R/explodeLayout.R
In DIVA-Lab-UTMB/epl: ExplodeLayout algorithm for network visulization
Defines functions
explodelayout
Documented in
explodelayout
#'Layout a network using the ExplodeLayout Algorithm.
#'
#'The explodelayout function applies the ExplodeLayout Algorithm to help user visuallize
#'and analyze their network data.
#'
#'
#'The explodelayout function takes as input two main files:
#'1. A nodelist file which containing the lables, original coordinates,
#'outcome (like case or control), entity, and cluster membership for all
#'nodes in the network.
#'2. A network file which represents the network.
#'
#'Data Format:
#'The nodelist file must be a dataframe with 8 columns, named as 'Label', 'FRX', 'FRY'
#''KKX', 'KKY', 'Outcome', 'Cluster', 'Entity'
#'The network file must be a dataframe or matrix. When it's a bipartite network, the column names
#'should be the variable names and row names should be the subjects names. When it's unipartite,
#'column names and row names are the same. The first column of the network file should be the outcome
#'of the nodes.
#'
#'Please note: the names in the network files MUST be consistent with the names in the network files.
#'
#'Output: the function will export the layout of network with different radius into 21 png files, which
#'includes 1 original layout graph and 20 layout graphs with radius of 0.1, 0.2, ... 1.9, 2.0. The
#'corresponding CCS scores of different radius will be saved into a 'statList.dat' file, which would
#'enable user to pick the best CCS score graph.
#'@param nodelistFile a nodelist, which is a dataframe with first column as the name of nodes
#'@param networkFile a network, which is a matrix or dataframe with first column as the outcome
#'@param data_name name your data so that we know how to name your results files
#'@param labelOn labels of nodes on or not. should be 'yes' or 'no'
#'@examples
#'explodelayout(nl1, net1, 'no','sample_data')
#'
#'@references \url{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543384/}
#'
#'
#'
#'@export
explodelayout <- function(nodelistFile, networkFile, labelOn = 'no', data_name = 'data') {
#para <- read.table('explodeLayout_input.txt', header = F, stringsAsFactors = F)
#data_name <- para[1,]
#labelOn <- para[2,]
################ DO NOT modify anything below this line ###############################
#######################################################################################
#devtools::load_all()
#import library functions written in separated scripts
#libPath1 <- paste(getwd(), 'ExplodeLayout_Library','libraries.R', sep = '/')
#libPath2 <- paste(getwd(), 'ExplodeLayout_Library','functions.R', sep = '/')
#libPath3 <- paste(getwd(), 'ExplodeLayout_Library','explodeLayoutMethodLibrary_uni&bi.R', sep = '/')
#source(libPath1)
#source(libPath2)
#source(libPath3)
#setting default parameters for explodeLayout
input <- list()
rvalues <- list()
#true means there's coordinates file to input
input$coordfileCheckBox <- TRUE
#choose layout algorithm if no initial coodinates are read in
input$layoutAlgoType <- 'fr'
#choose equil distance centroids on the explodeLayout circle
input$radialOrEquiDist <- "equidist"
#number of circles for explodeLayout
input$rings <-1
#choose how to decide centroids for each cluster
input$selectAlgoForCentroids <- 'median'
#choose whether to shift or rotate clusters
input$shiftOrRotate <- "rotate"
#choose view types (2 means the standard explodeLayout, other views would be added later)
input$viewTypes <- '2' #ExplodeLayout View
input$displace <- 0.4 #???
#vertex size
input$vertexSize <- 3
#vertex label size
input$labelSize <- 4
#show node degree or not
input$nodeDegree <- FALSE
#show edge weight or not
input$edgeWeight <- FALSE
#choose node color mode (color by clusters, outcome, etc..)
