#' @title Interactive Network Visualization
#' @description An interactive function to assist in the visualization of the result from INDEED
#' functions non_partial_corr() or patial_corr(). The size and the color of each node can be
#' adjusted by users to represent either the Node_Degree, Activity_Score, Z_Score, or P_Value.
#' The color of the edge is based on the binary value of either 1 corresponding to a positive
#' correlation depicted as green or a negative correlation of -1 depicted as red. Users also
#' have the option of having the width of each edge be proportional to its weight value. The
#' layout of the network can also be customized by choosing from the options: 'nice', 'sphere',
#' 'grid', 'star', and 'circle'. Nodes can be moved and zoomed in on. Each node and edge will
#' display extra information when clicked on. Secondary interactions will be highlighted as
#' well when a node is clicked on.
#' @param result This is the result from calling either non_partial_corr() or partial_corr().
#' @param nodesize This parameter determines what the size of each node will represent. The options
#' are 'Node_Degree', 'Activity_Score','P_Value' and 'Z_Score'. The title of the resulting
#' network will identify which parameter is selected to represent the node size. The default
#' is Node_Degree.
#' @param nodecolor This parameter determines what color each node will be based on a yellow to
#' blue color gradient. The options are 'Node_Degree', 'Activity_Score', 'P_Value', and '
#' Z_Score'. A color bar will be created based on which parameter is chosen. The default is
#' Activity_Score.
#' @param edgewidth This is a boolean value to indicate whether the edgewidth should be representative
#' of the weight connection (TRUE) or not (FALSE). The default is FALSE.
#' @param layout Users can choose from a a handful of network visualization templates including:
#' 'nice', 'sphere', 'grid', 'star', and 'circle'. The default is nice.
#' @examples result = non_partial_cor(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU,
#' method = "pearson", p_val = pvalue_M_GU, permutation = 1000,
#' permutation_thres = 0.05, fdr = FALSE)
#' network_display(result = result, nodesize = 'Node_Degree', nodecolor = 'Activity_Score',
#' edgewidth = FALSE, layout = 'nice')
#' @return An interactive depiction of the network resulting from INDEED functions
#' non_partial_corr() or patial_corr().
#' @import igraph
#' @import visNetwork
#' @importFrom grDevices topo.colors
#' @export
network_display <- function(result = NULL, nodesize= 'Node_Degree', nodecolor= 'Activity_Score',
edgewidth= FALSE, layout= 'nice'){
nodes <- result$activity_score
links <- result$diff_network
# Adding Z_Score to dataframe
Z_Score <- abs(qnorm(1 - (nodes$P_value)/2)) # trasfer p-value to z-score
nodes$zscore = Z_Score
vis.nodes <- data.frame(id= nodes$Node, name= nodes$ID,font.size = 24, pval= nodes$P_value,
ndegree= nodes$Node_Degree, ascore= nodes$Activity_Score,
zscore= nodes$zscore, stringsAsFactors = FALSE)
vis.links <- data.frame(from=links$Node1, to=links$Node2, binary= links$Binary,
weight= links$Weight)
vis.nodes$shape <- "dot"
vis.nodes$shadow <- TRUE # Nodes will drop shadow
# Information that will be displayed when hovering over a node
vis.nodes$title <- paste0("<p>", paste('ID: ', vis.nodes$name), "<br>","<br>",
paste('Node Degree: ', vis.nodes$ndegree),"<br>",
paste('Activity Score: ', vis.nodes$ascore),"<br>",
paste('P-value: ', vis.nodes$pval),"<br>",
paste('Z-score: ', round(vis.nodes$zscore, digits=3)),"</p>")
# Setting up the Node Size
if (missing(nodesize)){
vis.nodes$size <- (rank(-1 * vis.nodes$pval)+1)
nodesize <- 'p-value significance '
}
else if (nodesize == 'Node_Degree'){
vis.nodes$size <- ((vis.nodes$ndegree)+1)*5
nodesize <- 'Node Degree'
}
else if(nodesize == 'Activity_Score'){
