library(ccPaper)

Crohn's VS Ulcerative Colitis

GEO dataset GSE36807 has crohn's, uc, and normal samples.

library(GEOquery)

cucData <- getGEO(GEO = "GSE36807")[[1]]

tmpP <- pData(cucData)
tmpP$status <- "control"

cLoc <- grep("Crohn", tmpP$characteristics_ch1)
tmpP$status[cLoc] <- "crohns"

ucLoc <- grep("Ulcerative colitis", tmpP$characteristics_ch1)
tmpP$status[ucLoc] <- "uc"
pData(cucData) <- tmpP

.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(cucData, .sessionInfo, .timeDate, file = "inst/data/uc_crohns_rawData.RData")

Differential analysis

Use the limma package to rank probes.

library(limma)
library(hgu133plus2.db)
data(uc_crohns_rawData)

cucComps <- c("uc - control", "crohns - control")
geneID <- unlist(mget(featureNames(cucData), hgu133plus2ENTREZID))

cucExpr <- exprs(cucData)
cucCollapse <- collapseProbes(cucExpr, geneID)  # collapse to single genes using median of expression
cucCharacter <- pData(cucData)

cucFC <- rankGenes(cucCollapse, cucCharacter$status, cucComps, doAggregation = FALSE, 
    aggregateIndex = NA)
names(cucFC) <- c("UC", "CROHNS")

cucDiff <- lapply(cucFC, getDiffGenes, id = "id")
# this object can be run using base ccEnrich, or can be easily coerced

ORA

Do normal over-representation analysis on the cucDiff, those genes differentially expressed in the Crohn's and UC samples.

library(GO.db)
cucGeneList <- list(UC_up = list(genes = cucDiff$UC$up, universe = cucDiff$UC$universe, 
    annotation = "org.Hs.eg.db"), UC_dn = list(genes = cucDiff$UC$dn, universe = cucDiff$UC$universe, 
    annotation = "org.Hs.eg.db"), CR_up = list(genes = cucDiff$CROHNS$up, universe = cucDiff$CROHNS$universe, 
    annotation = "org.Hs.eg.db"), CR_dn = list(genes = cucDiff$CROHNS$dn, universe = cucDiff$CROHNS$universe, 
    annotation = "org.Hs.eg.db"))
cucGeneList <- new("ccGeneList", cucGeneList, ccType = "BP")

## Warning: NAs introduced by coercion


cucEnrich <- ccEnrich(cucGeneList)

## Performing Enrichment Calculations ....
## UC_up : BP 
## UC_dn : BP 
## CR_up : BP 
## CR_dn : BP 
## Done!!

pvalueType(cucEnrich) <- "pval"
pvalueCutoff(cucEnrich) <- 0.01

cucEnrich

##     Annotation category:  GO   BP 
##                FDR runs:  50 
## Default p-values to use:  pval 
##                 pCutoff:  0.01 
## 
## List:  UC_up 
## Gene to GO BP  test for over-representation 
## 6369 GO BP ids tested (657 have p <= 0.01 & count >= 0)
## Selected gene set size: 1207 
##     Gene universe size: 13833 
##     Annotation package: org.Hs.eg 
## 
## List:  UC_dn 
## Gene to GO BP  test for over-representation 
## 5621 GO BP ids tested (218 have p <= 0.01 & count >= 0)
## Selected gene set size: 935 
##     Gene universe size: 13833 
##     Annotation package: org.Hs.eg 
## 
## List:  CR_up 
## Gene to GO BP  test for over-representation 
## 1675 GO BP ids tested (179 have p <= 0.01 & count >= 0)
## Selected gene set size: 48 
##     Gene universe size: 13833 
##     Annotation package: org.Hs.eg 
## 
## List:  CR_dn 
## Gene to GO BP  test for over-representation 
## 2132 GO BP ids tested (70 have p <= 0.01 & count >= 0)
## Selected gene set size: 116 
##     Gene universe size: 13833 
##     Annotation package: org.Hs.eg


cucOpts <- new("ccOptions", listNames = names(cucGeneList), compareNames = c("UC_up", 
    "UC_dn", "CR_up", "CR_dn", "UC_up,CR_up", "UC_up,CR_dn", "UC_dn,CR_up", 
    "UC_dn,CR_dn"))
cucCompare <- ccCompare(cucEnrich, cucOpts)

cucCompare

## ccCompare results for:
## 
## Annotation category:  GO   BP 
## Main graph: A graphNEL graph with directed edges
## Number of Nodes = 621 
## Number of Edges = 117418

cucCompare <- breakEdges(cucCompare$BP, 0.8)
cucCy <- ccOutCyt(cucCompare, cucOpts)
breakEdges(cucCy, 1)

Run GSEA type analysis using limma

There are two ways to do GSEA using limma.

