combineNetwork - combine two networks


The function combines two networks at a time. The networks might be computed from grinn functions such as fetchGrinnNetwork, fetchCorrGrinnNetwork, fetchDiffCorrGrinnNetwork, or from users' provided networks. The provided network must be in the following format:
nodes = data.frame(id,nodename,nodetype) #nodes contain at least 3 columns
edges = data.frame(source,target,relname) #edges contain at least 3 columns nwX or nwY = list(nodes,edges).


combineNetwork(nwX, nwY, returnAs)


nwXlist of nodes and edges to be combined.
nwYlist of nodes and edges to be combined.
returnAsstring of output type. Specify the type of the returned network. It can be one of "tab","json","cytoscape", default is "tab". "cytoscape" is the format used in Cytoscape.js


list of nodes and edges. The list is with the following componens: edges and nodes. Return empty list if error.


# Create metabolite-protein network from the list of metabolites using grinn ids and combine the grinn network to a correlation network
kw <- c('G160','G300','G371')
grinnnw <- fetchGrinnNetwork(txtInput=kw, from="metabolite", to="protein")
# Compute a correlation network of metabolites and expand to a grinn network of metabolite-protein
dummy <- rbind(nodetype=rep("metabolite"),t(mtcars))
colnames(dummy) <- c('G1.1','G15603','G371','G17295',paste0('G',sample(400:22000, 28)))
corrnw <- fetchCorrGrinnNetwork(datX=dummy, corrCoef=0.7, pval=1e-12, method="spearman", returnAs="tab", xTo="protein")
result <- combineNetwork(grinnnw,corrnw)

# Create metabolite-protein network from the list of metabolites using grinn ids and combine the grinn network to a provided network
txtInput <- list('G371','G783','G1.1')
grinnnw <- fetchGrinnNetwork(txtInput, from="metabolite", to="protein")
nwY = list()
nwY$nodes = data.frame(id=c("G371","G783","XXX","YYY"),nodename=c("G371","G783","XXX","YYY"),nodetype=c("metabolite","metabolite","protein","protein"),stringsAsFactors = FALSE)
nwY$edges = data.frame(source=c("G371","G783"),target=c("XXX","YYY"),corr_coef=c(-0.368,0.385),pval=c(0.000927,0.000497),reltype=c("Metabolite_Protein","Metabolite_Protein"),relname=c("CORRELATION","CORRELATION"),stringsAsFactors = FALSE)
result <- combineNetwork(grinnnw,nwY)

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