vis.nodes$size <- ((vis.nodes$ascore)+1)*5
nodesize <- 'Activity Score'
}
else if(nodesize == 'P_Value'){
vis.nodes$size <- (rank(-1 * vis.nodes$pval)+1)
nodesize <- 'p-value significance'
}
else if(nodesize == 'Z_Score'){
vis.nodes$size <- ((vis.nodes$zscore)+1)*10
nodesize <- 'Z-Score'
} else {
vis.nodes$size <- (rank(-1 * vis.nodes$pval)+1)
nodesize <- 'p-value significance'
}
H <- "Higher"
L <- "Lower"
M <- " "
# Setting Up Node Color
if (missing(nodecolor)){
vis.nodes<- vis.nodes[order(vis.nodes$ascore, decreasing=TRUE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
nodecolor <- 'Activity Score'
}
else if (nodecolor == 'Node_Degree'){
vis.nodes<- vis.nodes[order(vis.nodes$ndegree, decreasing=TRUE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$ndegree), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$ndegree), alpha=1)
nodecolor <- 'Node Degree'
}
else if(nodecolor == 'Activity_Score'){
vis.nodes<- vis.nodes[order(vis.nodes$ascore, decreasing=TRUE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
nodecolor <- 'Activity Score'
}
else if(nodecolor == 'P_Value'){
vis.nodes<- vis.nodes[order(vis.nodes$pval, decreasing=FALSE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$pval), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$pval), alpha=1)
nodecolor <- 'p-value'
H <- "Lower"
L <- "Higher"
M <- " "
}
else if(nodecolor == 'Z_Score'){
vis.nodes<- vis.nodes[order(vis.nodes$zscore, decreasing=TRUE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$zscore), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$zscore), alpha=1)
nodecolor <- 'Z-Score'
}
else {
vis.nodes<- vis.nodes[order(vis.nodes$ascore, decreasing=TRUE), ]
vis.nodes$color.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
vis.nodes$color.highlight.background <- topo.colors(length(vis.nodes$ascore), alpha=1)
nodecolor <- 'Activity Score'
}
vis.nodes$borderWidth <- 2 # Node border width
vis.nodes$label <- vis.nodes$name # Node label
vis.nodes$color.highlight.border <- "darkred"
vis.nodes$color.border <- "black"
# Setting up edge width parameter
if (edgewidth == TRUE ){vis.links$width <- abs(vis.links$weight) * 3
} else {vis.links$width <- 3}
# Information that will be displayed when hovering over the edge
vis.links$title <- paste0("<p>", paste('Edge Weight: ',
round(abs(vis.links$weight), digits= 3)), "</p>")
vis.links$color[vis.links$binary == 1] <- "green" # line color
vis.links$color[vis.links$binary == -1] <- "red"
vis.links$arrowStrikethrough <- FALSE
vis.links$smooth <- TRUE # should the edges be curved?
vis.links$shadow <- TRUE # edge shadow
# Setting up layout of network
if (missing(layout)){l <- "layout_nicely"}
else if (layout == 'nice'){l <- "layout_nicely"}
else if (layout == 'sphere'){l <- "layout_on_sphere"}
else if (layout == 'star'){l <- "layout_as_star"}
else if (layout == 'grid'){l <- "layout_on_grid"}
else if (layout == 'circle'){l <- "layout_in_circle"
} else {l <- "layout_nicely"}
lnodes <- data.frame(label= c(H, M, L), position = "left", shape= c("circle"),
color= c("blue", "green", "yellow"))
ledges <- data.frame(color= c("green", "red"), label= c("Positive Change in Correlation",
"Negative Change in Correlation"),
font.align= "top", arrows= c("NA", "NA"), width= 4)
net <- visNetwork(vis.nodes, vis.links, width = "100%", height = "800px", main= "INDEED",
submain= paste("Node size represents: ", nodesize)) %>%
visOptions( highlightNearest= TRUE, nodesIdSelection= TRUE) %>%
visIgraphLayout(layout=l) %>%
visInteraction( dragView= TRUE, dragNodes= TRUE, zoomView= TRUE, navigationButtons= FALSE,
hideEdgesOnDrag= FALSE, multiselect = TRUE) %>%
visLegend(addEdges= ledges, addNodes= lnodes, position= "right", useGroups= FALSE, ncol=1,
main= paste("Node color based on ", nodecolor), width= 0.2, zoom = TRUE)
print(net)
}
Datasets
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