SCCgraph: Combines Strongly Connected Components into single nodes

In nem: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies

Description

SCCgraph is used to identify all nodes which are not distinguishable given the data.

Usage

SCCgraph(x,name=TRUE,nlength=20)

Arguments

x	graphNEL object or an adjacency matrix
name	Concatenate all names of summarized nodes, if TRUE, or number nodes, if FALSE. Default: TRUE
nlength	maximum length of names

Details

A graph inferred by either nem or nemModelSelection may have cycles if some phenotypic profiles are not distinguishable. The function SCCgraph identifies cycles in the graph (the strongly conneced components) and summarizes them in a single node. The resulting graph is then acyclic.

Value

graph	a graphNEL object with connected components of the input graph summarized into single nodes
scc	a list mapping SCCs to nodes
which.scc	a vector mapping nodes to SCCs

Author(s)

Florian Markowetz, Holger Froehlich

See Also

nem, transitive.reduction

Examples

   data("BoutrosRNAi2002")
   D   <- BoutrosRNAiDiscrete[,9:16]
   res <- nem(D,control=set.default.parameters(unique(colnames(D)), para=c(.13,.05)))
   # 
   sccg <- SCCgraph(res$graph,name=TRUE)
   #
   par(mfrow=c(1,2))
   if(require(Rgraphviz)){
    plot.nem(res, main="inferred from data")      
    plot(sccg$graph, main="condensed (rel,key)")
    }

nem documentation built on Oct. 31, 2019, 2:12 a.m.