input$nodeColor <- '2' #cluster color
#set default edge thickness or not
input$edgeThickness <- 0.1
#select the picture format to export
input$selectExportFileType <- 'png'
#specify a few path for input and output files
#basePath <- c(wd=paste(getwd(),'/Project', sep = ''))
#exportPath <- c(wd=normalizePath("~"))
#userSelectedDataFolder <- data_name
#userSelectedDataFolderFullPath <- paste(basePath, '/', userSelectedDataFolder, sep = "")
#dataFolder <- paste(userSelectedDataFolderFullPath,'/','Data',sep = "")
#rvalues$dataFolder <- dataFolder
#rvalues$resultsFolder <- paste(userSelectedDataFolderFullPath,'/','Results',sep = "")
#import data files provided by users
#input$nodeListFile <- paste(userSelectedDataFolder, '/Data/', userSelectedDataFolder, '_nodelist.dat',sep = "")
#input$networkFile <- paste(userSelectedDataFolder, '/Data/', userSelectedDataFolder, '_subset.txt',sep = "")
#nodelistFile <- paste(basePath, '/',input$nodeListFile, sep = "")
#nodelist_name <- paste(data_name, '_nodelist.dat',sep = "")
#network_name <- paste(data_name, '_subset.txt',sep = "")
#nodelistFile <- system.file("extdata", nodelist_name, package = "epl") #full path of nodelist
#networkFile <- system.file("extdata", network_name, package = "epl") #full path of network
#put nodelistFile and networkFile as two parameters
nodelist <- convertNodeListFile(nodelistFile, input)
rvalues$netType <- length(unique(nodelist$entity$Entity))
if (rvalues$netType > 2) {stop("Data not supported. Make sure your input has only one or two entities.")}
#get data from file, edgelist.dat file is saved
#rvalues$dataFromFile <- getData(basePath, input, rvalues, nodelist$modules)
rvalues$dataFromFile <- getDataFromFile(networkFile, nodelist$modules, rvalues)
rvalues$entity <- nodelist$entity #as.matrix(entity)
#rvalues$colnames <- colnames(rvalues$dataFromFile$df)
rvalues$colnames <- nodelist$coords[,1]
rvalues$nrows <- nrow(rvalues$dataFromFile$df)
rvalues$outcome <- nodelist$outcomes
rvalues$coord <- getOriginalNetworkCoordinates(input, rvalues$dataFromFile$g, nodelist$coords)
res <- findClosestRadius(input, rvalues$dataFromFile, rvalues$coord)
radius <- res$radius
rvalues$originalScore <- res$originalScore
rvalues$ClosestRadius <- radius
GOEresult <- findRadiusWithMaxGOE(input, rvalues$dataFromFile, rvalues$coord)
maxRadius <- GOEresult[1]
rvalues$displace <- maxRadius
goodnessscore <- as.numeric(sprintf("%.4f",as.numeric(GOEresult[2])))
#calculate coordinates for networks using ExplodeLayout at different radius, then store them
#coordinates.dat and statList.dat files are saved
returnExplodeNetwork <- storeCoordinatesForExplodeLayout(rvalues$dataFromFile, input, rvalues$coord, rvalues)
explode.net <- getCoordinatesForExplodeLayout(rvalues$dataFromFile, input, rvalues$coord, rvalues)
if(rvalues$dataFromFile$nclust != 1){
explodeGoodness <- calculateScore(input, explode.net, rvalues$dataFromFile$nclust)
} else{
explodeGoodness <- "NA"
}
explode.net <- formatExplodeNetMatrix(explode.net, rvalues, input)
#cat(paste(input$displace, ";", explodeGoodness,"\n", sep=""))
######################################## generating graphs ###############################################
ranges <- list()
#input$visualDirectorypath <- rvalues$resultsFolder
#if (file.exists('coordinates.data')) {
#returnExplodeNetwork <- data.frame(data.table::fread('coordinates.dat', sep = "\t"))}
columnNames <- colnames(returnExplodeNetwork)
coordX <- columnNames[2]
coordY <- columnNames[3]
explodeNet <- cbind.data.frame(returnExplodeNetwork[,1], returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordX],
returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordY], returnExplodeNetwork[,4],
returnExplodeNetwork[,5], returnExplodeNetwork[,6])
colnames(explodeNet) <- c(columnNames[1], coordX, coordY, columnNames[4], columnNames[5], columnNames[6] )
returnList <- list("explode.net" = explodeNet)#, "edgeList" = edgeList)
newSliderValues <- returnList
explode.net <- newSliderValues$explode.net
ranges$x <- c(min(explode.net[,2]), max(explode.net[,2]))
ranges$y <- c(min(explode.net[,3]), max(explode.net[,3]))
labelsData <- getDataInBoundingBox(newSliderValues$explode.net , ranges, rvalues)
lab <- labelsData$colnames
if(rvalues$netType == 2) {
lab <- c(rep(NA, labelsData$nrows), labelsData$colnames)
}
p <- suppressWarnings(plotnetwork(input, newSliderValues$explode.net, rvalues, 1, labelsData))
plotcord <- as.data.frame(newSliderValues$explode.net[,2:3])
colnames(plotcord) = c("X1", "X2")
# generate and save the original graph (before applying the ExplodeLayout algorithm)
p <- p + ggplot2::coord_cartesian(xlim = ranges$x, ylim = ranges$y)
if(labelOn == 'yes' && ((input$labelSize != 0) && (!is.na(input$labelSize)))){
p <- p + suppressWarnings( #rvalues$colnames
ggrepel::geom_label_repel(ggplot2::aes(plotcord$X1, plotcord$X2,label=lab, alpha=0.5),
na.rm = TRUE,
size = input$labelSize,
fontface = "bold" ,
box.padding = ggplot2::unit(0.5, "lines"),
point.padding = ggplot2::unit(0.5, "lines"),
segment.color = "#0000FF"
# remove lollipop stick : segment.size = 0
)
)
}
rvalues$plot <- p
exportFile <- paste(data_name, '_original','.',input$selectExportFileType, sep="")
ggplot2::ggsave(exportFile, rvalues$plot, device = input$selectExportFileType, width = 15, height = 15, units = 'cm' )
# generate and save 20 graphs using ExplodeLayout with radius from 0.1 to 2.0, step size is 0.1
for (i in 1 : 20) {
coordX <- columnNames[2 * i + 5]
coordY <- columnNames[2 * i + 6]
explodeNet <- cbind.data.frame(returnExplodeNetwork[,1], returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordX],
returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordY], returnExplodeNetwork[,4],
returnExplodeNetwork[,5], returnExplodeNetwork[,6])
colnames(explodeNet) <- c(columnNames[1], coordX, coordY, columnNames[4], columnNames[5], columnNames[6] )
returnList <- list("explode.net" = explodeNet)#, "edgeList" = edgeList)
newSliderValues <- returnList
explode.net <- newSliderValues$explode.net
#ranges$x <- c(min(explode.net[,2]), max(explode.net[,2]))
#ranges$y <- c(min(explode.net[,3]), max(explode.net[,3]))
p <- suppressWarnings(plotnetwork(input, newSliderValues$explode.net, rvalues, 1, labelsData))
#plotcord <- as.data.frame(matrix(as.numeric(sliderValues[,2:3]), nrow(sliderValues), 2, byrow=F))
plotcord <- as.data.frame(newSliderValues$explode.net[,2:3])
colnames(plotcord) = c("X1", "X2")
p <- p + ggplot2::coord_cartesian(xlim = ranges$x, ylim = ranges$y)
if(labelOn == 'yes' && ((input$labelSize != 0) && (!is.na(input$labelSize)))){
p <- p + suppressWarnings( #rvalues$colnames
ggrepel::geom_label_repel(ggplot2::aes(plotcord$X1, plotcord$X2,label=lab, alpha=0.5),
na.rm = TRUE,
size = input$labelSize,
fontface = "bold" ,
box.padding = ggplot2::unit(0.5, "lines"),
point.padding = ggplot2::unit(0.5, "lines"),
segment.color = "#0000FF"
# remove lollipop stick : segment.size = 0
)
)
}
rvalues$plot <- p
exportFile <- paste(data_name,'_radius',as.double(i/10),'.',input$selectExportFileType, sep="")
ggplot2::ggsave(exportFile, rvalues$plot, device = input$selectExportFileType, width = 15, height = 15, units = 'cm' )
}
}
DIVA-Lab-UTMB/epl documentation built on April 11, 2022, 3:46 p.m.
