## options(width = 75)

## options(useFancyQuotes=FALSE)

## 


## # generate the simulated network

## require(SANTA, quietly=TRUE)

## require(igraph, quietly=TRUE)

## set.seed(1) # for reproducibility

## g <- barabasi.game(n=500, power=1, m=1, directed=FALSE)


## # measure the distance between pairs of vertices in g

## dist.method <- "shortest.paths"

## D <- DistGraph(g, dist.method=dist.method, verbose=FALSE)


## # place the distances into discreet bins

## B <- BinGraph(D, verbose=FALSE)


## cluster.size <- 5

## s.to.use <- c(10, 20, 50, 100, 500)

## n.trials <- 10

## pvalues <- array(0, dim=c(n.trials, length(s.to.use)),

##     dimnames=list(NULL, as.character(s.to.use)))

## 

## # run the trials for each value of s

## for (s in s.to.use) {    	

##     for (i in 1:n.trials) {

##         # generate the hit set

##         seed.vertex <- sample(vcount(g), 1) # identify seed

##         sample.set <- order(D[seed.vertex, ])[1:s]

##         hit.set <- sample(sample.set, cluster.size)

## 

##         # measure the stength of association

##         g <- set.vertex.attribute(g, name="hits",

##             value=as.numeric(1:vcount(g) %in% hit.set))

##         pvalues[i, as.character(s)] <- Knet(g, nperm=100,

##             dist.method=dist.method, vertex.attr="hits",

##             B=B, verbose=FALSE)$pval

##     }

## }


## boxplot(-log10(pvalues), xlab="cutoff", ylab="-log10(p-value)")


## # cleanup

## rm(B, D, g)


## # create the network

## n.nodes <- 12

## edges <- c(1,2, 1,3, 1,4, 2,3, 2,4, 3,4, 1,5, 5,6,

##            2,7, 7,8, 4,9, 9,10, 3,11, 11,12)

## weights1 <- weights2 <- rep(0, n.nodes)

## weights1[c(1,2)] <- 1

## weights2[c(5,6)] <- 1

## 

## g <- graph.empty(n.nodes, directed=FALSE)

## g <- add.edges(g, edges)

## g <- set.vertex.attribute(g, "weights1", value=weights1)

## g <- set.vertex.attribute(g, "weights2", value=weights2)


## par(mfrow=c(1,2))

## 

## colors <- rep("grey", n.nodes)

## colors[which(weights1 == 1)] <- "red"

## g <- set.vertex.attribute(g, "color", value=colors)

## plot(g)

## 

## colors <- rep("grey", n.nodes)

## colors[which(weights2 == 1)] <- "red"

## g <- set.vertex.attribute(g, "color", value=colors)

## plot(g)

## 

## par(mfrow=c(1,1))

## g <- remove.vertex.attribute(g, "color")


## # set 1

## Knet(g, nperm=100, vertex.attr="weights1", verbose=FALSE)$pval

## Compactness(g, nperm=100, vertex.attr="weights1", verbose=FALSE)$pval

## 

## # set 2

## Knet(g, nperm=100, vertex.attr="weights2", verbose=FALSE)$pval

## Compactness(g, nperm=100, vertex.attr="weights2", verbose=FALSE)$pval


## # load igraph objects

## data(g.costanzo.raw)

## data(g.costanzo.cor)

## networks <- list(raw=g.costanzo.raw, cor=g.costanzo.cor)

## network.names <- names(networks)

## network.genes <- V(networks$raw)$name

## # genes identical across networks


## # obtain the GO term associations from org.Sc.sgd.db package

## library(org.Sc.sgd.db, quietly=TRUE)

## xx <- as.list(org.Sc.sgdGO2ALLORFS)

## go.terms <- c("GO:0000082", "GO:0003682", "GO:0007265",

##               "GO:0040008", "GO:0090329")

## # apply the GO terms to the networks

## for (name in network.names) {

##     for (go.term in go.terms) {

##         networks[[name]] <- set.vertex.attribute(

##             networks[[name]], name=go.term,

##             value=as.numeric(network.genes %in% xx[[go.term]]))

##     }

## }


## results <- list()

## for (name in network.names) {

##     results[[name]] <- Knet(networks[[name]], nperm=10,

##         vertex.attr=go.terms, edge.attr="distance", verbose=FALSE)

##     results[[name]] <- sapply(results[[name]],

##         function(res) res$pval)

## }


## # cleanup

## rm(g.costanzo.cor, g.costanzo.raw, network.genes, networks, xx)


## # load igraph objects