1 Use romer for competitive tests with rotations to do random samples 2 Use geneSetTest for gene-wise competitive tests, i.e. each set is tested against random gene samples

We're going to try both and see what we get out.

library(GO.db)
library(org.Hs.eg.db)
library(limma)
hsGO <- as.list(org.Hs.egGO2ALLEGS)
hsGO <- hsGO[(Ontology(names(hsGO)) == "BP")]
hsGO <- lapply(hsGO, unique)

useSet <- symbols2indices(hsGO, rownames(cucCollapse))

Run using romer

sampleStatus <- cucCharacter$status
doComps <- cucComps
f <- factor(sampleStatus)
design <- model.matrix(~0 + f)
colnames(design) <- levels(f)

contrast.matrix <- makeContrasts(contrasts = doComps, levels = design)

cucRomer <- multicontrastRomer(useSet, cucCollapse, design, contrast.matrix, 
    nrot = 10000)
names(cucRomer) <- c("UC", "CROHNS")
save(cucRomer, file = "inst/data/cucRomer.RData")

data(cucRomer)

geneAnnMapping <- new("namedList", .Data = hsGO, names = names(hsGO))

getSigID <- function(inRomer, pCut = 0.05, whichCol = c("Up", "Down")) {
    sigID <- lapply(whichCol, function(inCol) {
        rownames(inRomer[(inRomer[, inCol] <= pCut), ])
    })
    names(sigID) <- whichCol
    return(sigID)
}

cucSigRomer <- lapply(cucRomer, getSigID, pCut = 0.01)
cucSigRomer <- unlist(cucSigRomer, recursive = FALSE)

genCCSigList <- function(inSig) {
    tmp <- new("ccSigList", sigID = inSig)
}

cucCCSig <- lapply(cucSigRomer, genCCSigList)

cucCCRomer <- new("GENccEnrichResult", cucCCSig, categoryName = "GSEAGO", geneAnnMapping = geneAnnMapping, 
    overlapType = "overlap", annDescription = Term(names(geneAnnMapping)))

cucRomerOpts <- new("ccOptions", listNames = names(cucCCRomer), compareNames = c("UC.Up", 
    "UC.Down", "CROHNS.Up", "CROHNS.Down", "UC.Up,CROHNS.Up", "UC.Up,CROHNS.Down", 
    "UC.Down,CROHNS.Up", "UC.Down,CROHNS.Down"))
compareCUCRomer <- ccCompare(cucCCRomer, cucRomerOpts)
compareCUCRomer

## Annotation category:  GSEAGO    
## Main graph: A graphNEL graph with directed edges
## Number of Nodes = 288 
## Number of Edges = 15342

compareCUCRomer <- breakEdges(compareCUCRomer, 0.8)
cucRomerCW <- ccOutCyt(compareCUCRomer, cucRomerOpts, postText = "romer", rpcPort = 9001)
generateLegend(cucRomerOpts)

Now we will save groups so that they can be queried and examined later.

