inst/GENT/networkd3.R

In SAFE-ICU/GeneExpressionNetworkToolkit: Gene Expression Network Toolkit

#setwd("E:\\RShinyapp\\networksanalysis\\code")
#setwd("C://Networks//code//")
library(networkD3)
require(htmlwidgets)
# Read a data set.
# Data format: dataframe with 3 variables; variables 1 & 2 correspond to interactions; variable 3 is weight of interaction
edgeList <- as.data.frame(read.csv("Edgelist_top_10prcnt_genes.csv"))
colnames(edgeList) <- c("SourceName", "TargetName", "Weight")

# Create a graph. Use simplyfy to ensure that there are no duplicated edges or self loops
gD <- igraph::simplify(igraph::graph.data.frame(edgeList, directed=FALSE))

# Create a node list object (actually a data frame object) that will contain information about nodes
nodeList <- data.frame(ID = c(0:(igraph::vcount(gD) - 1)), # because networkD3 library requires IDs to start at 0
                       nName = igraph::V(gD)$name)

# Map node names from the edge list to node IDs
getNodeID <- function(x){
  which(x == igraph::V(gD)$name) - 1 # to ensure that IDs start at 0
}
# And add them to the edge list
edgeList <- plyr::ddply(edgeList, .variables = c("SourceName", "TargetName", "Weight"),
                        function (x) data.frame(SourceID = getNodeID(x$SourceName),
                                                TargetID = getNodeID(x$TargetName)))

############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities and add them to the edge and node lists

# Calculate degree for all nodes
nodeList <- cbind(nodeList, nodeDegree=igraph::degree(gD, v = igraph::V(gD), mode = "all"))

# Calculate betweenness for all nodes
betAll <- igraph::betweenness(gD, v = igraph::V(gD), directed = FALSE) / (((igraph::vcount(gD) - 1) * (igraph::vcount(gD)-2)) / 2)
betAll.norm <- (betAll - min(betAll))/(max(betAll) - min(betAll))
nodeList <- cbind(nodeList, nodeBetweenness=100*betAll.norm) # We are scaling the value by multiplying it by 100 for visualization purposes only (to create larger nodes)
rm(betAll, betAll.norm)

#Calculate Dice similarities between all pairs of nodes
dsAll <- igraph::similarity.dice(gD, vids = igraph::V(gD), mode = "all")

F1 <- function(x) {data.frame(diceSim = dsAll[x$SourceID +1, x$TargetID + 1])}
edgeList <- plyr::ddply(edgeList, .variables=c("SourceName", "TargetName", "Weight", "SourceID", "TargetID"),
                        function(x) data.frame(F1(x)))

rm(dsAll, F1, getNodeID, gD)

############################################################################################
# We will also create a set of colors for each edge, based on their dice similarity values
# We'll interpolate edge colors based on the using the "colorRampPalette" function, that
# returns a function corresponding to a collor palete of "bias" number of elements (in our case, that
# will be a total number of edges, i.e., number of rows in the edgeList data frame)
F2 <- colorRampPalette(c("#FFFF00", "#FF0000"), bias = nrow(edgeList), space = "rgb", interpolate = "linear")
colCodes <- F2(length(unique(edgeList$diceSim)))
edges_col <- sapply(edgeList$diceSim, function(x) colCodes[which(sort(unique(edgeList$diceSim)) == x)])

rm(colCodes, F2)
############################################################################################
# Let's create a network

D3_network_LM <- networkD3::forceNetwork(Links = edgeList, # data frame that contains info about edges
                                         Nodes = nodeList, # data frame that contains info about nodes
                                         Source = "SourceID", # ID of source node
                                         Target = "TargetID", # ID of target node
                                         Value = "Weight", # value from the edge list (data frame) that will be used to value/weight relationship amongst nodes
                                         NodeID = "nName", # value from the node list (data frame) that contains node description we want to use (e.g., node name)
                                         Nodesize = "nodeBetweenness",  # value from the node list (data frame) that contains value we want to use for a node size
                                         Group = "nodeDegree",  # value from the node list (data frame) that contains value we want to use for node color
                                         height = 500, # Size of the plot (vertical)
                                         width = 1000,  # Size of the plot (horizontal)
                                         fontSize = 20, # Font size
                                         linkDistance = networkD3::JS("function(d) { return 10*d.value; }"), # Function to determine distance between any two nodes, uses variables already defined in forceNetwork function (not variables from a data frame)
                                         linkWidth = networkD3::JS("function(d) { return d.value/5; }"),# Function to determine link/edge thickness, uses variables already defined in forceNetwork function (not variables from a data frame)
                                         opacity = 0.95, # opacity
                                         zoom = TRUE, # ability to zoom when click on the node
                                         opacityNoHover = 0.2, # opacity of labels when static
                                         linkColour = edges_col) # edge colors

# Plot network
D3_network_LM

# Save network as html file
#networkD3::saveNetwork(D3_network_LM, "D3_LM.html", selfcontained = TRUE)