dnet 1.0.7

Demo for TCGA mutation and survival dataset

Notes:
  • All results are based on dnet (version 1.0.7).
  • R scripts (i.e. R expressions) plus necessary comments are highlighted in light cyan, and the rest are outputs in the screen.
  • Images displayed below may be distorted, but should be normal in your screen.
  • Functions contained in dnet 1.0.7 are hyperlinked in-place and also listed on the right side. 
    •       
    # This is a demo for TCGA mutational profile dataset from Kandoth et al
# 
# This dataset is available from TCGA (see http://www.ncbi.nlm.nih.gov/pubmed/24132290), containing somatic mutational profiles for 3096 cancer samples with survival data. These cancer samples belong to one of 12 major cancer types, including breast adenocarcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), colon and rectal carcinoma (COAD/READ), bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma (OV) and acute myeloid leukaemia (LAML). For each patient sample, somatic mutations are represented as a profile of  states on genes, where non-zero entry indicates a gene for which how many mutations have occurred in the tumor relative to germ line. The dataset is provided as an 'ExpressionSet' object.
## assayData: exprs(TCGA_mutations), a matrix of 19171 genes X 3096 samples;
## phenoData: pData(TCGA_mutations), variables describing sample phenotypes (i.e. columns in assayData), including clinical/survival information about samples: "time" (i.e. survival time in days), "status" (i.e., survival status: 0=alive; 1=dead), "Age" (the patient age in years), "Gender" (the patient gender: male/female), "TCGA_tumor_type", "Tumor_stage", "Tumor_grade"
## featureData: fData(TCGA_mutations), variables describing features (i.e. rows in assayData), including information about features/genes: "EntrezID" for gene EntrezID, "Symbol" for gene symbol, "Desc" for gene description, "Synonyms" for gene symbol alias
###############################################################################
library(dnet)

# Load or install packages specifically used in this demo
for(pkg in c("Biobase","survival")){
    if(!require(pkg, character.only=T)){
        source("http://bioconductor.org/biocLite.R")
        biocLite(pkg)
        lapply(pkg, library, character.only=T)
    }
}

# load an "ExpressionSet" object
TCGA_mutations <- dRDataLoader(RData='TCGA_mutations')

    
'TCGA_mutations' (from package 'dnet' version 1.0.7) has been loaded into the working environment

    eset <- TCGA_mutations
# extract information about phenotype data
pd <- pData(eset)
pd[1:3,]

    

                                 time status Age Gender TCGA_tumor_type Tumor_stage
TCGA-B8-4153-01B-11D-1669-08  404      0  74   male            KIRC           3
TCGA-24-1469-01A-01W-0553-09  277      0  71 female              OV           3
TCGA-06-5411-01A-01D-1696-08  254      1  51   male             GBM          NA
                             Tumor_grade
TCGA-B8-4153-01B-11D-1669-08           3
TCGA-24-1469-01A-01W-0553-09           3
TCGA-06-5411-01A-01D-1696-08          NA

    

    # extract information about feature/gene data
fd <- fData(eset)
fd[1:3,]

    

               EntrezID    Symbol
1060P11.3 100506173 1060P11.3
A1BG              1      A1BG
A1CF          29974      A1CF
                                                                         Desc
1060P11.3 killer cell immunoglobulin-like receptor, three domains, pseudogene
A1BG                                                   alpha-1-B glycoprotein
A1CF                                           APOBEC1 complementation factor
                               Synonyms
1060P11.3                             -
A1BG               A1B|ABG|GAB|HYST2477
A1CF      ACF|ACF64|ACF65|APOBEC1CF|ASP

    

    # extract information about mutational data
md <- exprs(eset)
md[1:3,1:3]

    

              TCGA-B8-4153-01B-11D-1669-08 TCGA-24-1469-01A-01W-0553-09
1060P11.3                            1                            1
A1BG                                 0                            0
A1CF                                 0                            0
          TCGA-06-5411-01A-01D-1696-08
1060P11.3                            0
A1BG                                 1
A1CF                                 0

    

    # number of samples for each cancer type
tumor_type <- sort(unique(pData(eset)$TCGA_tumor_type))
table(pData(eset)$TCGA_tumor_type)

    

    
    BLCA     BRCA COADREAD      GBM     HNSC     KIRC     LAML     LUAD 
      92      763      193      275      300      417      185      155 
    LUSC       OV     UCEC 
     171      315      230 

    

    
# Survival analysis across tumor types using Cox proportional hazards model
# Cox regression yields an equation for the hazard/risk as a function of several explanatory variables

## fit a Cox proportional hazards model for age, gender, tumor type
data <- pd
fit <- survival::coxph(Surv(time,status) ~ Age + Gender + TCGA_tumor_type, data=data)
res <- anova(fit, test="Chisq")

## Now with gene mutational data in subject, adjust for other explanatory variables (or called covariates) include: age, gender, and tumor type
## only those genes with mutations at least 1% of samples will be analysed
flag <- sapply(1:nrow(md), function(i) ifelse(sum(md[i,]!=0)>=0.01*ncol(md), T, F))
esetGene <- eset[flag, ]
md_selected <- md[flag,]
## survival analysis to obtain hazard ratio (HR) and pvaules
HR <- rep(1, nrow(md_selected))
pvals <- rep(1, nrow(md_selected))
for(i in 1:nrow(md_selected)){
    ## fit a Cox proportional hazards model
    data <- cbind(pd, gene=md_selected[i,])
    fit <- survival::coxph(formula=Surv(time,status) ~ Age + Gender + TCGA_tumor_type + gene, data=data)
    ## ANOVA (Chisq test)
    res <- as.matrix(anova(fit, test="Chisq"))
    HR[i] <- res[5,2]
    pvals[i] <- res[5,4]
}
names(HR) <- rownames(md_selected)
names(pvals) <- rownames(md_selected)

# An igraph object that contains a functional protein association network in human. The network is extracted from the STRING database (version 10). Only those associations with medium confidence (score>=400) are retained.
org.Hs.string <- dRDataLoader(RData='org.Hs.string')

    
'org.Hs.string' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.string.RData) has been loaded into the working environment

    # restrict to those edges with high confidence (score>=700)
network <- subgraph.edges(org.Hs.string, eids=E(org.Hs.string)[combined_score>=700])
network

    

    IGRAPH UN-- 15341 316170 -- 
+ attr: name (v/c), seqid (v/c), geneid (v/n), symbol (v/c),
| description (v/c), neighborhood_score (e/n), fusion_score (e/n),
| cooccurence_score (e/n), coexpression_score (e/n), experimental_score
| (e/n), database_score (e/n), textmining_score (e/n), combined_score
| (e/n)
+ edges (vertex names):
 [1] 3017550--3023854 3023931--3028317 3019304--3028317 3028317--3033319
 [5] 3023602--3028317 3014709--3028317 3024678--3026825 3023468--3030905
 [9] 3026117--3030905 3026845--3029085 3017265--3027473 3015527--3033837
[13] 3019960--3033973 3021862--3033174 3015979--3025568 3015355--3025568
+ ... omitted several edges

    

    
# extract network that only contains genes in pvals
ind <- match(V(network)$symbol, names(pvals))
## for extracted graph
nodes_mapped <- V(network)$name[!is.na(ind)]
network <- dNetInduce(g=network, nodes_query=nodes_mapped, knn=0, remove.loops=F, largest.comp=T)
V(network)$name <- V(network)$symbol
network

    

    IGRAPH UN-- 1889 14160 -- 
+ attr: name (v/c), seqid (v/c), geneid (v/n), symbol (v/c),
| description (v/c), neighborhood_score (e/n), fusion_score (e/n),
| cooccurence_score (e/n), coexpression_score (e/n), experimental_score
| (e/n), database_score (e/n), textmining_score (e/n), combined_score
| (e/n)
+ edges (vertex names):
 [1] OR2G2--OR2L8   OR2G2--OR14A16 OR2G2--OR10G8  OR2G2--OR11L1  OR2G2--OR2L3  
 [6] OR2G2--OR5D18  OR2G2--OR10G7  OR2G2--OR2T12  OR2G2--OR5F1   OR2G2--OR2T33 
[11] OR2G2--OR2C3   OR2G2--OR6F1   OR2G2--OR5P2   OR2G2--OR8J3   OR2G2--OR4C15 
[16] OR2G2--OR2M2   OR2G2--OR5D13  OR2G2--OR4Q3   OR2G2--OR2B11  OR2G2--OR2M5  
+ ... omitted several edges

