classifyKNNsc {maigesPack} | R Documentation

## Function to do discrimination analysis, by the search and choose method

### 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 k nearest neighbours method. This function uses the search and choose method.

### Usage

    classifyKNNsc(obj=NULL, sLabelID="Classification", func="wilcox.test",
                  facToClass=NULL, gNameID="GeneName", geneGrp=1, path=NULL,
                  nGenes=3, cliques=100, kn=5)

### Arguments

`obj` | object of class `maiges` to search the classifiers.
`sLabelID` | character string with the identification of the sample label to be used.
`func` | string specifying the function to be used to search by the initial one-dimensional classifiers, like 'wilcox.test' or 't.test'.
`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.
`cliques` | integer specifying the number of cliques or classifiers to be generated.
`kn` | number of neighbours for the knn method.

### Details

This function implements the method known as Search and choose proposed by Cristo (2003). If you want to use an exhaustive search use the function `classifyKNN`.

This function uses the function `knn.cv` from package class to construct k-nearest neighbour classifiers. It is possible to use the functions `classifyLDAsc` and `classifySVMsc` to search by classifiers using Fisher's linear discriminant analysis and support vector machinnes, 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>

### References

Cristo, E.B. Metodos Estatisticos na Analise de Experimentos de Microarray. Masther's thesis, Instituto de Matematica e Estatistica - Universidade de Sao Paulo, 2003 (in portuguese).

### See Also

`knn.cv`, `classifyKNN`, `classifyLDAsc` and `classifySVMsc`.

### Examples

    ## Loading the dataset
    data(gastro)

    ## Doing KNN classifier with 2 genes for the 6th gene group comparing
    ## the 2 categories from 'Type' sample label.
    gastro.class = classifyKNNsc(gastro.summ, sLabelID="Type",
      gNameID="GeneName", nGenes=2, geneGrp=1, cliques=10)
    gastro.class

    ## To do classifier with 3 genes for the 6th gene group comparing
    ## normal vs adenocarcinomas from 'Tissue' sample label
    gastro.class = classifyKNNsc(gastro.summ, sLabelID="Tissue",
      gNameID="GeneName", nGenes=3, geneGrp=1, cliques=10,
      facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

[Package maigesPack version 1.4.0 Index]