samr.compute.siggenes.table {samr} | R Documentation |
Computes significant genes table, starting with samr object "samr.obj" and delta.table "delta.table"
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))
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 |
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)