I am trying to perform a GAGE analysis on a preranked list of PCA loadings. The PCA is performed on microarray data and the PC1 explains most of the variance among the samples. Thus, I would expect to obtain some gene sets o pathways enriched for this component. 

Here's the code I use:

xx <- prcomp(t(x), scale=TRUE) ## PCA on xx, a microarrray matrix

pc<- xx$rotation[,1, drop=F]## subset and order loadings of the first component
pc.ord<- pc[order(pc[,1]),, drop=F] 

datakegg.gs)
kg.mmu=kegg.gsets("mmu")
kg.mmu<-kegg.gsets(species = "mmu", id.type = "entrez")
kegg.gs=kg.mmu$kg.sets[kg.mmu$sigmet.idx]
kegg.gs=kg.mmu$kg.sets

pc.kegg.p <- gage(pc.ord, gsets = kegg.gs,ref = NULL, samp = NULL, same.dir = F,rank.test = T)

For pathways and GO analysis I only obtain NAs.

I you have any suggestions I would really appreciate.

Thank you,
Marco