## data(g.bandyopadhyay.treated)

## data(g.bandyopadhyay.untreated)

## networks <- list(

##     treated=g.bandyopadhyay.treated,

##     untreated=g.bandyopadhyay.untreated

## )

## network.names <- names(networks)


## # obtain GO term associations

## library(org.Sc.sgd.db, quietly=TRUE)

## xx <- as.list(org.Sc.sgdGO2ALLORFS)

## # change to use alternative GO terms

## associated.genes <- xx[["GO:0006974"]]

## associations <- sapply(networks, function(g)

##     as.numeric(V(g)$name %in% associated.genes),

##     simplify=FALSE)

## networks <- sapply(network.names, function(name)

##     set.vertex.attribute(networks[[name]], "rdds",

##     value=associations[[name]]), simplify=FALSE)


## results <- sapply(networks, function(g) Knet(g, nperm=10,

##     dist.method="shortest.paths", vertex.attr="rdds",

##     edge.attr="distance"), simplify=FALSE)

## p.values <- sapply(results, function(res) res$pval)

## p.values


## # cleanup

## rm(xx, networks, g.bandyopadhyay.treated, g.bandyopadhyay.untreated, associated.genes)


## # laod igraph object

## data(g.srivas.high)

## data(g.srivas.untreated)

## networks <- list(

##     high=g.srivas.high,

##     untreated=g.srivas.untreated

## )

## network.names <- names(networks)


## # obtain GO term associations

## library(org.Sc.sgd.db, quietly=TRUE)

## xx <- as.list(org.Sc.sgdGO2ALLORFS)

## associated.genes <- xx[["GO:0000725"]]

## associations <- sapply(networks, function(g)

##     as.numeric(V(g)$name %in% associated.genes),

##     simplify=FALSE)

## networks <- sapply(network.names, function(name)

##     set.vertex.attribute(networks[[name]], "dsbr",

##     value=associations[[name]]), simplify=FALSE)


## p.values <- sapply(networks, function(g)

##        Knet(g, nperm=10, dist.method="shortest.paths",

##        vertex.attr="dsbr", edge.attr="distance")$pval)

## p.values


## # cleanup

## rm(xx, networks, g.srivas.high, g.srivas.untreated)


## # import and convert RNAi data

## data(rnai.cheung)

## rnai.cheung <- -log10(rnai.cheung)

## rnai.cheung[!is.finite(rnai.cheung)] <- max(rnai.cheung[is.finite(rnai.cheung)])

## rnai.cheung[rnai.cheung < 0] <- 0


## # import and create IntAct network

## data(edgelist.intact)

## g.intact <- graph.edgelist(as.matrix(edgelist.intact),

##     directed=FALSE)

## 

## # import data and create HumanNet network

## data(edgelist.humannet)

## g.humannet <- graph.edgelist(as.matrix(edgelist.humannet)[,1:2],

##     directed=FALSE)

## g.humannet <- set.edge.attribute(g.humannet, "distance",

##     value=edgelist.humannet$distance)

## networks <- list(intact=g.intact, humannet=g.humannet)


## network.names <- names(networks)

## network.genes <- sapply(networks, get.vertex.attribute,

##     name="name", simplify=FALSE)

## rnai.cheung.genes <- rownames(rnai.cheung)

## cancers <- colnames(rnai.cheung)

## 

## for (cancer in cancers) {

##     for (name in network.names) {

##         vertex.weights <-rep(NA, vcount(networks[[name]]))

##         names(vertex.weights) <- network.genes[[name]]

##         common.genes <- rnai.cheung.genes[rnai.cheung.genes

##             %in% network.genes[[name]]]

##         vertex.weights[common.genes] <- rnai.cheung[common.genes, cancer]

##         networks[[name]] <- set.vertex.attribute(networks[[name]],

##             cancer, value=vertex.weights)

##     }

## }


## knet.res <- sapply(networks, Knet, nperm=10,

##    dist.method="shortest.paths", vertex.attr=cancers,

##    edge.attr="distance", simplify=FALSE)

## p.values <- sapply(knet.res, function(i) sapply(i,

##    function(j) j$pval))

## p.values


## # cleanup

## rm(rnai.cheung, rnai.cheung.genes, networks, network.genes, edgelist.humannet, edgelist.intact, common.genes, g.humannet, g.intact)


## sessionInfo()