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Defines functions explodelayout
Documented in explodelayout
#'Layout a network using the ExplodeLayout Algorithm.
#'
#'The explodelayout function applies the ExplodeLayout Algorithm to help user visuallize
#'and analyze their network data.
#'
#'
#'The explodelayout function takes as input two main files:
#'1. A nodelist file which containing the lables, original coordinates,
#'outcome (like case or control), entity, and cluster membership for all
#'nodes in the network.
#'2. A network file which represents the network.
#'
#'Data Format:
#'The nodelist file must be a dataframe with 8 columns, named as 'Label', 'FRX', 'FRY'
#''KKX', 'KKY', 'Outcome', 'Cluster', 'Entity'
#'The network file must be a dataframe or matrix. When it's a bipartite network, the column names
#'should be the variable names and row names should be the subjects names. When it's unipartite,
#'column names and row names are the same. The first column of the network file should be the outcome
#'of the nodes.
#'
#'Please note: the names in the network files MUST be consistent with the names in the network files.
#'
#'Output: the function will export the layout of network with different radius into 21 png files, which
#'includes 1 original layout graph and 20 layout graphs with radius of 0.1, 0.2, ... 1.9, 2.0. The
#'corresponding CCS scores of different radius will be saved into a 'statList.dat' file, which would
#'enable user to pick the best CCS score graph.
#'@param nodelistFile a nodelist, which is a dataframe with first column as the name of nodes
#'@param networkFile a network, which is a matrix or dataframe with first column as the outcome
#'@param data_name name your data so that we know how to name your results files
#'@param labelOn labels of nodes on or not. should be 'yes' or 'no'
#'@examples
#'explodelayout(nl1, net1, 'no','sample_data')
#'
#'@references \url{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543384/}
#'
#'
#'
#'@export
explodelayout <- function(nodelistFile, networkFile, labelOn = 'no', data_name = 'data') {
#para <- read.table('explodeLayout_input.txt', header = F, stringsAsFactors = F)
#data_name <- para[1,]
#labelOn <- para[2,]
################ DO NOT modify anything below this line ###############################
#######################################################################################
#devtools::load_all()
#import library functions written in separated scripts
#libPath1 <- paste(getwd(), 'ExplodeLayout_Library','libraries.R', sep = '/')
#libPath2 <- paste(getwd(), 'ExplodeLayout_Library','functions.R', sep = '/')
#libPath3 <- paste(getwd(), 'ExplodeLayout_Library','explodeLayoutMethodLibrary_uni&bi.R', sep = '/')
#source(libPath1)
#source(libPath2)
#source(libPath3)
#setting default parameters for explodeLayout
input <- list()
rvalues <- list()
#true means there's coordinates file to input
input$coordfileCheckBox <- TRUE
#choose layout algorithm if no initial coodinates are read in
input$layoutAlgoType <- 'fr'
#choose equil distance centroids on the explodeLayout circle
input$radialOrEquiDist <- "equidist"
#number of circles for explodeLayout
input$rings <-1
#choose how to decide centroids for each cluster
input$selectAlgoForCentroids <- 'median'
#choose whether to shift or rotate clusters
input$shiftOrRotate <- "rotate"
#choose view types (2 means the standard explodeLayout, other views would be added later)
input$viewTypes <- '2' #ExplodeLayout View
input$displace <- 0.4 #???
#vertex size
input$vertexSize <- 3
#vertex label size
input$labelSize <- 4
#show node degree or not
input$nodeDegree <- FALSE
#show edge weight or not
input$edgeWeight <- FALSE
#choose node color mode (color by clusters, outcome, etc..)
