varSel.highest.var.eSRG {MCRestimate}R Documentation

Variable selection and cluster functions to work with MCRestimate.exprSetRG

Description

Different functions for a variable selection and clustering methods. These functions are used for the function MCRestimate.exprSetRG

Usage

       varSel.highest.t.stat.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=500,...)

       varSel.highest.var.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=2000,...)

       varSel.green.int.max.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,lambda=0.5,...)
       varSel.green.int.sec.eSRG(sample.gene.matrix,classfactor,theParameter=NULL, lambda=0.5,...)

Arguments

sample.gene.matrix a matrix in which the rows corresponds to genes and the colums corresponds to samples
classfactor a factor containing the values that should be predicted
theParameter Either NULL or a logical vector which indicates for every gene if it should be left out from further analysis or not
var.numbers some methods needs an argument which specifies how many variables should be taken
lambda additional parameter for some methods
... Further parameters

Details

varSel.highest.var.eSRG selects a number (specified by 'var.numbers') of variables with the highest variance. varSel.highest.t.stat.eSRG selects variables with highest t statistics. varSel.green.int.max.eSRG and varSel.green.int.sec.eSRG perform a selection based on the green intensity channel. All functions only work with MCRestimate.exprSetRG.

Value

Every function returns a list consisting of two arguments:

matrix the result matrix of the variable redution or the clustering
parameter The parameter which are used to reproduce the algorithm, i.e. a vector which indicates for every gene if it will be left out from further analysis or not if a gene reduction is performed or the output of the function kmeans for the clustering algorithm.

Author(s)

Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de

See Also

MCRestimate

Examples

library(MCRestimate)
m <- matrix(c(rnorm(10,2,0.5),rnorm(10,4,0.5),rnorm(10,7,0.5),rnorm(10,2,0.5),rnorm(10,4,0.5),rnorm(10,2,0.5)),ncol=2)
cluster.kmeans.mean(m ,number.clusters=3)

[Package MCRestimate version 1.10.5 Index]