    

    
# Identification of gene-active network
net <- dNetPipeline(g=network, pval=pvals, method="customised", significance.threshold=2.5e-02)

    
Start at 2015-07-21 17:19:41

First, consider the input fdr (or p-value) distribution
Second, determine the significance threshold...
	significance threshold: 2.50e-02
Third, calculate the scores according to the input fdr (or p-value) and the threshold (if any)...
	Amongst 2836 scores, there are 149 positives.
Finally, find the subgraph from the input graph with 1889 nodes and 14160 edges...
	Size of the subgraph: 44 nodes and 67 edges

Finish at 2015-07-21 17:19:51
Runtime in total is: 10 secs


    net

    

    IGRAPH UN-- 44 67 -- 
+ attr: name (v/c), seqid (v/c), geneid (v/n), symbol (v/c),
| description (v/c), score (v/n), neighborhood_score (e/n),
| fusion_score (e/n), cooccurence_score (e/n), coexpression_score
| (e/n), experimental_score (e/n), database_score (e/n),
| textmining_score (e/n), combined_score (e/n)
+ edges (vertex names):
 [1] CTNNB1--FAT4   CTNNB1--MAGI2  CTNNB1--DNM2   CTNNB1--TP53   CTNNB1--TNIK  
 [6] CTNNB1--TJP1   CTNNB1--ABL1   CTNNB1--PTEN   DSC2  --TP53   IDH1  --TP53  
[11] ITGB1 --HSPG2  ITGB1 --COL6A2 ITGB1 --PAK7   ITGB1 --ITGAM  ITGB1 --PTEN  
[16] ITGB1 --AGRN   ITGB1 --UTRN   EPHA3 --EPHA1  EPHA3 --ABL1   FAM13A--VAV1  
+ ... omitted several edges

    

    
# visualisation of the gene-active network itself
## the layout of the network visualisation (fixed in different visuals) 
glayout <- layout.fruchterman.reingold(net)
## color nodes according to communities (identified via a spin-glass model and simulated annealing)
com <- spinglass.community(net, spins=25)
com$csize <- sapply(1:length(com),function(x) sum(com$membership==x))
vgroups <- com$membership
colormap <- "yellow-darkorange"
palette.name <- visColormap(colormap=colormap)
mcolors <- palette.name(length(com))
vcolors <- mcolors[vgroups]
com$significance <- dCommSignif(net, com)
## node sizes according to degrees
vdegrees <- igraph::degree(net)
## highlight different communities
mark.groups <- communities(com)
mark.col <- visColoralpha(mcolors, alpha=0.2)
mark.border <- visColoralpha(mcolors, alpha=0.2)
edge.color <- c("#C0C0C0", "#000000")[crossing(com,net)+1]
edge.color <- visColoralpha(edge.color, alpha=0.5)
## visualise the subnetwrok
visNet(g=net, glayout=glayout, vertex.label=V(net)$geneSymbol, vertex.color=vcolors, vertex.frame.color=vcolors, vertex.shape="sphere", mark.groups=mark.groups, mark.col=mark.col, mark.border=mark.border, mark.shape=1, mark.expand=10, edge.color=edge.color, newpage=F, vertex.label.color="blue", vertex.label.dist=0.4, vertex.label.font=2)

    


    legend_name <- paste("C",1:length(mcolors)," (n=",com$csize,", pval=",signif(com$significance,digits=2),")",sep='')
legend("topleft", legend=legend_name, fill=mcolors, bty="n", cex=0.6)

    


    
# fit a Cox proportional hazards model using the subnetwork
## for the whole network
data_g <- t(md[V(net)$name,])
data_g <- apply(data_g!=0, 1, sum)
data <- cbind(pd, net=data_g)
fit <- coxph(formula=Surv(time,status) ~ Age + Gender + TCGA_tumor_type + net, data=data)
res <- as.matrix(anova(fit))
HR_g <- res[5,2]
pvals_g <- res[5,4]
## for the cumulative nodes from the network
cg_names <- names(sort(HR[V(net)$name], decreasing=T))
cg_signif <- matrix(1, nrow=length(cg_names), ncol=2)
rownames(cg_signif) <- cg_names
colnames(cg_signif) <- c("HR", "pvalue")
for(i in 1:length(cg_names)){
    data_g <- t(md[cg_names[1:i],])
    if(i!=1){
        data_g <- apply(data_g!=0, 1, sum)
    }else{
        data_g <- as.vector(data_g!=0)
    }
    data <- cbind(pd, cnet=data_g)
    fit <- coxph(formula=Surv(time,status) ~ Age + Gender + TCGA_tumor_type + cnet, data=data)
    res <- as.matrix(anova(fit))
    cg_signif[i,] <- res[5,c(2,4)]
}
cg_signif[cg_signif[,2]==0,2] <- min(cg_signif[cg_signif[,2]!=0,2])
naive <- sample(HR, length(cg_names))
bp.HR.list <- list(All=naive, Neti=HR[cg_names], Netc=cg_signif[2:nrow(cg_signif),1])
par(las=2, mar=c(10,8,4,2)) # all axis labels horizontal
boxplot(bp.HR.list, outline=F, horizontal=F, names=c("naive\n(genes in random)", "dnet\n(genes individually)", "dnet \n(genes in combination)"), col=c("red","green","blue"), ylab="Cox hazard ratio (HR)", log="y", ylim=c(0.1,100))

    


    # Two-sample Kolmogorov-Smirnov test
## Genes randomly choosen versus genes in the network (used individually)
stats::ks.test(x=naive, y=HR[cg_names], alternative="two.sided", exact=NULL)

    
Warning message:
cannot compute exact p-value with ties

    
	Two-sample Kolmogorov-Smirnov test

data:  naive and HR[cg_names]
D = 0.81818, p-value = 3.23e-13
alternative hypothesis: two-sided


    

    ## Genes in the network (used individually) versuse genes in the network (used in combination)
stats::ks.test(x=HR[cg_names], y=cg_signif[2:nrow(cg_signif),1], alternative="two.sided", exact=NULL)

    

    
	Two-sample Kolmogorov-Smirnov test

data:  HR[cg_names] and cg_signif[2:nrow(cg_signif), 1]
D = 1, p-value = 4.441e-16
alternative hypothesis: two-sided


    

    
# Network-based sample classifications and visualisations on 2D sample landscape
# it uses the gene-active subnetwork overlaid by mutation frequency data
frac_mutated <- sapply(tumor_type, function(x) {
    e <- eset[, which(pData(eset)$TCGA_tumor_type==x)]
    apply(exprs(e)!=0,1,sum)/ncol(e)
})
rownames(frac_mutated) <- fData(eset)$Symbol
data <- frac_mutated[V(net)$name,]
sReorder <- dNetReorder(g=net, data, feature="edge", node.normalise="degree", amplifier=3, metric="none")

    
Start at 2015-07-21 17:20:06

First, define topology of a map grid (2015-07-21 17:20:06)...
Second, initialise the codebook matrix (36 X 67) using 'linear' initialisation, given a topology and input data (2015-07-21 17:20:06)...
Third, get training at the rough stage (2015-07-21 17:20:06)...
	1 out of 363 (2015-07-21 17:20:06)
	37 out of 363 (2015-07-21 17:20:06)
	74 out of 363 (2015-07-21 17:20:06)
	111 out of 363 (2015-07-21 17:20:06)
	148 out of 363 (2015-07-21 17:20:06)
	185 out of 363 (2015-07-21 17:20:06)
	222 out of 363 (2015-07-21 17:20:06)
	259 out of 363 (2015-07-21 17:20:06)
	296 out of 363 (2015-07-21 17:20:06)
	333 out of 363 (2015-07-21 17:20:06)
	363 out of 363 (2015-07-21 17:20:06)
Fourth, get training at the finetune stage (2015-07-21 17:20:06)...
	1 out of 1441 (2015-07-21 17:20:06)
	145 out of 1441 (2015-07-21 17:20:06)
	290 out of 1441 (2015-07-21 17:20:06)
	435 out of 1441 (2015-07-21 17:20:06)
	580 out of 1441 (2015-07-21 17:20:06)
	725 out of 1441 (2015-07-21 17:20:06)
	870 out of 1441 (2015-07-21 17:20:06)
	1015 out of 1441 (2015-07-21 17:20:06)
	1160 out of 1441 (2015-07-21 17:20:06)
	1305 out of 1441 (2015-07-21 17:20:06)
	1441 out of 1441 (2015-07-21 17:20:07)
Next, identify the best-matching hexagon/rectangle for the input data (2015-07-21 17:20:07)...
Finally, append the response data (hits and mqe) into the sMap object (2015-07-21 17:20:07)...

