Function to do discrimination analysis

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Description

Function to search by groups of few genes, also called cliques, that can discriminate (or classify) between two distinct biological sample types, using the Support Vector Machinne method. This function uses exhaustive search.

Usage

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classifySVM(obj=NULL, sLabelID="Classification", facToClass=NULL,
            gNameID="GeneName", geneGrp=1, path=NULL, nGenes=3)

Arguments

obj

object of class maiges to search the classifiers.

sLabelID

character string with the identification of the sample label to be used.

facToClass

named list with 2 character vectors specifying the samples to be compared. If NULL (default) the first 2 types of sLabelID are used.

gNameID

character string with the identification of gene label ID.

geneGrp

character or integer specifying the gene group to be tested (colnames of GeneGrps slot). If both geneGrp and path are NULL all genes are used. Defaults to 1 (first group).

path

character or integer specifying the gene network to be tested (names of Paths slot). If both geneGrp and path are NULL all genes are used. Defaults to NULL.

nGenes

integer specifying the number of genes in the clique, or classifier.

Details

Pay attention with the arguments geneGrp and path, if both of them is NULL an exhaustive search for all dataset will be done, and this search may be extremely computational intensive, which may result in a process running during some weeks or months depending on the number of genes in your dataset.

If you want to construct classifiers from a group of several genes, the search and choose (SC) method may be an interesting option. It is implemented in the function classifySVMsc. This method uses the function svm from package e1071 to search classifiers by Support Vector Machines. The functions classifyLDA and classifyKNN were also dedined to construct classifiers by Fisher's linear discriminant analysis ans k-neighbours, respectively.

Value

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

Author(s)

Elier B. Cristo, adapted by Gustavo H. Esteves <gesteves@vision.ime.usp.br>

See Also

svm, classifySVMsc, classifyLDA and classifyKNN.

Examples

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## Loading the dataset
data(gastro)

## Doing SVM classifier with 2 genes for the 6th gene group comparing
## the 2 categories from 'Type' sample label.
gastro.class = classifySVM(gastro.summ, sLabelID="Type",
  gNameID="GeneName", nGenes=2, geneGrp=6)
gastro.class

## To do classifier with 3 genes for the 6th gene group comparing
## normal vs adenocarcinomas from 'Tissue' sample label
gastro.class = classifySVM(gastro.summ, sLabelID="Tissue",
  gNameID="GeneName", nGenes=3, geneGrp=6,
  facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

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