I have used the Pearson correlation method to infer a network of interacting genes from microarray data. And ultimately, I would like to study this network with 'WGCNA'. However as useful as some of the commands in WGCNA seem, I can't find anywhere in the tutorials a command that actually allows me to visualise the networks.... So I instead used the package 'network' to plot and visualise this network.....

Subsequently I'd like to use 'RCyjs' to visualise the networks via javascript and ultimately create a website. Does anyone know if it is possible to jointly use shiny and RCyjs to make a pipeline that can accept microarray data and via these packages, produce a network?? Or should I try to use Django (if that's possible)?

p1<-cor(t(E.rna[1:20,1:20]),use="p") #pearson correlation

plot(as.network.matrix(p1)) #network package

RCyjs(portRange=9047:9057, quiet=TRUE, graph=as.network.matrix(p1)); #RCyjs

Error in validObject(.Object) : 
  invalid class “RCyjsClass” object: invalid object for slot "graph" in class "RCyjsClass": got class "network", should be or extend class "graph"

RCyjs does not seem to accept networks and instead only takes 'graphs'.... how can I rectify this? If possible could anyone recommend alternate packages?

Christopher,

Sorry for my delay.   Here is a complete example modeled after yours.  Not having access to your E.rna matrix, I generated a small random network

library(RCyjs)
set.seed(3)
size <- 5
m <- matrix(runif(size * size), nrow=size, dimnames=list(LETTERS[1:size], LETTERS[1:size]))
m.cor <- cor(m)
diag(m.cor) <- 0

# create an adjacency matrix from the correlation, with edges between
# every pair of nodes with absolute correlation > threshold
threshold <- 0.45
m.adjacency.logical <- abs(m.cor) > threshold
m.adjacency <- 1 * m.adjacency.logical
g.am <- graphAM(m.adjacency)
g <- asg.am, "graphNEL")
nodeDataDefaults(g, attr="label") <- "not yet assigned"
nodeData(g, nodes(g), attr="label") <- nodes(g)
edgeDataDefaults(g, attr="cor") <- 0

# extract the corrleations to use as edge weights
m.corFiltered <- m.cor * m.adjacency
for(source.node in colnames(m.corFiltered)){
   correlations <- as.list(m.corFiltered[, source.node])
   correlations <- correlations[correlations != 0]
   if(length(correlations) > 0)
      edgeData(g, source.node, names(correlations), attr = "cor") <- as.numeric(correlations)
   }

rcy <- RCyjs(7000:7010, graph=g)
layout(rcy, "cose")
httpSetStyle(rcy, "style.js") # see below

Visual mapping rules are available as function calls in RCyjs, but I have found that using cytoscape.js native approach - loading json style files - to be easier overall.  Here is the verbatim text of the "style.js" referred to above:

vizmap = [

   {selector: "node", css: {
      "text-valign":"center",
      "text-halign":"center",
      "content":"data(label)",
      "background-color": "white",
      "border-color":"black","border-width":"1px",
      "shape": "ellipse",
      "width": 40,
      "height": 40,
      "font-size":"12px"}},

   {selector: 'edge', css:{
      "width": "2px",
      "curve-style": "bezier"
      }},

   {selector: 'edge[cor < 0]', css: {
      "line-color": "mapData(cor, -1.0, 0.0, rgb(220,255, 220), rgb(0, 255, 0))",
      "source-arrow-shape": "none",
      "target-arrow-shape": "triangle",
      "target-arrow-color": "rgb(0, 192, 0)",
      }},

   {selector: 'edge[cor > 0]', css: {
      "line-color": "mapData(cor, 0.0, 1.0, rgb(255,200,200), rgb(255, 0, 0))",
      "source-arrow-shape": "none",
      "target-arrow-shape": "triangle",
      "target-arrow-color": "rgb(192, 0, 0)",
      }}
      ];

Hi Christopher,

As you say, RCyjs (and RCy3) expect graphNEL objects, which are defined in the Bioconductor graph package.

It is quite easy  to convert among the various common graph/network representations.  If you will provide a small reproducible example (a few lines of code ought to be enough) I will respond with a few more lines of code which transform your network object into a graphNEL.

 Paul