cucNodeGroups <- cytOutNodes("response to lipopolysaccharide and bacterial - UC.Up", 
    cucRomerCW)
cucNodeGroups <- cytOutNodes("regulation of inflammatory response - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("hydrogen peroxide metabolism - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("regulation of cell cycle and DNA damage response - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("regulation of ubiquitination and ligase activity - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("nucleotide and nucleoside metabolism - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("amine metabolism - UC.Up,CROHNS.Up", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("glandular cell differentiation - UC.Down", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("oligodendrocyte differentiation - UC.Down,CROHNS.Down", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("cellular pattern specification - UC.Down,CROHNS.Down", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("NAD biosynthesis - CROHNS.Up", cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("hormone metabolism - CROHNS.Up", cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("extrinsic signal transduction - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("regulation of nitric-oxide synthase - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("fatty-acyl-CoA biosynthesis - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("response to growth hormone - CROHNS.Up", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("melanin metabolism - CROHNS.Up", cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("protein dephosphorylation - CROHNS.Up", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("chemokine and cytokine production - UC.Up,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("membrane biogenesis and assembly - UC.Down", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("activin receptor signaling - UC.Down", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("nik/nk-kappab cascade - UC.Up", cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("er unfolded protein response - UC.Up, CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("regulation of ras/rac/rho gtpase activity - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("antigen processing and presentation - UC.UP,CROHNS.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("COPII vesicle coating and targeting - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("negative regulation of peptidase activity - UC.Up", 
    cucRomerCW, cucNodeGroups)
cucNodeGroups <- cytOutNodes("response to type 1 interferon - UC.Up", cucRomerCW, 
    cucNodeGroups)
cucNodeGroups <- cytOutNodes("protein N-linked glycosylation - UC.Up", cucRomerCW, 
    cucNodeGroups)
.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(cucRomerCW, cucNodeGroups, compareCUCRomer, cucRomerOpts, .sessionInfo, 
    .timeDate, file = "inst/data/cucCCOutput.RData")

We can then write out the results into a nice table.

data(cucCCOutput)
allDescStrings <- sapply(cucNodeGroups, function(x) {
    x$descStr
})
string2List <- strsplit(allDescStrings, " - ", fixed = TRUE)
justDesc <- sapply(string2List, function(x) {
    x[1]
})
listMem <- sapply(string2List, function(x) {
    x[2]
})
listMemSplit <- strsplit(listMem, ",", fixed = TRUE)

descMembershipTable <- matrix("", nrow = length(allDescStrings), ncol = 5)
colnames(descMembershipTable) <- c("Description", "UC.Down", "UC.Up", "CROHNS.Down", 
    "CROHNS.Up")

descMembershipTable[, "Description"] <- justDesc

for (inRow in seq(1, nrow(descMembershipTable))) {
    useSplit <- listMemSplit[[inRow]]
    trimSplit <- gsub(" ", "", useSplit)
    useLocs <- sapply(trimSplit, grep, colnames(descMembershipTable), ignore.case = TRUE)
    descMembershipTable[inRow, useLocs] <- "X"
}

orderBy <- c("UC.Up,CROHNS.Up", "UC.UP,CROHNS.Up", "UC.Up", "UC.Down", "UC.Down,CROHNS.Down", 
    "CROHNS.Up", "CROHNS.Down")

listMem <- gsub(" ", "", listMem)
newOrder <- unlist(lapply(orderBy, function(x) {
    which(listMem %in% x)
}))

descMembershipTable <- descMembershipTable[newOrder, ]
require(xtable)

# add an html link to each entry in the table
useLink <- paste("<a href=\"#loc", seq(1, nrow(descMembershipTable)), "\">", 
    descMembershipTable[, "Description"], "</a>", sep = "")
descMembershipTable[, "Description"] <- useLink

Print out the table.

Description	UC.Down	UC.Up	CROHNS.Down	CROHNS.Up
hydrogen peroxide metabolism		X		X
nucleotide and nucleoside metabolism		X		X
amine metabolism		X		X
extrinsic signal transduction		X		X
regulation of nitric-oxide synthase		X		X
fatty-acyl-CoA biosynthesis		X		X
chemokine and cytokine production		X		X
er unfolded protein response		X		X
antigen processing and presentation		X		X
response to lipopolysaccharide and bacterial		X
regulation of inflammatory response		X
regulation of cell cycle and DNA damage response		X
regulation of ubiquitination and ligase activity		X
nik/nk-kappab cascade		X
regulation of ras/rac/rho gtpase activity		X
COPII vesicle coating and targeting		X
negative regulation of peptidase activity		X
response to type 1 interferon		X
protein N-linked glycosylation		X
glandular cell differentiation	X
membrane biogenesis and assembly	X
activin receptor signaling	X
oligodendrocyte differentiation	X		X
cellular pattern specification	X		X
NAD biosynthesis				X
hormone metabolism				X
response to growth hormone				X
melanin metabolism				X
protein dephosphorylation				X

We also want the set of GO terms, their descriptions, and list memberships so that the inquisitive mind can see how we generated the groups.

