varSel.highest.var {MCRestimate} | R Documentation |
Different functions for a variable selection and clustering methods. These functions are mainly used for the function MCRestimate
identity(sample.gene.matrix,classfactor,...) varSel.highest.t.stat(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=500,...) varSel.highest.var(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=2000,...) varSel.AUC(sample.gene.matrix, classfactor, theParameter=NULL,var.numbers=200,...) cluster.kmeans.mean(sample.gene.matrix,classfactor,theParameter=NULL,number.clusters=500,...) varSel.removeManyNA(sample.gene.matrix,classfactor, theParameter=NULL, NAthreshold=0.25,...) varSel.impute.NA(sample.gene.matrix ,classfactor,theParameter=NULL,...)
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 |
Parameter that depends on the function. For
'cluster.kmeans.mean' eighter NULL or an output of the function
kmeans . If it is NULL then kmeans will be used to
form clusters of the genes. Otherwise the already existing clusters
will be used. In both ways there will be a calculation of the
metagene intensities afterwards. For the other functions eighter
NULL or a logical vector which indicates for every gene if it sould
be left out from further analysis or not |
number.clusters |
parameter which specifies the number of clusters |
var.numbers |
some methods needs an argument which specifies how many variables should be taken |
NAthreshold |
integer- if the percentage of the NA is higher than this threshold the variable will be deleted |
... |
Further parameters |
metagene.kmeans.mean
performes a kmeans clustering with
a number of clusters specified by 'number clusters' and takes the mean
of each cluster. varSel.highest.var
selects a number (specified
by 'var.numbers') of variables with the highest variance. varSel.AUC
chooses the
most discriminating variables due to the AUC criterium (the
library ROC
is required).
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. |
Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de
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)