classifyKNN: Function to do discrimination analysis

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

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 neighbourhood method. This function uses exhaustive search.

Usage

classifyKNN(obj=NULL, sLabelID="Classification", facToClass=NULL,
            gNameID="GeneName", geneGrp=1, path=NULL, nGenes=3, 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.
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.
kn	number of neighbours for the knn method.

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 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 classifyKNNsc. This function uses the function knn.cv from package class to construct k-nearest neighbour classifiers. It possible to use functions classifyLDA or classifySVM to construct classifiers using Fisher's linear discriminant analysis or support vector machines methods, respectively.

Value

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

Author(s)

Elier B. Cristo, adapted by Gustavo H. Esteves <[e-mail]>

See Also

knn.cv, classifyKNNsc, classifyLDA, classifySVM.

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 = classifyKNN(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 = classifyKNN(gastro.summ, sLabelID="Tissue",
  gNameID="GeneName", nGenes=3, geneGrp=6,
  facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

maigesPack documentation built on Nov. 8, 2020, 6:23 p.m.