Below are the summaries of the training results:
   dimension of input data: 11x67
   xy-dimension of map grid: xdim=6, ydim=6
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 36x2
   initialisation method: linear
   dimension of codebook matrix: 36x67
   mean quantization error: 0.00343617643351582

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 33 at rough stage; 131 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2015-07-21 17:20:07
Runtime in total is: 1 secs


    visNetReorder(g=net, data=data, sReorder=sReorder, height=ceiling(sqrt(ncol(data)))*3, newpage=T, glayout=glayout, colormap="darkgreen-lightgreen-lightpink-darkred", vertex.label=NA,vertex.shape="sphere", vertex.size=16,mtext.cex=0.8,border.color="888888", zlim=c(0,0.1), mark.groups=mark.groups, mark.col=mark.col, mark.border=mark.border, mark.shape=1, mark.expand=10, edge.color=edge.color)

    


    
# output the subnetwork and their mutation frequency data
## Write the subnetwork into a SIF-formatted file (Simple Interaction File)
sif <- data.frame(source=get.edgelist(net)[,1], type="interaction", target=get.edgelist(net)[,2])
write.table(sif, file=paste("Survival_TCGA.sif", sep=""), quote=F, row.names=F,col.names=F,sep="\t")
## Output the corresponding mutation frequency data
hmap <- data.frame(Symbol=rownames(data), data)
write.table(hmap, file=paste("Survival_TCGA.txt", sep=""), quote=F, row.names=F,col.names=T,sep="\t")

# define the "mutation ubiquity" of genes in terms of a vector which stores the fraction of samples (within a tumor type) having the mutated gene 
# sparseness for a vector is: 1) one if the vector contains only a single non-zero value; 2) zero if and only if all elements are equal; 3) otherwise, the value interpolates smoothly between the two extremes
sparseness <- sapply(1:nrow(frac_mutated), function(i){
    v <- frac_mutated[i,]
    n <- length(v)
    norm1 <- sum(abs(v))
    norm2 <- sqrt(sum(v^2))
    (sqrt(n)-norm1/norm2) / (sqrt(n)-1)
})
sparseness <- matrix(sparseness, ncol=1)
rownames(sparseness) <- rownames(frac_mutated)
# derive the "mutation ubiquity" of genes: mutational fraction with the same type, and fraction consistent across different types
ubiquity <- 1- sparseness
dev.new()
hist(ubiquity,20, xlab="Cross-tumor mutation ubiquity", xlim=c(0,1))

    


    
# GSEA using the network as a gene set against the cross-tumor mutation ubiquity and each tumor type
customised.genesets <- list(net=V(net)$name)
eTerm <- dGSEA(data=cbind(ubiquity, frac_mutated), identity="symbol", genome="Hs", ontology="Customised", customised.genesets=customised.genesets, weight=0, nperm=2000)

    
Start at 2015-07-21 17:20:57

First, load the ontology Customised and its gene associations in the genome Hs (2015-07-21 17:20:57) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Then, do mapping based on symbol (2015-07-21 17:20:58) ...
	Among 19420 symbols of input data, there are 19420 mappable via official gene symbols but 0 left unmappable
Third, perform GSEA analysis (2015-07-21 17:21:02) ...
	Sample 1 is being processed at (2015-07-21 17:21:02) ...
		 1 of 1 gene sets have been processed
	Sample 2 is being processed at (2015-07-21 17:21:05) ...
		 1 of 1 gene sets have been processed
	Sample 3 is being processed at (2015-07-21 17:21:08) ...
		 1 of 1 gene sets have been processed
	Sample 4 is being processed at (2015-07-21 17:21:11) ...
		 1 of 1 gene sets have been processed
	Sample 5 is being processed at (2015-07-21 17:21:13) ...
		 1 of 1 gene sets have been processed
	Sample 6 is being processed at (2015-07-21 17:21:16) ...
		 1 of 1 gene sets have been processed
	Sample 7 is being processed at (2015-07-21 17:21:19) ...
		 1 of 1 gene sets have been processed
	Sample 8 is being processed at (2015-07-21 17:21:21) ...
		 1 of 1 gene sets have been processed
	Sample 9 is being processed at (2015-07-21 17:21:23) ...
		 1 of 1 gene sets have been processed
	Sample 10 is being processed at (2015-07-21 17:21:26) ...
		 1 of 1 gene sets have been processed
	Sample 11 is being processed at (2015-07-21 17:21:29) ...
		 1 of 1 gene sets have been processed
	Sample 12 is being processed at (2015-07-21 17:21:31) ...
		 1 of 1 gene sets have been processed

End at 2015-07-21 17:21:34
Runtime in total is: 37 secs


    ## Comparing normalised enrichement score (NES)
frac_pvalue <- as.vector(eTerm$pvalue)
frac_fdr <- stats::p.adjust(frac_pvalue, method="BH")
frac_nes <- as.vector(eTerm$nes)
frac_es <- as.vector(eTerm$es)
df <- cbind(frac_es, frac_nes, frac_pvalue, frac_fdr)
rownames(df) <- colnames(eTerm$es)
rownames(df)[nrow(df)] <- "Mutation\nubiquity"
ind <- sort.int(frac_es, index.return=T)$ix
data <- df[ind,]
par(las=1) # make label text perpendicular to axis
par(mar=c(5,8,4,2)) # increase y-axis margin.
z <- data[,2]
barY <- barplot(z, xlab="Normalised enrichment score (NES)", horiz=TRUE, names.arg=rownames(data), cex.names=0.7, cex.lab=0.7, cex.axis=0.7, col="transparent")

    


    ## GSEA plot for ubiquity
visGSEA(eTerm, which_sample=1)

    


    
# Evolutionary analysis for genes in the subnetwork
## get a list of genes in the subnetwork
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="PS2", sizeRange=c(10,20000), min.overlap=0)

    
Start at 2015-07-21 17:21:36

First, load the ontology PS2 and its gene associations in the genome Hs (2015-07-21 17:21:36) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egPS' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egPS.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:21:36) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:21:36) ...
	There are 19 terms being used, each restricted within [10,20000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:21:36) ...

End at 2015-07-21 17:21:36
Runtime in total is: 0 secs


    ## Look at the evolution relevance along the path to the eukaryotic common ancestor
output <- dEnricherView(eTerm, top_num=NULL, sortBy="none", details=TRUE)
write.table(output, file="enrichment_PS2.txt", quote=F, row.names=F,col.names=T,sep="\t")
output

    

                        name nAnno nOverlap  zscore pvalue  adjp    namespace
11         33208:Metazoa   615        6  3.0600 0.0025 0.024      kingdom
13        6072:Eumetazoa   485        1 -0.4110 0.4400 0.470      no rank
15       33213:Bilateria   408        2  0.6760 0.1300 0.210      no rank
17   33511:Deuterostomia   672        6  2.8100 0.0041 0.026      no rank
18         7711:Chordata    75        2  3.7000 0.0016 0.024       phylum
19       7742:Vertebrata    96        0 -0.5460 0.2600 0.350      no rank
20   117571:Euteleostomi   568        3  0.9690 0.0960 0.170      no rank
21    8287:Sarcopterygii   106        0 -0.5740 0.2800 0.350      no rank
22       32523:Tetrapoda   123        0 -0.6190 0.3200 0.380      no rank
23         32524:Amniota   206        2  1.7300 0.0250 0.080      no rank
24        40674:Mammalia    13        0 -0.2000 0.0390 0.110        class
25          32525:Theria    75        1  1.6100 0.0220 0.080      no rank
26         9347:Eutheria    53        0 -0.4050 0.1500 0.220      no rank
27 1437010:Boreoeutheria    78        1  1.5600 0.0240 0.080      no rank
3         2759:Eukaryota  7067        9 -3.8900 1.0000 1.000 superkingdom
30         9443:Primates    23        0 -0.2670 0.0680 0.140        order
36      207598:Homininae    15        0 -0.2150 0.0450 0.110    subfamily
37     9606:Homo sapiens    34        0 -0.3240 0.1000 0.170      species
8     33154:Opisthokonta  3263       10 -0.0144 0.4200 0.470      no rank
     distance                                                        members
11 0.09260898                              ABL1,BTK,NCAM1,KAT6A,MAST1,TNRC6C
13  0.1117612                                                           TJP1
15   0.126603                                                     DSC2,ITGB1
17  0.1485278                             COL6A2,EPHA1,EPHA3,VAV1,HUWE1,TP53
18  0.1575984                                                    EPHB1,ITGAM
19  0.1695313                                                               
20  0.1829545                                            ARHGEF11,MAGI2,FAT4
21  0.1855467                                                               
22   0.188559                                                               
23  0.1924103                                                     ARAP2,AGRN
24  0.1955288                                                               
25  0.1991713                                                           UTRN
26  0.2026269                                                               
27  0.2040922                                                          HSPG2
3           0          ABCA1,COL19A1,CTNNB1,DNMT1,IDH1,TRPC6,TNIK,SIN3A,PAK7
30  0.2070882                                                               
36   0.223133                                                               
37  0.2334046                                                               
8  0.03880849 BRCA2,DNM2,NPM1,PTEN,CACNA1I,FAM13A,RAD50,STAT1,ARHGAP17,SMC1B

