions to handle cDNA microarray data, including several methods of data analysis

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

Function to construct Relevance Networks comparing two distinct biological types.

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relNetworkM(data=NULL, gLabelID="GeneName", sLabelID="Classification",
            geneGrp=NULL, path=NULL, samples=NULL,
            type="Rpearson", ...)

`data`	object of class `maiges`.
`gLabelID`	character string giving the identification of gene label ID.
`sLabelID`	character string giving the identification of sample label ID.
`geneGrp`	character string (or numeric index) specifying the gene group to calculate the correlation values between them. If NULL (together with path) all genes are used.
`path`	character string (or numeric index) specifying the gene network to calculate the correlation values between them. If NULL (together with geneGrp) all genes are used.
`samples`	a named list with two character vectors specifying the two groups that must be compared.
`type`	type of correlation to be calculated. May be 'Rpearson' (default), 'pearson', 'kendall' or 'spearman'.
`...`	additional parameters for functions `robustCorr` or `cor`.

This method uses the function cor to calculate the usual correlation values or robustCorr to calculate a robust correlation using an idea similar to the leave-one-out. The correlation values are calculated for pairs of genes in the two groups specified by the argument samples, then a Fisher's Z transformation are done to calculate the significance for the difference between the two correlation values, this is implemented in the function compCorr. This method was first used in the work from Gomes et al. (2005).

The result of this function is an object of class maigesRelNetM.

Gustavo H. Esteves <gesteves@vision.ime.usp.br>

Gomes, L.I.; Esteves, G.H.; Carvalho, A.F.; Cristo, E.B.; Hirata Jr., R.; Martins, W.K.; Marques, S.M.; Camargo, L.P.; Brentani, H.; Pelosof, A.; Zitron, C.; Sallum, R.A.; Montagnini, A.; Soares, F.A.; Neves, E.J. & Reis, L.F. Expression Profile of Malignant and Nonmalignant Lesions of Esophagus and Stomach: Differential Activity of Functional Modules Related to Inflammation and Lipid Metabolism, Cancer Research, 65, 7127-7136, 2005 (http://cancerres.aacrjournals.org/cgi/content/abstract/65/16/7127)

cor, robustCorr compCorr, maigesRelNetM, plot.maigesRelNetM, image.maigesRelNetM.

## Loading the dataset
data(gastro)

## Constructing the relevance network for sample
## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
  samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
  type="Rpearson")