dnet (version 1.1.7)

dNetFind: Function to find heuristically maximum scoring subgraph

Description

dNetFind is supposed to find the maximum scoring subgraph from an input graph and scores imposed on its nodes. The input graph and the output subgraph are both of "igraph" or "graphNET" object. The input scores imposed on the nodes in the input graph can be divided into two parts: the positive nodes and the negative nodes. The searching for maximum scoring subgraph is deduced to find the connected subgraph containing the positive nodes as many as possible, but the negative nodes as few as possible. To this end, a heuristic search is used (see Note below).

Usage

dNetFind(g, scores)

Arguments

g

an object of class "igraph" or "graphNEL"

scores

a vector of scores. For each element, it must have the name that could be mapped onto the input graph. Also, the names in input "scores" should contain all those in the input graph "g", but the reverse is not necessary

Value

a subgraph with a maximum score, an object of class "igraph" or "graphNEL". It has node attributes 'score' and 'type' (either 'desired' or 'linker')

See Also

dNetFind

Examples

# NOT RUN {
# 1) generate an vector consisting of random values from beta distribution
x <- rbeta(1000, shape1=0.5, shape2=1)

# 2) fit a p-value distribution under beta-uniform mixture model
fit <- dBUMfit(x, ntry=1, hist.bum=FALSE, contour.bum=FALSE)

# 3) calculate the scores according to the fitted BUM and fdr=0.01
# using "pdf" method
scores <- dBUMscore(fit, method="pdf", fdr=0.05, scatter.bum=FALSE)
names(scores) <- as.character(1:length(scores))

# 4) generate a random graph according to the ER model
g <- erdos.renyi.game(1000, 1/100)

# 5) produce the induced subgraph only based on the nodes in query
subg <- dNetInduce(g, V(g), knn=0)

# 6) find the subgraph with the maximum score
subgraph <- dNetFind(subg, scores)
# }