    

    ## load Entrezgene info
org.Hs.eg <- dRDataLoader(RData='org.Hs.eg')

    
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment

    gene_info <- org.Hs.eg$gene_info
entrez <- unlist(eTerm$overlap[6], use.names=F)
## build neighbor-joining tree
data <- frac_mutated[V(net)$name,]
tree_bs <- visTreeBootstrap(t(data), nodelabels.arg=list(cex=0.7,bg="white-pink-violet"), metric=c("euclidean","pearson","spearman","cos","manhattan","kendall","mi")[3], num.bootstrap=2000, plot.phylo.arg=list(cex=1, edge.width=1.2))

    
Start at 2015-07-21 17:21:36

First, build the tree (using nj algorithm and spearman distance) from input matrix (11 by 44)...
Second, perform bootstrap analysis with 2000 replicates...
Finally, visualise the bootstrapped tree...

Finish at 2015-07-21 17:21:39
Runtime in total is: 3 secs



    flag <- match(tree_bs$tip.label, colnames(data))
base <- sapply(eTerm$overlap, function(x){
    as.character(gene_info[match(x,rownames(gene_info)),2])
})
## reordering via hierarchical clustering
if(1){
    cluster_order <- matrix(1, nrow=length(base))
    base_order <- matrix(1, nrow=length(base))
    for(i in 1:length(base)){
        tmp <- base[[i]]
        ind <- match(tmp, rownames(data))
        if(length(ind)>0){
            base_order[ind] <- i
            tmpD <- data[ind,]
            if(length(tmp) != 1){
                distance <- as.dist(sDistance(tmpD, metric="pearson"))
                cluster <- hclust(distance, method="average")
                cluster_order[ind] <- cluster$order
            }else if(length(tmp) == 1){
                cluster_order[ind] <- 1
            }
        }
    }
    ## contruct data frame including 1st column for temporary index, 2nd for cluster order, 3rd for base/cluster ID
    df <- data.frame(ind=1:nrow(data), cluster_order, base_order)
    # order by: first base, then hexagon
    ordering <- df[order(base_order,cluster_order),]$ind
}
RowSideColors <- sapply(1:length(base), function(x) base_order==x)
RowSideColors <- t(RowSideColors)
rslab <- ifelse(eTerm$adjp<0.05," (FDR<0.05)","")
rslab <- paste(gsub(".*:","",eTerm$set_info$name), rslab, sep="")
rownames(RowSideColors) <- rslab
colnames(RowSideColors) <- rownames(data)
RowSideColors <- ifelse(RowSideColors==T, "gray","white")
RowSideColors <- RowSideColors[, ordering]
base_order1 <- base_order[ordering]
basesep_index <- sapply(unique(base_order1), function(x) which(base_order1[length(base_order1):1]==x)[1])
basesep_index <- basesep_index[1:length(basesep_index)-1]
labRow <- sapply(pvals[match(V(net)$name, names(pvals))], function(x){
    if(x < 0.005){
        " ***"
    }else if(x < 0.01){
        " **"
    }else if(x<0.05){
        " *"
    }else{
        ""
    }
})
labRow <- paste(rownames(data), labRow, sep="")
visHeatmapAdv(data=data[ordering,flag], Rowv=F, Colv=F, colormap="lightyellow-orange", zlim=c(0,0.12), keysize=1.5, RowSideColors=RowSideColors, RowSideWidth=2, RowSideLabelLocation="top", add.expr=abline(h=(basesep_index-0.5), lty=2,lwd=1,col="black"), offsetRow=-0.5, labRow=labRow[ordering], KeyValueName="Frequency", margins=c(6,6))

    


    
# Cross-tumor mutation ubiquity versus common ancestors
ind <- match(V(net)$name, rownames(ubiquity))
net_ubiquity <- ubiquity[ind]
net_ubiquity <- net_ubiquity[ordering]
names(net_ubiquity) <- rownames(data)[ordering]
data <- cbind(net_ubiquity, base_order1)
par(las=2, mar=c(12,8,4,2)) # all axis labels horizontal
lbls <- eTerm$set_info$name[unique(base_order1)]
lbls <- gsub(".*:","",lbls)
visBoxplotAdv(formula=net_ubiquity ~ base_order1, data=data, method=c("center","hex","square","swarm")[4], pch=19, xlab="", ylab="Cross-tumor mutation ubiquity", ylim=c(0,1), labels=lbls)

    
Warning message:
data length is not a multiple of split variable
Error: length(pie.out) == n.obs is not TRUE

    ## Deuterostomia versus all ancestors
stats::ks.test(x=net_ubiquity[base_order1==6], y=net_ubiquity, alternative="two.sided", exact=NULL)

    
Warning message:
cannot compute exact p-value with ties

    
	Two-sample Kolmogorov-Smirnov test

data:  net_ubiquity[base_order1 == 6] and net_ubiquity
D = 0.67424, p-value = 0.01645
alternative hypothesis: two-sided


    

    ## Deuterostomia versus ancestors before Deuterostomia
stats::ks.test(x=net_ubiquity[base_order1==6], y=net_ubiquity[base_order1<6], alternative="two.sided", exact=NULL)

    

    
	Two-sample Kolmogorov-Smirnov test

data:  net_ubiquity[base_order1 == 6] and net_ubiquity[base_order1 < 6]
D = 0.79762, p-value = 0.0008923
alternative hypothesis: two-sided


    

    ## Deuterostomia versus ancestors after Deuterostomia
stats::ks.test(x=net_ubiquity[base_order1==6], y=net_ubiquity[base_order1>6], alternative="two.sided", exact=NULL)

    

    
	Two-sample Kolmogorov-Smirnov test

data:  net_ubiquity[base_order1 == 6] and net_ubiquity[base_order1 > 6]
D = 0.72222, p-value = 0.02797
alternative hypothesis: two-sided


    

    
# GOBP enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="GOBP")

    
Start at 2015-07-21 17:21:40

First, load the ontology GOBP and its gene associations in the genome Hs (2015-07-21 17:21:40) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egGOBP' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egGOBP.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:21:40) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:21:40) ...
	There are 2251 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:21:41) ...