hydrogen peroxide metabolism - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:2000377	regulation of reactive oxygen species metabolic process	X	X
GO:0042554	superoxide anion generation	X	X
GO:0072593	reactive oxygen species metabolic process	X	X
GO:0032928	regulation of superoxide anion generation	X
GO:0042743	hydrogen peroxide metabolic process		X
GO:2000379	positive regulation of reactive oxygen species metabolic process		X

back

nucleotide and nucleoside metabolism - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0043094	cellular metabolic compound salvage	X	X
GO:0043174	nucleoside salvage	X	X
GO:0072529	pyrimidine-containing compound catabolic process	X	X
GO:0009112	nucleobase metabolic process	X
GO:0072527	pyrimidine-containing compound metabolic process	X
GO:0009113	purine nucleobase biosynthetic process	X
GO:0006213	pyrimidine nucleoside metabolic process	X
GO:0043173	nucleotide salvage		X
GO:0008655	pyrimidine-containing compound salvage		X

back

amine metabolism - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0006596	polyamine biosynthetic process	X	X
GO:0033238	regulation of cellular amine metabolic process	X	X
GO:0009308	amine metabolic process	X	X
GO:0009309	amine biosynthetic process	X	X
GO:0006521	regulation of cellular amino acid metabolic process	X	X
GO:0042401	cellular biogenic amine biosynthetic process	X	X
GO:0044106	cellular amine metabolic process	X	X
GO:0033240	positive regulation of cellular amine metabolic process		X

back

extrinsic signal transduction - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:2001240	negative regulation of extrinsic apoptotic signaling pathway in absence of ligand	X	X
GO:1901099	negative regulation of signal transduction in absence of ligand	X	X
GO:2001239	regulation of extrinsic apoptotic signaling pathway in absence of ligand	X	X
GO:0038034	signal transduction in absence of ligand	X	X
GO:0097192	extrinsic apoptotic signaling pathway in absence of ligand	X	X

back

regulation of nitric-oxide synthase - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0032770	positive regulation of monooxygenase activity	X	X
GO:0051000	positive regulation of nitric-oxide synthase activity	X	X
GO:0032768	regulation of monooxygenase activity	X	X
GO:0050999	regulation of nitric-oxide synthase activity		X

back

fatty-acyl-CoA biosynthesis - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0035336	long-chain fatty-acyl-CoA metabolic process	X	X
GO:0046949	fatty-acyl-CoA biosynthetic process	X	X
GO:0035338	long-chain fatty-acyl-CoA biosynthetic process	X	X
GO:0035337	fatty-acyl-CoA metabolic process	X	X

back

chemokine and cytokine production - UC.Up,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0032602	chemokine production	X	X
GO:0009306	protein secretion	X
GO:0050704	regulation of interleukin-1 secretion	X
GO:0050707	regulation of cytokine secretion	X
GO:0050718	positive regulation of interleukin-1 beta secretion	X
GO:0032635	interleukin-6 production	X
GO:0050706	regulation of interleukin-1 beta secretion	X
GO:0032680	regulation of tumor necrosis factor production	X
GO:0050714	positive regulation of protein secretion	X
GO:0050715	positive regulation of cytokine secretion	X
GO:0050663	cytokine secretion	X
GO:0042092	type 2 immune response	X
GO:0001818	negative regulation of cytokine production	X
GO:0032732	positive regulation of interleukin-1 production	X
GO:0050708	regulation of protein secretion	X
GO:0032651	regulation of interleukin-1 beta production	X
GO:0032755	positive regulation of interleukin-6 production	X
GO:0032760	positive regulation of tumor necrosis factor production	X
GO:0001817	regulation of cytokine production	X
GO:0002643	regulation of tolerance induction	X
GO:0032652	regulation of interleukin-1 production	X
GO:0032640	tumor necrosis factor production	X
GO:0032757	positive regulation of interleukin-8 production	X
GO:0032715	negative regulation of interleukin-6 production	X
GO:0032675	regulation of interleukin-6 production	X
GO:0071706	tumor necrosis factor superfamily cytokine production	X
GO:0032642	regulation of chemokine production	X
GO:0032722	positive regulation of chemokine production	X
GO:0050716	positive regulation of interleukin-1 secretion	X
GO:0032731	positive regulation of interleukin-1 beta production	X
GO:0042033	chemokine biosynthetic process		X
GO:0050755	chemokine metabolic process		X
GO:0045408	regulation of interleukin-6 biosynthetic process		X