input$nodeColor <- '2' #cluster color
#set default edge thickness or not
input$edgeThickness <- 0.1
#select the picture format to export
input$selectExportFileType <- 'png'
#specify a few path for input and output files
#basePath <- c(wd=paste(getwd(),'/Project', sep = ''))
#exportPath <- c(wd=normalizePath("~"))
#userSelectedDataFolder <- data_name
#userSelectedDataFolderFullPath <- paste(basePath, '/', userSelectedDataFolder, sep = "")
#dataFolder <- paste(userSelectedDataFolderFullPath,'/','Data',sep = "")
#rvalues$dataFolder <- dataFolder
#rvalues$resultsFolder <- paste(userSelectedDataFolderFullPath,'/','Results',sep = "")
#import data files provided by users
#input$nodeListFile <- paste(userSelectedDataFolder, '/Data/', userSelectedDataFolder, '_nodelist.dat',sep = "")
#input$networkFile <- paste(userSelectedDataFolder, '/Data/', userSelectedDataFolder, '_subset.txt',sep = "")
#nodelistFile <- paste(basePath, '/',input$nodeListFile, sep = "")
#nodelist_name <- paste(data_name, '_nodelist.dat',sep = "")
#network_name <- paste(data_name, '_subset.txt',sep = "")
#nodelistFile <- system.file("extdata", nodelist_name, package = "epl") #full path of nodelist
#networkFile <- system.file("extdata", network_name, package = "epl") #full path of network
#put nodelistFile and networkFile as two parameters
nodelist <- convertNodeListFile(nodelistFile, input)
rvalues$netType <- length(unique(nodelist$entity$Entity))
if (rvalues$netType > 2) {stop("Data not supported. Make sure your input has only one or two entities.")}
#get data from file, edgelist.dat file is saved
#rvalues$dataFromFile <- getData(basePath, input, rvalues, nodelist$modules)
rvalues$dataFromFile <- getDataFromFile(networkFile, nodelist$modules, rvalues)
rvalues$entity <- nodelist$entity #as.matrix(entity)
#rvalues$colnames <- colnames(rvalues$dataFromFile$df)
rvalues$colnames <- nodelist$coords[,1]
rvalues$nrows <- nrow(rvalues$dataFromFile$df)
rvalues$outcome <- nodelist$outcomes
rvalues$coord <- getOriginalNetworkCoordinates(input, rvalues$dataFromFile$g, nodelist$coords)
res <- findClosestRadius(input, rvalues$dataFromFile, rvalues$coord)
radius <- res$radius
rvalues$originalScore <- res$originalScore
rvalues$ClosestRadius <- radius
GOEresult <- findRadiusWithMaxGOE(input, rvalues$dataFromFile, rvalues$coord)
maxRadius <- GOEresult[1]
rvalues$displace <- maxRadius
goodnessscore <- as.numeric(sprintf("%.4f",as.numeric(GOEresult[2])))
#calculate coordinates for networks using ExplodeLayout at different radius, then store them
#coordinates.dat and statList.dat files are saved
returnExplodeNetwork <- storeCoordinatesForExplodeLayout(rvalues$dataFromFile, input, rvalues$coord, rvalues)
explode.net <- getCoordinatesForExplodeLayout(rvalues$dataFromFile, input, rvalues$coord, rvalues)
if(rvalues$dataFromFile$nclust != 1){
explodeGoodness <- calculateScore(input, explode.net, rvalues$dataFromFile$nclust)
} else{
explodeGoodness <- "NA"
}
explode.net <- formatExplodeNetMatrix(explode.net, rvalues, input)
#cat(paste(input$displace, ";", explodeGoodness,"\n", sep=""))
######################################## generating graphs ###############################################
ranges <- list()
#input$visualDirectorypath <- rvalues$resultsFolder
#if (file.exists('coordinates.data')) {
#returnExplodeNetwork <- data.frame(data.table::fread('coordinates.dat', sep = "\t"))}
columnNames <- colnames(returnExplodeNetwork)
coordX <- columnNames[2]
coordY <- columnNames[3]
explodeNet <- cbind.data.frame(returnExplodeNetwork[,1], returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordX],
returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordY], returnExplodeNetwork[,4],
returnExplodeNetwork[,5], returnExplodeNetwork[,6])
colnames(explodeNet) <- c(columnNames[1], coordX, coordY, columnNames[4], columnNames[5], columnNames[6] )
returnList <- list("explode.net" = explodeNet)#, "edgeList" = edgeList)
newSliderValues <- returnList
explode.net <- newSliderValues$explode.net
ranges$x <- c(min(explode.net[,2]), max(explode.net[,2]))
ranges$y <- c(min(explode.net[,3]), max(explode.net[,3]))
labelsData <- getDataInBoundingBox(newSliderValues$explode.net , ranges, rvalues)
lab <- labelsData$colnames
if(rvalues$netType == 2) {
lab <- c(rep(NA, labelsData$nrows), labelsData$colnames)
}
p <- suppressWarnings(plotnetwork(input, newSliderValues$explode.net, rvalues, 1, labelsData))
plotcord <- as.data.frame(newSliderValues$explode.net[,2:3])
colnames(plotcord) = c("X1", "X2")
# generate and save the original graph (before applying the ExplodeLayout algorithm)
p <- p + ggplot2::coord_cartesian(xlim = ranges$x, ylim = ranges$y)
if(labelOn == 'yes' && ((input$labelSize != 0) && (!is.na(input$labelSize)))){
p <- p + suppressWarnings( #rvalues$colnames
ggrepel::geom_label_repel(ggplot2::aes(plotcord$X1, plotcord$X2,label=lab, alpha=0.5),
na.rm = TRUE,
size = input$labelSize,
fontface = "bold" ,
box.padding = ggplot2::unit(0.5, "lines"),
point.padding = ggplot2::unit(0.5, "lines"),
segment.color = "#0000FF"
# remove lollipop stick : segment.size = 0
)
)
}
rvalues$plot <- p
exportFile <- paste(data_name, '_original','.',input$selectExportFileType, sep="")
ggplot2::ggsave(exportFile, rvalues$plot, device = input$selectExportFileType, width = 15, height = 15, units = 'cm' )
# generate and save 20 graphs using ExplodeLayout with radius from 0.1 to 2.0, step size is 0.1
for (i in 1 : 20) {
coordX <- columnNames[2 * i + 5]
coordY <- columnNames[2 * i + 6]
explodeNet <- cbind.data.frame(returnExplodeNetwork[,1], returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordX],
returnExplodeNetwork[,colnames(returnExplodeNetwork) == coordY], returnExplodeNetwork[,4],
returnExplodeNetwork[,5], returnExplodeNetwork[,6])
colnames(explodeNet) <- c(columnNames[1], coordX, coordY, columnNames[4], columnNames[5], columnNames[6] )
returnList <- list("explode.net" = explodeNet)#, "edgeList" = edgeList)
newSliderValues <- returnList
explode.net <- newSliderValues$explode.net
#ranges$x <- c(min(explode.net[,2]), max(explode.net[,2]))
#ranges$y <- c(min(explode.net[,3]), max(explode.net[,3]))
p <- suppressWarnings(plotnetwork(input, newSliderValues$explode.net, rvalues, 1, labelsData))
#plotcord <- as.data.frame(matrix(as.numeric(sliderValues[,2:3]), nrow(sliderValues), 2, byrow=F))
plotcord <- as.data.frame(newSliderValues$explode.net[,2:3])
colnames(plotcord) = c("X1", "X2")
p <- p + ggplot2::coord_cartesian(xlim = ranges$x, ylim = ranges$y)
if(labelOn == 'yes' && ((input$labelSize != 0) && (!is.na(input$labelSize)))){
p <- p + suppressWarnings( #rvalues$colnames
ggrepel::geom_label_repel(ggplot2::aes(plotcord$X1, plotcord$X2,label=lab, alpha=0.5),
na.rm = TRUE,
size = input$labelSize,
fontface = "bold" ,
box.padding = ggplot2::unit(0.5, "lines"),
point.padding = ggplot2::unit(0.5, "lines"),
segment.color = "#0000FF"
# remove lollipop stick : segment.size = 0
)
)
}
rvalues$plot <- p
exportFile <- paste(data_name,'_radius',as.double(i/10),'.',input$selectExportFileType, sep="")
ggplot2::ggsave(exportFile, rvalues$plot, device = input$selectExportFileType, width = 15, height = 15, units = 'cm' )
}
}
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