End at 2015-07-21 17:21:41
Runtime in total is: 1 secs


    ## write into the file called 'enrichment_GOBP.txt'
output <- dEnricherView(eTerm, top_num=NULL, sortBy="adjp", details=TRUE)
write.table(output, file="enrichment_GOBP.txt", quote=F, row.names=F,col.names=T,sep="\t")
## visualise the top significant terms in the GOBP heirarchy
## first, load the GOBP ontology
ig.GOBP <- dRDataLoader(RData='ig.GOBP')

    
'ig.GOBP' (from http://supfam.org/dnet/RData/1.0.7/ig.GOBP.RData) has been loaded into the working environment

    g <- ig.GOBP
## select the top most significant 10 terms
top <- dEnricherView(eTerm, top_num=10, details=TRUE)
top

    

                                                                  name nAnno
GO:0007411                                           axon guidance   379
GO:0007569                                              cell aging    24
GO:0018108                       peptidyl-tyrosine phosphorylation   120
GO:0043065                positive regulation of apoptotic process   277
GO:0051056 regulation of small GTPase mediated signal transduction   122
GO:0010976    positive regulation of neuron projection development    70
GO:0030198                       extracellular matrix organization   312
GO:0042692                             muscle cell differentiation    35
GO:0007264               small GTPase mediated signal transduction   472
GO:0016477                                          cell migration   172
           nOverlap zscore  pvalue    adjp namespace distance
GO:0007411       11   9.74 3.7e-10 2.1e-08   Process       14
GO:0007569        3  11.30 5.6e-07 1.3e-05   Process        4
GO:0018108        5   8.06 1.0e-06 1.3e-05   Process        9
GO:0043065        7   7.11 9.1e-07 1.3e-05   Process        8
GO:0051056        5   7.98 1.1e-06 1.3e-05   Process        8
GO:0010976        4   8.59 1.5e-06 1.4e-05   Process        8
GO:0030198        7   6.60 2.2e-06 1.8e-05   Process        5
GO:0042692        3   9.26 2.7e-06 2.0e-05   Process        5
GO:0007264        8   5.88 5.4e-06 3.5e-05   Process        7
GO:0016477        5   6.53 8.2e-06 4.8e-05   Process        6
                                                                         members
GO:0007411 ITGB1,ARHGEF11,AGRN,EPHA1,EPHB1,ABL1,CACNA1I,TRPC6,COL6A2,EPHA3,NCAM1
GO:0007569                                                       TP53,BRCA2,NPM1
GO:0018108                                            EPHA1,EPHB1,ABL1,BTK,EPHA3
GO:0043065                             TP53,ITGB1,DNM2,CTNNB1,ARHGEF11,ABL1,VAV1
GO:0051056                                   ARHGEF11,VAV1,FAM13A,ARHGAP17,ARAP2
GO:0010976                                                ITGB1,UTRN,MAGI2,EPHA3
GO:0030198                           ITGB1,COL19A1,HSPG2,AGRN,ITGAM,COL6A2,NCAM1
GO:0042692                                                      CTNNB1,ABL1,UTRN
GO:0007264                 ITGB1,CTNNB1,ARHGEF11,ABL1,VAV1,FAM13A,ARHGAP17,ARAP2
GO:0016477                                            PTEN,ITGB1,ABL1,PAK7,EPHA3

    

    nodes_query <- rownames(top)
nodes.highlight <- rep("red", length(nodes_query))
names(nodes.highlight) <- nodes_query
## induce the shortest paths (one for each significant term) to the ontology root
subg <- dDAGinduce(g, nodes_query, path.mode="shortest_paths")
## color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
visDAG(g=subg, data=-1*log10(eTerm$adjp[V(subg)$name]), node.info="both", node.attrs=list(color=nodes.highlight))

    


    
# GOMF enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="GOMF")

    
Start at 2015-07-21 17:22:21

First, load the ontology GOMF and its gene associations in the genome Hs (2015-07-21 17:22:21) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egGOMF' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egGOMF.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:22:21) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:22:21) ...
	There are 633 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:22:22) ...

End at 2015-07-21 17:22:22
Runtime in total is: 1 secs


    ## write into the file called 'enrichment_GOMF.txt'
output <- dEnricherView(eTerm, top_num=NULL, sortBy="adjp", details=TRUE)
write.table(output, file="enrichment_GOMF.txt", quote=F, row.names=F,col.names=T,sep="\t")
## visualise the top significant terms in the GOMF heirarchy
## first, load the GOMF ontology
ig.GOMF <- dRDataLoader(RData='ig.GOMF')

    
'ig.GOMF' (from http://supfam.org/dnet/RData/1.0.7/ig.GOMF.RData) has been loaded into the working environment

    g <- ig.GOMF
## select the top most significant 10 terms
top <- dEnricherView(eTerm, top_num=10, details=TRUE)
top

    

                                       name nAnno nOverlap zscore  pvalue    adjp
GO:0000287        magnesium ion binding   190        4   4.60 2.4e-04 0.00099
GO:0002020             protease binding    85        3   5.48 1.2e-04 0.00099
GO:0003690  double-stranded DNA binding    98        3   5.03 2.0e-04 0.00099
GO:0008022   protein C-terminus binding   172        4   4.90 1.5e-04 0.00099
GO:0019899               enzyme binding   309        5   4.30 2.8e-04 0.00099
GO:0019901       protein kinase binding   347        6   4.95 6.5e-05 0.00099
GO:0017124           SH3 domain binding   116        3   4.54 3.8e-04 0.00110
GO:0005096    GTPase activator activity   251        4   3.80 8.5e-04 0.00220
GO:0008134 transcription factor binding   266        4   3.64 1.1e-03 0.00260
GO:0004672      protein kinase activity   192        3   3.23 2.5e-03 0.00480
           namespace distance                         members
GO:0000287  Function        6            ABL1,IDH1,PTEN,MAST1
GO:0002020  Function        5                TP53,ITGB1,BRCA2
GO:0003690  Function        7               STAT1,TP53,CTNNB1
GO:0008022  Function        4          ABL1,CTNNB1,TJP1,HSPG2
GO:0019899  Function        4     PTEN,STAT1,TP53,CTNNB1,DNM2
GO:0019901  Function        6 PTEN,TP53,ITGB1,NPM1,UTRN,EPHA1
GO:0017124  Function        5              ABL1,DNM2,ARHGAP17
GO:0005096  Function        5  ARHGEF11,FAM13A,ARHGAP17,ARAP2
GO:0008134  Function        4         TP53,CTNNB1,DNMT1,KAT6A
GO:0004672  Function        6                 ABL1,EPHA1,TNIK

    

    nodes_query <- rownames(top)
nodes.highlight <- rep("red", length(nodes_query))
names(nodes.highlight) <- nodes_query
## induce the shortest paths (one for each significant term) to the ontology root
subg <- dDAGinduce(g, nodes_query, path.mode="shortest_paths")
## color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
visDAG(g=subg, data=-1*log10(eTerm$adjp[V(subg)$name]), node.info="both", node.attrs=list(color=nodes.highlight))

    


    
# MP enrichment analysis
#eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MP", ontology.algorithm="elim")
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MP", min.overlap=4)

    
Start at 2015-07-21 17:22:36

First, load the ontology MP and its gene associations in the genome Hs (2015-07-21 17:22:36) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMP' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMP.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:22:36) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:22:37) ...
	There are 4731 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:22:43) ...

End at 2015-07-21 17:22:44
Runtime in total is: 8 secs


    ## write into the file called 'enrichment_MP.txt'
output <- dEnricherView(eTerm, top_num=NULL, sortBy="adjp", details=TRUE)
write.table(output, file="enrichment_MP.txt", quote=F, row.names=F,col.names=T,sep="\t")
## visualise the top significant terms in the MP heirarchy
## first, load the MP ontology
ig.MP <- dRDataLoader(RData='ig.MP')

    
'ig.MP' (from http://supfam.org/dnet/RData/1.0.7/ig.MP.RData) has been loaded into the working environment

    g <- ig.MP
## select the top most significant 10 terms
top <- dEnricherView(eTerm, top_num=10, details=TRUE)
top

    

                                                           name nAnno nOverlap
MP:0000172                 abnormal bone marrow cell number   194       10
MP:0008011                                 intestine polyps    30        5
MP:0010269          decreased mammary gland tumor incidence    14        4
MP:0012422      decreased integument system tumor incidence    14        4
MP:0010352                    gastrointestinal tract polyps    33        5
MP:0000333                decreased bone marrow cell number   137        8
MP:0012416                  decreased gland tumor incidence    17        4
MP:0008021                     increased blastoma incidence    36        5
MP:0002398 abnormal bone marrow cell morphology/development   508       13
MP:0008215                 decreased immature B cell number   117        7
           zscore  pvalue    adjp           namespace distance
MP:0000172   9.66 7.2e-10 3.7e-07 Mammalian_phenotype        4
MP:0008011  13.00 3.8e-09 4.9e-07 Mammalian_phenotype        4
MP:0010269  15.40 3.4e-09 4.9e-07 Mammalian_phenotype        7
MP:0012422  15.40 3.4e-09 4.9e-07 Mammalian_phenotype        6
MP:0010352  12.40 7.1e-09 7.3e-07 Mammalian_phenotype        3
MP:0000333   9.27 9.1e-09 7.5e-07 Mammalian_phenotype        5
MP:0012416  13.90 1.0e-08 7.5e-07 Mammalian_phenotype        6
MP:0008021  11.80 1.2e-08 7.9e-07 Mammalian_phenotype        7
MP:0002398   7.12 2.6e-08 1.5e-06 Mammalian_phenotype        3
MP:0008215   8.78 5.0e-08 2.5e-06 Mammalian_phenotype       11
                                                                           members
MP:0000172                   PTEN,TP53,CTNNB1,KAT6A,BRCA2,IDH1,NPM1,RAD50,ABL1,BTK
MP:0008011                                            PTEN,TP53,ITGB1,CTNNB1,DNMT1
MP:0010269                                                  PTEN,TP53,ITGB1,CTNNB1
MP:0012422                                                  PTEN,TP53,ITGB1,CTNNB1
MP:0010352                                            PTEN,TP53,ITGB1,CTNNB1,DNMT1
MP:0000333                            PTEN,TP53,CTNNB1,KAT6A,BRCA2,IDH1,RAD50,ABL1
MP:0012416                                                  PTEN,TP53,ITGB1,CTNNB1
MP:0008021                                            PTEN,TP53,CTNNB1,BRCA2,RAD50
MP:0002398 PTEN,TP53,CTNNB1,KAT6A,STAT1,BRCA2,IDH1,NPM1,RAD50,ABL1,BTK,ABCA1,ITGAM
MP:0008215                                    TP53,KAT6A,RAD50,ABL1,BTK,HUWE1,VAV1