back

er unfolded protein response - UC.Up, CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0070059	intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress	X	X
GO:0035966	response to topologically incorrect protein	X
GO:0035967	cellular response to topologically incorrect protein	X
GO:0034620	cellular response to unfolded protein	X
GO:0032075	positive regulation of nuclease activity	X
GO:0006986	response to unfolded protein	X
GO:0032069	regulation of nuclease activity	X
GO:0006987	activation of signaling protein activity involved in unfolded protein response	X
GO:0034976	response to endoplasmic reticulum stress	X
GO:0030968	endoplasmic reticulum unfolded protein response	X
GO:0006984	ER-nucleus signaling pathway	X

back

antigen processing and presentation - UC.UP,CROHNS.Up

GOID	Description	UC.Up	CROHNS.Up
GO:0002474	antigen processing and presentation of peptide antigen via MHC class I	X	X
GO:0042590	antigen processing and presentation of exogenous peptide antigen via MHC class I	X	X
GO:0002479	antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent	X	X
GO:0019882	antigen processing and presentation	X
GO:0048002	antigen processing and presentation of peptide antigen	X
GO:0019884	antigen processing and presentation of exogenous antigen	X
GO:0002478	antigen processing and presentation of exogenous peptide antigen	X

back

response to lipopolysaccharide and bacterial - UC.Up

GOID	Description	UC.Up
GO:0009617	response to bacterium	X
GO:0071222	cellular response to lipopolysaccharide	X
GO:0042742	defense response to bacterium	X
GO:0031663	lipopolysaccharide-mediated signaling pathway	X
GO:0071219	cellular response to molecule of bacterial origin	X
GO:0032496	response to lipopolysaccharide	X
GO:0071216	cellular response to biotic stimulus	X
GO:0009595	detection of biotic stimulus	X
GO:0016045	detection of bacterium	X
GO:0002237	response to molecule of bacterial origin	X

back

regulation of inflammatory response - UC.Up

GOID	Description	UC.Up
GO:0002697	regulation of immune effector process	X
GO:0050688	regulation of defense response to virus	X
GO:0002699	positive regulation of immune effector process	X
GO:0050690	regulation of defense response to virus by virus	X
GO:0045191	regulation of isotype switching	X
GO:0002228	natural killer cell mediated immunity	X
GO:0002831	regulation of response to biotic stimulus	X
GO:0031348	negative regulation of defense response	X
GO:0031347	regulation of defense response	X
GO:0002703	regulation of leukocyte mediated immunity	X
GO:0002443	leukocyte mediated immunity	X
GO:0001906	cell killing	X
GO:0009615	response to virus	X
GO:0050727	regulation of inflammatory response	X
GO:0001909	leukocyte mediated cytotoxicity	X
GO:0002449	lymphocyte mediated immunity	X
GO:0051607	defense response to virus	X
GO:0016064	immunoglobulin mediated immune response	X
GO:0050728	negative regulation of inflammatory response	X

back

regulation of cell cycle and DNA damage response - UC.Up

GOID	Description	UC.Up
GO:0030330	DNA damage response, signal transduction by p53 class mediator	X
GO:0000077	DNA damage checkpoint	X
GO:0071156	regulation of cell cycle arrest	X
GO:0031570	DNA integrity checkpoint	X
GO:0044774	mitotic DNA integrity checkpoint	X
GO:0042770	signal transduction in response to DNA damage	X
GO:0071158	positive regulation of cell cycle arrest	X
GO:0072401	signal transduction involved in DNA integrity checkpoint	X
GO:0072431	signal transduction involved in mitotic G1 DNA damage checkpoint	X
GO:2000045	regulation of G1/S transition of mitotic cell cycle	X
GO:0044783	G1 DNA damage checkpoint	X
GO:2000134	negative regulation of G1/S transition of mitotic cell cycle	X
GO:0072422	signal transduction involved in DNA damage checkpoint	X
GO:0006977	DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest	X
GO:0031571	mitotic G1 DNA damage checkpoint	X
GO:0072413	signal transduction involved in mitotic cell cycle checkpoint	X
GO:0044773	mitotic DNA damage checkpoint	X
GO:0031572	G2 DNA damage checkpoint	X
GO:0072395	signal transduction involved in cell cycle checkpoint	X

