samr.compute.siggenes.table: Compute significant genes table
In samr: SAM: Significance Analysis of Microarrays

Description Usage Arguments Value Author(s) References Examples

Computes significant genes table, starting with samr object "samr.obj" and delta.table "delta.table"

1 2	samr.compute.siggenes.table(samr.obj, del, data, delta.table, min.foldchange=0, all.genes=FALSE, compute.localfdr=FALSE)

`samr.obj`	Object returned from call to samr
`del`	Value of delta to define cutoff rule
`data`	Data object, same as that used in call to samr
`delta.table`	Object returned from call to samr.compute.delta.table
`min.foldchange`	The minimum fold change desired; should be >1; default is zero, meaning no fold change criterion is applied
`all.genes`	Should all genes be listed? Default FALSE
`compute.localfdr`	Should the local fdrs be computed (this can take some time)? Default FALSE

return(list(genes.up=res.up, genes.lo=res.lo, color.ind.for.multi=color.ind.for.multi, ngenes.up=ngenes.up, ngenes.lo=ngenes.lo))

`genes.up`	Matrix of significant genes having posative correlation with the outcome. For survival data, genes.up are those genes having positive correlation with risk- that is, increased expression corresponds to higher risk (shorter survival).
`genes.lo`	Matrix of significant genes having negative correlation with the outcome. For survival data,genes. lo are those whose increased expression corresponds to lower risk (longer survival).
`color.ind.for.multi`	For multiclass response: a matrix with entries +1 if the class mean is larger than the overall mean at the 95 levels, -1 if less, and zero otehrwise. This is useful in determining which class or classes causes a feature to be significant
`ngenes.up`	Number of significant genes with positive correlation
`ngenes.lo`	Number of significant genes with negative correlation

Balasubrimanian Narasimhan and Robert Tibshirani

Tusher, V., Tibshirani, R. and Chu, G. (2001): Significance analysis of microarrays applied to the ionizing radiation response" PNAS 2001 98: 5116-5121, (Apr 24). http://www-stat.stanford.edu/~tibs/sam

#generate some example data
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))

data=list(x=x,y=y, geneid=as.character(1:nrow(x)),
genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)


samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=100)

delta.table<-samr.compute.delta.table(samr.obj)
del<- 0.3
siggenes.table<- samr.compute.siggenes.table(samr.obj, del, data, delta.table)