    

    nodes_query <- rownames(top)
nodes.highlight <- rep("red", length(nodes_query))
names(nodes.highlight) <- nodes_query
## induce all possible paths to the ontology root
subg <- dDAGinduce(g, nodes_query)
## color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
visDAG(g=subg, data=-1*log10(eTerm$adjp[V(subg)$name]), node.info=c("none","term_id","term_name","both","full_term_name")[5], layout.orientation=c("left_right","top_bottom","bottom_top","right_left")[1], node.attrs=list(color=nodes.highlight))

    


    
# DO enrichment analysis
#eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="DO", ontology.algorithm="pc")
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="DO")

    
Start at 2015-07-21 17:22:54

First, load the ontology DO and its gene associations in the genome Hs (2015-07-21 17:22:54) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egDO' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egDO.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:22:54) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:22:54) ...
	There are 917 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:22:56) ...

End at 2015-07-21 17:22:57
Runtime in total is: 3 secs


    ## write into the file called 'enrichment_DO.txt'
output <- dEnricherView(eTerm, top_num=NULL, sortBy="adjp", details=TRUE)
write.table(output, file="enrichment_DO.txt", quote=F, row.names=F,col.names=T,sep="\t")
## visualise the top significant terms in the DO heirarchy
## first, load the DO ontology
ig.DO <- dRDataLoader(RData='ig.DO')

    
'ig.DO' (from http://supfam.org/dnet/RData/1.0.7/ig.DO.RData) has been loaded into the working environment

    g <- ig.DO
## select the top most significant 10 terms
top <- dEnricherView(eTerm, top_num=10, details=TRUE)
top

    

                                                    name nAnno nOverlap zscore
DOID:3713                       ovary adenocarcinoma    31        4  10.10
DOID:10652                       Alzheimer's disease   397       10   6.14
DOID:4866    salivary gland adenoid cystic carcinoma    38        4   9.08
DOID:680                                   tauopathy   400       10   6.11
DOID:1037                     lymphoblastic leukemia   413       10   5.97
DOID:0050904                salivary gland carcinoma    55        4   7.40
DOID:0070004                                 myeloma   372        9   5.64
DOID:8618                         oral cavity cancer    55        4   7.40
DOID:8850                      salivary gland cancer    55        4   7.40
DOID:5683           hereditary breast ovarian cancer   221        7   5.95
              pvalue    adjp        namespace distance
DOID:3713    2.6e-07 5.0e-05 Disease_Ontology       10
DOID:10652   1.0e-06 5.2e-05 Disease_Ontology        6
DOID:4866    7.5e-07 5.2e-05 Disease_Ontology        8
DOID:680     1.1e-06 5.2e-05 Disease_Ontology        5
DOID:1037    1.5e-06 5.7e-05 Disease_Ontology        7
DOID:0050904 4.9e-06 1.0e-04 Disease_Ontology        7
DOID:0070004 4.7e-06 1.0e-04 Disease_Ontology        7
DOID:8618    4.9e-06 1.0e-04 Disease_Ontology        5
DOID:8850    4.9e-06 1.0e-04 Disease_Ontology        6
DOID:5683    5.9e-06 1.1e-04 Disease_Ontology        5
                                                              members
DOID:3713                                      TP53,PTEN,CTNNB1,BRCA2
DOID:10652   TP53,ABCA1,ITGAM,ITGB1,CTNNB1,ABL1,HSPG2,NCAM1,DNM2,AGRN
DOID:4866                                     TP53,CTNNB1,DNMT1,NCAM1
DOID:680     TP53,ABCA1,ITGAM,ITGB1,CTNNB1,ABL1,HSPG2,NCAM1,DNM2,AGRN
DOID:1037      TP53,ITGAM,ITGB1,PTEN,STAT1,ABL1,DNMT1,NCAM1,EPHA3,BTK
DOID:0050904                                  TP53,CTNNB1,DNMT1,NCAM1
DOID:0070004       TP53,ITGB1,STAT1,CTNNB1,ABL1,IDH1,NPM1,DNMT1,NCAM1
DOID:8618                                     TP53,CTNNB1,DNMT1,NCAM1
DOID:8850                                     TP53,CTNNB1,DNMT1,NCAM1
DOID:5683                       TP53,PTEN,STAT1,RAD50,ABL1,NPM1,BRCA2

    

    nodes_query <- rownames(top)
nodes.highlight <- rep("red", length(nodes_query))
names(nodes.highlight) <- nodes_query
## induce all possible shortest paths to the ontology root
subg <- dDAGinduce(g, nodes_query)
## color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
visDAG(g=subg, data=-1*log10(eTerm$adjp[V(subg)$name]), node.info="both", zlim=c(0,4), node.attrs=list(color=nodes.highlight))

    


    
# Pathway enrichment analysis
## uisng CP
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC2CP")

    
Start at 2015-07-21 17:23:03

First, load the ontology MsigdbC2CP and its gene associations in the genome Hs (2015-07-21 17:23:03) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC2CP' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC2CP.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:03) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:03) ...
	There are 245 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:04) ...

End at 2015-07-21 17:23:04
Runtime in total is: 1 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=TRUE)

    

                                         name nAnno nOverlap zscore  pvalue    adjp
M5193                      SIG_CHEMOTAXIS    45        4   5.49 5.5e-05 0.00088
M258                    PID_BARD1_PATHWAY    29        3   5.21 1.4e-04 0.00110
M1315 SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES    36        3   4.56 3.3e-04 0.00130
M233                      PID_EPO_PATHWAY    34        3   4.73 2.7e-04 0.00130
M5887             NABA_BASEMENT_MEMBRANES    40        3   4.27 5.0e-04 0.00160
M142                  PID_AJDISS_2PATHWAY    48        3   3.79 1.0e-03 0.00250
M67          PID_ARF6_TRAFFICKING_PATHWAY    49        3   3.73 1.1e-03 0.00250
M231                      PID_KIT_PATHWAY    52        3   3.59 1.4e-03 0.00270
M261           PID_P53_REGULATION_PATHWAY    59        3   3.28 2.2e-03 0.00390
M124                    PID_CXCR4_PATHWAY   102        4   3.12 2.5e-03 0.00410
      namespace
M5193        C2
M258         C2
M1315        C2
M233         C2
M5887        C2
M142         C2
M67          C2
M231         C2
M261         C2
M124         C2
                                                                          distance
M5193                                                  Genes related to chemotaxis
M258                                                        BARD1 signaling events
M1315                             Genes related to PIP3 signaling in B lymphocytes
M233                                                         EPO signaling pathway
M5887                   Genes encoding structural components of basement membranes
M142  Posttranslational regulation of adherens junction stability and dissassembly
M67                                                        Arf6 trafficking events
M231                Signaling events mediated by Stem cell factor receptor (c-Kit)
M261                                                                   p53 pathway
M124                                               CXCR4-mediated signaling events
                     members
M5193 BTK,PTEN,ARHGEF11,PAK7
M258         RAD50,TP53,NPM1
M1315          BTK,PTEN,VAV1
M233         BTK,STAT1,TRPC6
M5887      COL6A2,AGRN,HSPG2
M142        ABL1,CTNNB1,DNM2
M67        ITGB1,CTNNB1,DNM2
M231         PTEN,STAT1,VAV1
M261         ABL1,TP53,HUWE1
M124   PTEN,STAT1,ITGB1,VAV1

    

    ## using KEGG
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC2KEGG")

    
Start at 2015-07-21 17:23:04

First, load the ontology MsigdbC2KEGG and its gene associations in the genome Hs (2015-07-21 17:23:04) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC2KEGG' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC2KEGG.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:04) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:04) ...
	There are 186 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:05) ...