back

regulation of ubiquitination and ligase activity - UC.Up

GOID	Description	UC.Up
GO:0051352	negative regulation of ligase activity	X
GO:0051439	regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle	X
GO:0031398	positive regulation of protein ubiquitination	X
GO:0051443	positive regulation of ubiquitin-protein ligase activity	X
GO:0051437	positive regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle	X
GO:0031145	anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolic process	X
GO:0051340	regulation of ligase activity	X
GO:0051438	regulation of ubiquitin-protein ligase activity	X
GO:0051444	negative regulation of ubiquitin-protein ligase activity	X
GO:0051351	positive regulation of ligase activity	X
GO:0051436	negative regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle	X

back

nik/nk-kappab cascade - UC.Up

GOID	Description	UC.Up	CROHNS.Up
GO:1901222	regulation of NIK/NF-kappaB cascade	X	X
GO:0045860	positive regulation of protein kinase activity	X
GO:0051347	positive regulation of transferase activity	X
GO:0033674	positive regulation of kinase activity	X
GO:0007250	activation of NF-kappaB-inducing kinase activity	X
GO:0038061	NIK/NF-kappaB cascade	X

back

regulation of ras/rac/rho gtpase activity - UC.Up

GOID	Description	UC.Up
GO:0051345	positive regulation of hydrolase activity	X
GO:0032855	positive regulation of Rac GTPase activity	X
GO:0032318	regulation of Ras GTPase activity	X
GO:0032320	positive regulation of Ras GTPase activity	X
GO:0032862	activation of Rho GTPase activity	X
GO:0032321	positive regulation of Rho GTPase activity	X
GO:0032319	regulation of Rho GTPase activity	X

back

COPII vesicle coating and targeting - UC.Up

GOID	Description	UC.Up
GO:0048208	COPII vesicle coating	X
GO:0048207	vesicle targeting, rough ER to cis-Golgi	X
GO:0048199	vesicle targeting, to, from or within Golgi	X
GO:0090114	COPII-coated vesicle budding	X

back

negative regulation of peptidase activity - UC.Up

GOID	Description	UC.Up
GO:0052547	regulation of peptidase activity	X
GO:0010466	negative regulation of peptidase activity	X
GO:0010951	negative regulation of endopeptidase activity	X
GO:0052548	regulation of endopeptidase activity	X

back

response to type 1 interferon - UC.Up

GOID	Description	UC.Up
GO:0071357	cellular response to type I interferon	X
GO:0034340	response to type I interferon	X
GO:0060337	type I interferon-mediated signaling pathway	X

back

protein N-linked glycosylation - UC.Up

GOID	Description	UC.Up
GO:0018196	peptidyl-asparagine modification	X
GO:0018279	protein N-linked glycosylation via asparagine	X
GO:0006487	protein N-linked glycosylation	X

back

glandular cell differentiation - UC.Down

GOID	Description	UC.Down
GO:0002068	glandular epithelial cell development	X
GO:0002067	glandular epithelial cell differentiation	X
GO:0003309	type B pancreatic cell differentiation	X
GO:0003323	type B pancreatic cell development	X
GO:0002066	columnar/cuboidal epithelial cell development	X
GO:0035883	enteroendocrine cell differentiation	X
GO:0002065	columnar/cuboidal epithelial cell differentiation	X

back

membrane biogenesis and assembly - UC.Down

GOID	Description	UC.Down	UC.Up	CROHNS.Down	CROHNS.Up
GO:0071709	membrane assembly	X
GO:0044091	membrane biogenesis	X

back

activin receptor signaling - UC.Down

GOID	Description	UC.Down	UC.Up	CROHNS.Down	CROHNS.Up
GO:0032925	regulation of activin receptor signaling pathway	X
GO:0032924	activin receptor signaling pathway	X

back

oligodendrocyte differentiation - UC.Down,CROHNS.Down

GOID	Description	UC.Down	CROHNS.Down
GO:0048715	negative regulation of oligodendrocyte differentiation	X	X
GO:0045686	negative regulation of glial cell differentiation	X
GO:0048713	regulation of oligodendrocyte differentiation	X
GO:0014014	negative regulation of gliogenesis	X
GO:0048709	oligodendrocyte differentiation		X

