samr.compute.siggenes.table {samr}R Documentation

Compute significant genes table

Description

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

Usage

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))

gene.up Matrix of significant genes having posative correlation with the outcome
gene.lo Matrix of significant genes having negative correlation with the outcome
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

#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)


[Package samr version 1.26 Index]