CLEAN-package: Clustering Enrichment Analysis

In uc-bd2k/CLEAN: Clustering Enrichment Analysis

Clustering Enrichment Analysis

Description

Given an hierarchical gene clustering and a list of functional categories, this package performs functional enrichment analysis of all possible clusters and generates files to simultaneously display gene expression data, gene clustering, sample clustering, and functional annotation of gene clustering.

Details

Package:	CLEAN
Type:	Package
Version:	1.0
Date:	2008-08-23
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

Johannes Freudenberg, Xiangdong Liu, Mario Medvedovic

Maintainer: Mario Medvedovic <[e-mail]>

See Also

gimmR

Examples

data(gimmOut)
require(CLEAN.Rn)
res <- runCLEAN(gimmOut, species = "Rn")

generateTreeViewFiles(gimmOut, functionalCategories=getFunctionalCategories("geneRIFs", species = "Rn"))
#same as 
generateTreeViewFiles(gimmOut, functionalCategories="geneRIFs", species = "Rn")

#multiple category types
generateTreeViewFiles(gimmOut, functionalCategories=c("geneRIFs", "CpGislands", "GO", "KEGG"), species = "Rn")

trt <- sapply(colnames(gimmOut$clustData)[-(1:2)], function(str) strsplit(str, split = "_")[[1]][1])
#not run
#generateTreeViewFiles(gimmOut, cclust = NA, verbose = FALSE, functionalCategories=c("geneRIFs",
#	"CpGislands", "GO", "KEGG"), species = "Rn", callTreeView = TRUE, sampleDesc = trt)
generateTreeViewFiles(gimmOut, cclust = NA, verbose = FALSE, functionalCategories=c("geneRIFs",
	"CpGislands", "GO", "KEGG"), species = "Rn", callTreeView = FALSE, sampleDesc = trt)

#non-hierarchical clustering
d <- nonHierarchicalClustering(function(m, k, ...) kmeans(m, k, ...)$cluster, 
	gimmOut$clustData[,-(1:2)], k = 2:4, nstart = 10)
#not run
#generateTreeViewFiles(gimmOut, rclust = d, cclust = NA, verbose = FALSE, functionalCategories=c("geneRIFs",
#    "CpGislands", "GO", "KEGG"), species = "Rn", callTreeView = TRUE, sampleDesc = trt)
generateTreeViewFiles(gimmOut, rclust = d, cclust = NA, verbose = FALSE, functionalCategories=c("geneRIFs",
    "CpGislands", "GO", "KEGG"), species = "Rn", callTreeView = FALSE, sampleDesc = trt)

#geneList enrichment
geneList <- gimmOut$clustData[,1]
require(org.Rn.eg.db)
allGenes <- unique(keys(org.Rn.egSYMBOL)) #one should really use the list of 
                                          #genes represented on the microarray instead
res <- geneListEnrichment(geneList, allGenes, functionalCategories = "GO", 
	species = "Rn", sigFDR = 0.01, maxGenesInCategory = 10000)
genesInEnrichedCategories(res[,1], geneList, funcCategories = "GO", species = "Rn")

uc-bd2k/CLEAN documentation built on Sept. 22, 2022, 4:12 a.m.