back

cellular pattern specification - UC.Down,CROHNS.Down

GOID	Description	UC.Down	CROHNS.Down
GO:0009953	dorsal/ventral pattern formation	X
GO:0009952	anterior/posterior pattern specification	X
GO:0007389	pattern specification process	X
GO:0003002	regionalization	X
GO:0007379	segment specification		X
GO:0001840	neural plate development		X

back

NAD biosynthesis - CROHNS.Up

GOID	Description	CROHNS.Up
GO:0072525	pyridine-containing compound biosynthetic process	X
GO:0009435	NAD biosynthetic process	X
GO:0019363	pyridine nucleotide biosynthetic process	X
GO:0072524	pyridine-containing compound metabolic process	X
GO:0019359	nicotinamide nucleotide biosynthetic process	X

back

hormone metabolism - CROHNS.Up

GOID	Description	CROHNS.Up
GO:0016114	terpenoid biosynthetic process	X
GO:0042573	retinoic acid metabolic process	X
GO:0008299	isoprenoid biosynthetic process	X
GO:0042448	progesterone metabolic process	X
GO:0034754	cellular hormone metabolic process	X

back

response to growth hormone - CROHNS.Up

GOID	Description	CROHNS.Up
GO:0071378	cellular response to growth hormone stimulus	X
GO:0060416	response to growth hormone stimulus	X
GO:0060396	growth hormone receptor signaling pathway	X

back

melanin metabolism - CROHNS.Up

GOID	Description	CROHNS.Up
GO:0006582	melanin metabolic process	X
GO:0019748	secondary metabolic process	X
GO:0042438	melanin biosynthetic process	X

back

protein dephosphorylation - CROHNS.Up

GOID	Description	CROHNS.Up
GO:0035304	regulation of protein dephosphorylation	X
GO:0035307	positive regulation of protein dephosphorylation	X
GO:0035306	positive regulation of dephosphorylation	X

back

Demonstration of why graph representation is useful

Is the graph representation actually important for our method, or is it just useful over all?

library(graph)
data(cucRomer)
data(cucCCOutput)

getTable <- function(inName) {
    tmpDat <- cucRomer[[inName]]
    colnames(tmpDat) <- paste(inName, colnames(tmpDat), sep = ".")
    tmpDat
}

cucTables <- lapply(names(cucRomer), getTable)
all.equal(rownames(cucTables[[1]]), rownames(cucTables[[2]]))

## [1] TRUE


cucTables <- do.call(cbind, cucTables)
cucTables <- as.data.frame(cucTables, stringsAsFactors = FALSE)
cucTables$ID <- rownames(cucTables)

sigNodes <- nodes(compareCUCRomer@mainGraph)
nodeMembership <- unlist(nodeData(compareCUCRomer@mainGraph, sigNodes, "listMembership"))

cucTables <- cucTables[sigNodes, ]
cucTables$membership <- nodeMembership
cucTables$description <- unlist(nodeData(compareCUCRomer@mainGraph, sigNodes, 
    "Desc"))

groupLabel <- "nucleotide and nucleoside metabolism - UC.Up,CROHNS.Up"
allLabel <- sapply(cucNodeGroups, function(x) {
    x$descStr
})
whichLabel <- which(allLabel %in% groupLabel)
groupNodes <- cucNodeGroups[[whichLabel]]$nodes
cucTables[groupNodes, ]

