maigesPack (version 1.36.0)

relNetworkM: Relevance Network analysis

Description

Function to construct Relevance Networks comparing two distinct biological types.

Usage

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

Arguments

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.

Value

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

Details

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).

References

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)

See Also

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

Examples

## 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")