End at 2015-07-21 17:23:05
Runtime in total is: 1 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=TRUE)

    

                                                                name nAnno nOverlap
M5539                                         KEGG_AXON_GUIDANCE   129        6
M7098                              KEGG_ECM_RECEPTOR_INTERACTION    84        4
M7253                                        KEGG_FOCAL_ADHESION   200        6
M19877                                   KEGG_ENDOMETRIAL_CANCER    52        3
M2333                 KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION    57        3
M12868                                   KEGG_PATHWAYS_IN_CANCER   325        7
M2164                  KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION   117        4
M16376 KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC    75        3
M3126                                  KEGG_LEISHMANIA_INFECTION    72        3
M9726                                     KEGG_PANCREATIC_CANCER    70        3
       zscore  pvalue    adjp namespace
M5539    5.85 1.2e-05 0.00018        C2
M7098    4.83 1.6e-04 0.00100        C2
M7253    4.33 1.9e-04 0.00100        C2
M19877   4.71 2.8e-04 0.00110        C2
M2333    4.45 4.0e-04 0.00130        C2
M12868   3.59 7.0e-04 0.00170        C2
M2164    3.86 7.3e-04 0.00170        C2
M16376   3.72 1.1e-03 0.00180        C2
M3126    3.83 9.8e-04 0.00180        C2
M9726    3.90 8.9e-04 0.00180        C2
                                                     distance
M5539                                           Axon guidance
M7098                                ECM-receptor interaction
M7253                                          Focal adhesion
M19877                                     Endometrial cancer
M2333                   Pathogenic Escherichia coli infection
M12868                                     Pathways in cancer
M2164                    Leukocyte transendothelial migration
M16376 Arrhythmogenic right ventricular cardiomyopathy (ARVC)
M3126                                    Leishmania infection
M9726                                       Pancreatic cancer
                                       members
M5539        ABL1,PAK7,ITGB1,EPHA1,EPHA3,EPHB1
M7098                  ITGB1,COL6A2,HSPG2,AGRN
M7253       CTNNB1,PTEN,VAV1,PAK7,ITGB1,COL6A2
M19877                        TP53,CTNNB1,PTEN
M2333                        CTNNB1,ABL1,ITGB1
M12868 TP53,CTNNB1,PTEN,BRCA2,ABL1,ITGB1,STAT1
M2164                  CTNNB1,VAV1,ITGB1,ITGAM
M16376                       CTNNB1,ITGB1,DSC2
M3126                        ITGB1,STAT1,ITGAM
M9726                         TP53,BRCA2,STAT1

    

    ## uisng REACTOME
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC2REACTOME")

    
Start at 2015-07-21 17:23:05

First, load the ontology MsigdbC2REACTOME and its gene associations in the genome Hs (2015-07-21 17:23:05) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC2REACTOME' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC2REACTOME.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:05) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:05) ...
	There are 662 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:07) ...

End at 2015-07-21 17:23:07
Runtime in total is: 2 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=TRUE)

    

                                                     name nAnno nOverlap zscore
M8821                          REACTOME_AXON_GUIDANCE   243       10   7.60
M509                   REACTOME_DEVELOPMENTAL_BIOLOGY   388       11   6.23
M7169                     REACTOME_NCAM1_INTERACTIONS    39        4   8.16
M11187 REACTOME_NCAM_SIGNALING_FOR_NEURITE_OUT_GROWTH    64        4   6.15
M501                REACTOME_SIGNALING_BY_RHO_GTPASES   112        5   5.60
M508                    REACTOME_SIGNALING_BY_SCF_KIT    75        3   4.03
M872                      REACTOME_L1CAM_INTERACTIONS    84        3   3.73
M2049                      REACTOME_SIGNALING_BY_PDGF   118        3   2.92
M522                 REACTOME_CELL_CELL_COMMUNICATION   118        3   2.92
M529                                 REACTOME_MEIOSIS   112        3   3.04
        pvalue    adjp namespace
M8821  3.9e-08 8.6e-07        C2
M509   5.4e-07 5.9e-06        C2
M7169  2.0e-06 1.5e-05        C2
M11187 2.4e-05 1.3e-04        C2
M501   2.9e-05 1.3e-04        C2
M508   7.4e-04 2.7e-03        C2
M872   1.1e-03 3.6e-03        C2
M2049  3.9e-03 7.9e-03        C2
M522   3.9e-03 7.9e-03        C2
M529   3.3e-03 7.9e-03        C2
                                                      distance
M8821                          Genes involved in Axon guidance
M509                   Genes involved in Developmental Biology
M7169                     Genes involved in NCAM1 interactions
M11187 Genes involved in NCAM signaling for neurite out-growth
M501                Genes involved in Signaling by Rho GTPases
M508                    Genes involved in Signaling by SCF-KIT
M872                      Genes involved in L1CAM interactions
M2049                      Genes involved in Signaling by PDGF
M522                 Genes involved in Cell-Cell communication
M529                                 Genes involved in Meiosis
                                                                    members
M8821         DNM2,ITGB1,NCAM1,TRPC6,COL6A2,CACNA1I,AGRN,ARHGEF11,ABL1,PAK7
M509   DNM2,ITGB1,NCAM1,TRPC6,COL6A2,CACNA1I,AGRN,ARHGEF11,CTNNB1,ABL1,PAK7
M7169                                             NCAM1,COL6A2,CACNA1I,AGRN
M11187                                            NCAM1,COL6A2,CACNA1I,AGRN
M501                                    VAV1,ARHGEF11,FAM13A,ARHGAP17,ARAP2
M508                                                        STAT1,PTEN,VAV1
M872                                                       DNM2,ITGB1,NCAM1
M2049                                                     STAT1,PTEN,COL6A2
M522                                                     ITGB1,CTNNB1,MAGI2
M529                                                      BRCA2,RAD50,SMC1B

    

    ## uisng BIOCARTA
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC2BIOCARTA", min.overlap=2)

    
Start at 2015-07-21 17:23:07

First, load the ontology MsigdbC2BIOCARTA and its gene associations in the genome Hs (2015-07-21 17:23:07) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC2BIOCARTA' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC2BIOCARTA.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:08) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:08) ...
	There are 214 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:08) ...

End at 2015-07-21 17:23:08
Runtime in total is: 1 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=TRUE)

    

                                name nAnno nOverlap zscore  pvalue    adjp
M10628      BIOCARTA_ATM_PATHWAY    20        3   5.51 7.6e-05 0.00070
M9703   BIOCARTA_ATRBRCA_PATHWAY    21        3   5.35 9.3e-05 0.00070
M16334    BIOCARTA_EPHA4_PATHWAY    10        2   5.29 1.9e-04 0.00097
M4956  BIOCARTA_MONOCYTE_PATHWAY    11        2   5.02 2.6e-04 0.00099
M6220       BIOCARTA_AGR_PATHWAY    36        3   3.83 8.2e-04 0.00240
M11358      BIOCARTA_ARF_PATHWAY    17        2   3.88 1.0e-03 0.00260
M10145     BIOCARTA_PTEN_PATHWAY    18        2   3.74 1.2e-03 0.00270
M16355  BIOCARTA_NKCELLS_PATHWAY    20        2   3.50 1.7e-03 0.00320
M15926      BIOCARTA_TFF_PATHWAY    21        2   3.39 2.0e-03 0.00330
M3873  BIOCARTA_CHEMICAL_PATHWAY    22        2   3.29 2.3e-03 0.00340
       namespace                                                 distance
M10628        C2                                    ATM Signaling Pathway
M9703         C2    Role of BRCA1, BRCA2 and ATR in Cancer Susceptibility
M16334        C2     Eph Kinases and ephrins support platelet aggregation
M4956         C2                       Monocyte and its Surface Molecules
M6220         C2                    Agrin in Postsynaptic Differentiation
M11358        C2       Tumor Suppressor Arf Inhibits Ribosomal Biogenesis
M10145        C2           PTEN dependent cell cycle arrest and apoptosis
M16355        C2 Ras-Independent pathway in NK cell-mediated cytotoxicity
M15926        C2                 Trefoil Factors Initiate Mucosal Healing
M3873         C2            Apoptotic Signaling in Response to DNA Damage
                members
M10628  TP53,ABL1,RAD50
M9703  TP53,RAD50,BRCA2
M16334      ITGB1,EPHB1
M4956       ITGB1,ITGAM
M6220   ITGB1,UTRN,PAK7
M11358        TP53,ABL1
M10145       ITGB1,PTEN
M16355       ITGB1,VAV1
M15926     ITGB1,CTNNB1
M3873        TP53,STAT1

    

    
# SCOP superfamily domain enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="SF")

    
Start at 2015-07-21 17:23:08

First, load the ontology SF and its gene associations in the genome Hs (2015-07-21 17:23:08) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egSF' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egSF.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:09) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:09) ...
	There are 328 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:09) ...