##            UC.NGenes    UC.Up UC.Down UC.Mixed CROHNS.NGenes CROHNS.Up
## GO:0009112        63 0.006499  0.9936  0.08179            63  0.160684
## GO:0043094        30 0.006899  0.9932  0.02500            30  0.001200
## GO:0043173        12 0.028597  0.9715  0.05439            12  0.008199
## GO:0043174        12 0.007199  0.9929  0.15468            12  0.003400
## GO:0072529        30 0.001200  0.9989  0.30107            30  0.003700
## GO:0072527        69 0.005899  0.9942  0.19648            69  0.138086
## GO:0009113        10 0.009399  0.9907  0.04350            10  0.165083
## GO:0008655         9 0.016998  0.9831  0.25347             9  0.004000
## GO:0006213        47 0.005199  0.9949  0.43016            47  0.169883
##            CROHNS.Down CROHNS.Mixed         ID      membership
## GO:0009112      0.8394      0.40866 GO:0009112           UC.Up
## GO:0043094      0.9989      0.04760 GO:0043094 UC.Up,CROHNS.Up
## GO:0043173      0.9919      0.02130 GO:0043173       CROHNS.Up
## GO:0043174      0.9967      0.06239 GO:0043174 UC.Up,CROHNS.Up
## GO:0072529      0.9964      0.02860 GO:0072529 UC.Up,CROHNS.Up
## GO:0072527      0.8620      0.14079 GO:0072527           UC.Up
## GO:0009113      0.8350      0.65173 GO:0009113           UC.Up
## GO:0008655      0.9961      0.05039 GO:0008655       CROHNS.Up
## GO:0006213      0.8302      0.30747 GO:0006213           UC.Up
##                                                 description
## GO:0009112                     nucleobase metabolic process
## GO:0043094              cellular metabolic compound salvage
## GO:0043173                               nucleotide salvage
## GO:0043174                               nucleoside salvage
## GO:0072529 pyrimidine-containing compound catabolic process
## GO:0072527 pyrimidine-containing compound metabolic process
## GO:0009113           purine nucleobase biosynthetic process
## GO:0008655           pyrimidine-containing compound salvage
## GO:0006213          pyrimidine nucleoside metabolic process

OK, so given the full table of 288 results, where do these terms end up falling in the list? i.e. if I had to scroll through the list, how likely would I be to actually determine that nucleoside - nucleotide metabolism comes from both UC.Up and CROHNS.Up?

newOrder <- order(cucTables[, "UC.Up"], cucTables[, "CROHNS.Up"], cucTables[, 
    "membership"])
cucTables <- cucTables[newOrder, ]
which(cucTables$ID %in% groupNodes)

## [1]  40 123 136 149 155 167 191 206 215

Date and System information

Sys.time()

## [1] "2014-02-19 10:42:29 EST"

sessionInfo()

## R version 3.0.1 (2013-05-16)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=C                 LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] graph_1.40.1                xtable_1.7-1               
##  [3] mouse4302.db_2.10.1         org.Mm.eg.db_2.10.1        
##  [5] rat2302.db_2.10.1           org.Rn.eg.db_2.10.1        
##  [7] affy_1.40.0                 colorspace_1.2-4           
##  [9] mgcv_1.7-27                 nlme_3.1-113               
## [11] hgu133plus2.db_2.10.1       hgu95av2.db_2.10.1         
## [13] ALL_1.4.16                  GO.db_2.10.1               
## [15] org.Hs.eg.db_2.10.1         devtools_1.4.1             
## [17] ccPaper_0.0.19              ggplot2_0.9.3.1            
## [19] limma_3.18.7                categoryComparePaper_1.6.15
## [21] AnnotationDbi_1.24.0        RSQLite_0.11.4             
## [23] DBI_0.2-7                   Biobase_2.22.0             
## [25] BiocGenerics_0.8.0          roxygen2_2.2.2             
## [27] digest_0.6.4               
## 
## loaded via a namespace (and not attached):
##  [1] affyio_1.30.0         annotate_1.40.0       AnnotationForge_1.4.4
##  [4] BiocInstaller_1.12.0  brew_1.0-6            Category_2.28.0      
##  [7] dichromat_2.0-0       evaluate_0.5.1        formatR_0.10         
## [10] genefilter_1.44.0     GOstats_2.28.0        grid_3.0.1           
## [13] GSEABase_1.24.0       gtable_0.1.2          httr_0.2             
## [16] hwriter_1.3           IRanges_1.20.6        knitr_1.5            
## [19] labeling_0.2          lattice_0.20-24       markdown_0.6.4       
## [22] MASS_7.3-29           Matrix_1.1-1.1        memoise_0.1          
## [25] munsell_0.4.2         plyr_1.8              preprocessCore_1.24.0
## [28] proto_0.3-10          RBGL_1.38.0           RColorBrewer_1.0-5   
## [31] RCurl_1.95-4.1        RCytoscape_1.12.0     reshape2_1.2.2       
## [34] scales_0.2.3          splines_3.0.1         stats4_3.0.1         
## [37] stringr_0.6.2         survival_2.37-4       tools_3.0.1          
## [40] whisker_0.3-2         XML_3.98-1.1          XMLRPC_0.3-0         
## [43] zlibbioc_1.8.0