R: Creates features from a GSA analysis that can be used in...

GSA.make.features {GSA} | R Documentation

## Creates features from a GSA analysis that can be used in other procedures

### Description

Creates features from a GSA analysis that can be used in other procedures, for example, sample classification.

### Usage

    GSA.make.features(GSA.func.obj, x, genesets, genenames)

### Arguments

`GSA.func.obj` | Object returned by GSA.func
`x` | Expression dataset from which the features are to be created
`genesets` | Gene set collection
`genenames` | Vector of gene names in expression dataset

### Details

Creates features from a GSA analysis that can be used in other procedures, for example, sample classification. For example, suppose the GSA analysis computes a maxmean score for gene set 1 that is positive, based on the mean of the positive part of the scores in that gene set. Call the subset of genes with positive scores "A". Then we compute a new feature for this geneset, for each sample, by computing the mean of the scores for genes in A, setting other gene scores to zero.

### Author(s)

Robert Tibshirani

### References

Efron, B. and Tibshirani, R. On testing the significance of sets of genes. Stanford tech report rep 2006. http://www-stat.stanford.edu/~tibs/ftp/GSA.pdf

### Examples

    ######### two class unpaired comparison
    # y must take values 1,2

    set.seed(100)
    x<-matrix(rnorm(1000*20),ncol=20)
    dd<-sample(1:1000,size=100)

    u<-matrix(2*rnorm(100),ncol=10,nrow=100)
    x[dd,11:20]<-x[dd,11:20]+u
    y<-c(rep(1,10),rep(2,10))

    genenames=paste("g",1:1000,sep="")

    #create some random gene sets
    genesets=vector("list",50)
    for(i in 1:50){
     genesets[[i]]=paste("g",sample(1:1000,size=30),sep="")
    }
    geneset.names=paste("set",as.character(1:50),sep="")

    GSA.func.obj<-GSA.func(x,y, genenames=genenames, genesets=genesets,  resp.type="Two class unpaired")

    GSA.make.features(GSA.func.obj, x, genesets, genenames)

[Package GSA version 1.03 Index]