classifyKNN {maigesPack} | R Documentation |
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.
classifyKNN(obj=NULL, sLabelID="Classification", facToClass=NULL, gNameID="GeneName", geneGrp=1, path=NULL, nGenes=3, kn=5)
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. |
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.
The result of this function is an object of class maigesClass
.
Elier B. Cristo, adapted by Gustavo H. Esteves <gesteves@vision.ime.usp.br>
knn.cv
, classifyKNNsc
,
classifyLDA
, classifySVM
.
## 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")))