Compute significant genes table

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Description

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

Usage

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

Arguments

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

Value

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

Author(s)

Balasubrimanian Narasimhan and Robert Tibshirani

References

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

Examples

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#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)