Gene set enrichment for two or three categories

Description

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.

Author(s)

Markus Boenn Maintainer: Markus Boenn <markus.boenn@ufz.de>

Examples

##
## 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", fact=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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(geecc)
geecc 1.6.0 loaded
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/geecc/geecc-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: geecc-package
> ### Title: Gene set enrichment for two or three categories
> ### Aliases: geecc-package geecc
> ### Keywords: package
> 
> ### ** Examples
> 
> ##
> ## 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", fact=CatList)
> ConCub.obj.2 <- runConCub( obj=ConCub.obj, filter=ConCubFilter.obj, nthreads=1 )
Testing: counts ~ deg + go + len (mi)

> ConCub.obj.3 <- filterConCub( obj=ConCub.obj.2, filter=ConCubFilter.obj )
Dimension before filtering: deg=2, go=4, len=10
Dimension after filtering: deg=2, go=4, len=10
> plotConCub( obj=ConCub.obj.3, filter=ConCubFilter.obj )
> x <- getTable(ConCub.obj.3)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>