End at 2015-07-21 17:23:10
Runtime in total is: 2 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=TRUE)

    

                                   name nAnno nOverlap zscore  pvalue    adjp
47769            SAM/Pointed domain   115        4   6.15 2.6e-05 0.00012
55550                    SH2 domain   112        4   6.25 2.3e-05 0.00012
56112 Protein kinase-like (PK-like)   526        8   5.11 2.5e-05 0.00012
57184 Growth factor receptor domain   127        4   5.79 4.2e-05 0.00015
49785 Galactose-binding domain-like    75        3   5.77 8.1e-05 0.00021
50156               PDZ domain-like   149        4   5.25 9.0e-05 0.00021
48350 GTPase activation domain, GAP    90        3   5.19 1.7e-04 0.00032
53300                      vWA-like    95        3   5.02 2.0e-04 0.00032
57196                   EGF/Laminin   174        4   4.76 1.9e-04 0.00032
49265          Fibronectin type III   200        4   4.34 3.6e-04 0.00050
      namespace distance                                    members
47769        sf   a.60.1                    EPHA1,EPHA3,EPHB1,ARAP2
55550        sf   d.93.1                        VAV1,STAT1,ABL1,BTK
56112        sf  d.144.1 EPHA1,EPHA3,EPHB1,ABL1,BTK,MAST1,TNIK,PAK7
57184        sf    g.3.9                     EPHA1,EPHA3,EPHB1,FAT4
49785        sf   b.18.1                          EPHA1,EPHA3,EPHB1
50156        sf   b.36.1                  ARHGEF11,TJP1,MAGI2,MAST1
48350        sf  a.116.1                      ARAP2,FAM13A,ARHGAP17
53300        sf   c.62.1                         COL6A2,ITGAM,ITGB1
57196        sf   g.3.11                      HSPG2,FAT4,AGRN,ITGB1
49265        sf    b.1.2                    EPHA1,EPHA3,EPHB1,NCAM1

    

    
# DGIdb druggable gene category enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="DGIdb", sizeRange=c(10,5000), min.overlap=2)

    
Start at 2015-07-21 17:23:10

First, load the ontology DGIdb and its gene associations in the genome Hs (2015-07-21 17:23:10) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egDGIdb' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egDGIdb.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:10) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:10) ...
	There are 36 terms being used, each restricted within [10,5000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:10) ...

End at 2015-07-21 17:23:10
Runtime in total is: 0 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=F)

    

                                                                 name nAnno
Clinically actionable                       Clinically actionable   237
Tyrosine kinase                                   Tyrosine kinase   151
Dna repair                                             Dna repair   385
Histone modification                         Histone modification   249
Transcription factor binding         Transcription factor binding   403
Tumor suppressor                                 Tumor suppressor   716
Kinase                                                     Kinase   823
Transcription factor complex         Transcription factor complex   282
External side of plasma membrane External side of plasma membrane   189
Drug resistance                                   Drug resistance   349
                                 nOverlap zscore  pvalue    adjp
Clinically actionable                  12  11.30 2.1e-12 3.1e-11
Tyrosine kinase                         5   5.60 2.8e-05 2.1e-04
Dna repair                              7   4.41 1.2e-04 5.9e-04
Histone modification                    5   3.99 4.5e-04 1.7e-03
Transcription factor binding            6   3.46 1.0e-03 3.0e-03
Tumor suppressor                        8   3.09 1.7e-03 4.3e-03
Kinase                                  8   2.66 4.5e-03 9.7e-03
Transcription factor complex            3   1.73 2.8e-02 5.2e-02
External side of plasma membrane        2   1.40 4.2e-02 7.1e-02
Drug resistance                         3   1.33 5.4e-02 8.1e-02

    

    
# TFBS enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC3TFT")

    
Start at 2015-07-21 17:23:11

First, load the ontology MsigdbC3TFT and its gene associations in the genome Hs (2015-07-21 17:23:11) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC3TFT' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC3TFT.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:11) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:11) ...
	There are 597 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:12) ...

End at 2015-07-21 17:23:13
Runtime in total is: 2 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=F)

    

                          name nAnno nOverlap zscore  pvalue   adjp
M15623           V$SOX5_01   265        5   4.81 1.2e-04 0.0016
M6517  AAAYWAACM_V$HFH4_01   254        5   4.94 9.2e-05 0.0016
M12520           V$SOX9_B1   237        4   3.97 6.3e-04 0.0059
M19808          V$RORA2_01   150        3   3.85 9.8e-04 0.0069
M14141           V$NRF2_Q4   255        3   2.61 6.7e-03 0.0150
M17117         V$E2F_Q3_01   235        3   2.78 5.0e-03 0.0150
M17588           V$RFX1_01   256        3   2.60 6.8e-03 0.0150
M18461           V$ARNT_01   260        3   2.56 7.1e-03 0.0150
M2315      V$NFKAPPAB65_01   237        3   2.77 5.2e-03 0.0150
M4468           V$FOXM1_01   246        3   2.68 5.9e-03 0.0150

    

    
# miRNA target enrichment analysis
eTerm <- dEnricher(data=V(net)$name, identity="symbol", genome="Hs", ontology="MsigdbC3MIR")

    
Start at 2015-07-21 17:23:13

First, load the ontology MsigdbC3MIR and its gene associations in the genome Hs (2015-07-21 17:23:13) ...
'org.Hs.eg' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.eg.RData) has been loaded into the working environment
'org.Hs.egMsigdbC3MIR' (from http://supfam.org/dnet/RData/1.0.7/org.Hs.egMsigdbC3MIR.RData) has been loaded into the working environment
Then, do mapping based on symbol (2015-07-21 17:23:13) ...
	Among 44 symbols of input data, there are 44 mappable via official gene symbols but 0 left unmappable
Third, perform enrichment analysis using HypergeoTest (2015-07-21 17:23:13) ...
	There are 212 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-21 17:23:14) ...

End at 2015-07-21 17:23:14
Runtime in total is: 1 secs


    dEnricherView(eTerm, top_num=10, sortBy="adjp", details=F)
    

                                                                   name nAnno
M10873                                              ATACCTC,MIR-202   179
M17260                                              ATGTACA,MIR-493   314
M19058                                              ATACTGT,MIR-144   199
M4317                                      CTCTGGA,MIR-520A,MIR-525   158
M698                                      CACTTTG,MIR-520G,MIR-520H   237
M9559                                               GTGCCAT,MIR-183   175
M13700                                      AAAGGGA,MIR-204,MIR-211   224
M10705                           TGCACTT,MIR-519C,MIR-519B,MIR-519A   448
M14709        TGTTTAC,MIR-30A-5P,MIR-30C,MIR-30D,MIR-30B,MIR-30E-5P   579
M18759 GCACTTT,MIR-17-5P,MIR-20A,MIR-106A,MIR-106B,MIR-20B,MIR-519D   595
       nOverlap zscore pvalue  adjp
M10873        3   2.58 0.0069 0.029
M17260        4   2.33 0.0100 0.029
M19058        3   2.35 0.0099 0.029
M4317         3   2.85 0.0044 0.029
M698          4   3.01 0.0031 0.029
M9559         3   2.63 0.0064 0.029
M13700        3   2.11 0.0150 0.036
M10705        4   1.54 0.0410 0.069
M14709        5   1.66 0.0340 0.069
M18759        5   1.59 0.0390 0.069

    Source demo

    TCGA.r

    Functions used in this demo