geecc-package {geecc} | R Documentation |
This package performs gene set enrichment analyses considering two or three categories. Categories might be regulated genes, sequence length, GC content, GO terms, KEGG pathways and so on.
Markus Boenn Maintainer: Markus Boenn <markus.boenn@ufz.de>
## ## a completely artificial example run ## through the routines of the package ## R <- 500 #generate R random gene-ids ID <- sapply(1:R, function(r){paste( sample(LETTERS, 10), collapse="" ) } ) ID <- unique(ID) #assign artificial differentially expressed genes randomly category1 <- list( deg.smallFC=sample(ID, 100, rep=FALSE), deg.hughFC=sample(ID, 100, rep=FALSE) ) #assign artificial GO terms of genes randomly category2 <- list( go1=sample(ID, 50, replace=FALSE), go2=sample(ID, 166, replace=FALSE), go3=sample(ID, 74, replace=FALSE), go4=sample(ID, 68, replace=FALSE) ) #assign artificial sequence length of genes randomly LEN <- setNames(sample(seq(100, 1000, 100), length(ID), replace=TRUE), ID) category3 <- split( ID, f=factor(LEN, levels=seq(100, 1000, 100)) ) CatList <- list(deg=category1, go=category2, len=category3) ConCubFilter.obj <- new("concubfilter", names=names(CatList)) ConCub.obj <- new("concub", categories=CatList) ConCub.obj.2 <- runConCub( obj=ConCub.obj, filter=ConCubFilter.obj, nthreads=1 ) ConCub.obj.3 <- filterConCub( obj=ConCub.obj.2, filter=ConCubFilter.obj ) plotConCub( obj=ConCub.obj.3, filter=ConCubFilter.obj ) x <- getTable(ConCub.obj.3)