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 Fisher's linear discriminant analysis. This function uses the search and choose method.

Usage

1
2
3
classifyLDAsc(obj=NULL, sLabelID="Classification", func="wilcox.test",
              facToClass=NULL, gNameID="GeneName", geneGrp=1, path=NULL,
              nGenes=3, cliques=100, sortBy="cv")

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.

sortBy

character string with the field to be sorted. May be 'cv' (default) or 'svd'.

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 classifyLDA.

This method uses the function lda from package MASS to search by classifiers using Fisher's linear discriminant analysis. It is possible to search classifiers by Support Vector Machines and k-nearest neighbour classifiers using the functions classifySVMsc and classifyKNNsc, 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

lda, classifyLDA, classifySVMsc and classifyKNNsc.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## Loading the dataset
data(gastro)

## Doing LDA classifier with 2 genes for the 6th gene group comparing
## the 2 categories from 'Type' sample label.
gastro.class = classifyLDAsc(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 = classifyLDAsc(gastro.summ, sLabelID="Tissue",
  gNameID="GeneName", nGenes=3, geneGrp=1, cliques=10,
  facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.