Different functions for a variable selection and clustering methods. These functions are mainly used for the function MCRestimate
1 2 3 4 5 6 7 8 9 10  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' either NULL or an output of the function

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
performs 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 reduction 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
MCRestimate
1 2 
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.