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Network visualization and R - An example how to plot a network and customize its appearance in Cytoscape directly from R using the RCy3 package
cyPlot_mod <- function (node.df, edge.df)
{
edge.nodes <- unique(c(as.character(edge.df[, 1]), as.character(edge.df[,
2])))
mydata <- new("graphNEL", edgemode = "directed", nodes = unique(c(as.character(node.df[,
1]), edge.nodes)))
node.df[, 1] <- as.character(node.df[, 1])
edge.df[, 1:2] <- sapply(edge.df[, 1:2], as.character)
node.class <- sapply(node.df, class)
if (any(grep("factor", node.class))) {
node.df[, grep("factor", node.class)] <- sapply(node.df[,
grep("factor", node.class)], as.character)
}
if (any(grep("integer", node.class))) {
node.df[, grep("integer", node.class)] <- sapply(node.df[,
grep("integer", node.class)], as.numeric)
}
node.class <- sapply(node.df, class)
edge.class <- sapply(edge.df, class)
if (any(grep("factor", edge.class))) {
edge.df[, grep("factor", edge.class)] <- sapply(edge.df[,
grep("factor", edge.class)], as.character)
}
if (any(grep("integer", edge.class))) {
edge.df[, grep("integer", edge.class)] <- sapply(edge.df[,
grep("integer", edge.class)], as.numeric)
}
edge.class <- sapply(edge.df, class)
if (length(grep("character", node.class)) > 1)
for (i in 2:length(grep("character", node.class))) {
mydata <- RCy3::initNodeAttribute(graph = mydata, attribute.name = names(node.class[grep("character",
node.class)])[i], attribute.type = "char", default.value = "undefined")
graph::nodeData(mydata, n = as.character(node.df[, 1]), attr = names(node.class[grep("character",
node.class)])[i]) <- as.character(node.df[, grep("character",
node.class)[i]])
}
if (length(grep("numeric", node.class)) > 0)
for (i in 1:length(grep("numeric", node.class))) {
mydata <- RCy3::initNodeAttribute(graph = mydata, attribute.name = names(node.class[grep("numeric",
node.class)])[i], attribute.type = "numeric", default.value = 0)
graph::nodeData(mydata, n = as.character(node.df[, 1]), attr = names(node.class[grep("numeric",
node.class)])[i]) <- as.numeric(node.df[, grep("numeric",
node.class)[i]])
}
mydata = graph::addEdge(as.vector(edge.df[, 1], mode = "character"),
as.vector(edge.df[, 2], mode = "character"), mydata)
if (length(grep("character", edge.class)) > 2)
for (i in 3:length(grep("character", edge.class))) {
mydata <- RCy3::initEdgeAttribute(graph = mydata, attribute.name = names(edge.df[,
grep("character", edge.class)])[i], attribute.type = "char",
default.value = "undefined")
graph::edgeData(mydata, as.vector(edge.df[, 1], mode = "character"),
as.vector(edge.df[, 2], mode = "character"), attr = names(edge.df[,
grep("character", edge.class)])[i]) <- as.character(edge.df[,
grep("character", edge.class)[i]])
}
if (length(grep("numeric", edge.class)) > 0)
for (i in 1:length(grep("numeric", edge.class))) {
mydata <- RCy3::initEdgeAttribute(mydata, attribute.name = names(edge.class[grep("numeric",
edge.class)])[i], attribute.type = "numeric", default.value = 0)
graph::edgeData(mydata, as.vector(edge.df[, 1], mode = "character"),
as.vector(edge.df[, 2], mode = "character"), attr = names(edge.class[grep("numeric",
edge.class)])[i]) <- as.numeric(edge.df[, grep("numeric",
edge.class)[i]])
}
return(mydata)
}
Gillenormand JeanValjean 2
Zephine Listolier 3
Joly Feuilly 5
Brevet Judge 2
Bamatabois JeanValjean 2
Gavroche JeanValjean 1
MadameHucheloup Courfeyrac 1
Gavroche Javert 1
Count BishopCharles-Francois-BienvenuMyriel 2
Dahlia Listolier 3
Fantine JeanValjean 9
Marius Cosette 21
MadameHucheloup Joly 1
Blacheville Listolier 4
Scaufflaire JeanValjean 1
MadameHucheloup Gavroche 1
Montparnasse Claquesous 2
Fantine Zephine 4
MadameHucheloup Grantaire 1
Pontmercy Thenardier 1
Combeferre Marius 5
Bahorel Feuilly 3
Courfeyrac JeanProuvaire 3
Combeferre Mabeuf 2
Bahorel Mabeuf 2
Gueulemer JeanValjean 1
Brevet JeanValjean 2
Joly JeanProuvaire 2
Dahlia FelixTholomyes 3
OldMan BishopCharles-Francois-BienvenuMyriel 1
Brujon Babet 3
Toussaint Javert 1
Fantine Dahlia 4
Fameuil Listolier 4
Grantaire Combeferre 1
LAigleDeMeauxBossuet Marius 5
Child2 Gavroche 2
Mabeuf Marius 1
Gueulemer Thenardier 5
Courfeyrac Marius 9
SisterSimplice Fantine 2
Cosette FelixTholomyes 1
Judge JeanValjean 3
Gillenormand Cosette 3
Fantine Marguerite 2
MaubertIsabeau JeanValjean 1
Fantine Fameuil 3
Fauchelevent Javert 1
Mabeuf Gavroche 1
Gervais JeanValjean 1
MademoiselleGillenormand Cosette 2
LieutenantTheoduleGillenormand Gillenormand 1
Gavroche MadameBurgon 2
LAigleDeMeauxBossuet JeanValjean 1
Gueulemer Javert 1
Cochepaille Bamatabois 1
LieutenantTheoduleGillenormand Cosette 1
Javert JeanValjean 17
Marius JeanValjean 19
Babet JeanValjean 1
Grantaire Joly 2
Thenardier JeanValjean 12
Feuilly JeanProuvaire 2
Courfeyrac Feuilly 6
Brujon Eponine 1
Grantaire Enjolras 3
Cochepaille Champmathieu 2
Montparnasse Gavroche 1
Babet Thenardier 6
Javert Cosette 1
Joly Marius 2
SisterSimplice Javert 1
Chenildieu Bamatabois 1
Fantine Listolier 3
Zephine Dahlia 4
Joly Mabeuf 1
JeanProuvaire Enjolras 4
Grantaire Feuilly 1
Brujon Thenardier 3
Marius LieutenantTheoduleGillenormand 1
Claquesous MadameThenardier 1
Courfeyrac Eponine 1
Bahorel JeanProuvaire 2
Marius Gillenormand 12
Brujon Montparnasse 1
Toussaint Cosette 2
Eponine Thenardier 3
Enjolras Marius 7
Favourite Blacheville 4
Zephine Favourite 4
Brujon Gueulemer 3
MotherInnocent JeanValjean 1
Grantaire LAigleDeMeauxBossuet 3
LAigleDeMeauxBossuet Bahorel 4
Marius Pontmercy 1
Bahorel Enjolras 4
Grantaire JeanProuvaire 1
Dahlia Blacheville 3
Marius MademoiselleGillenormand 6
Bahorel Courfeyrac 6
Brujon Claquesous 1
BaronessDeThenard Marius 1
Bahorel Combeferre 5
MadameThenardier JeanValjean 7
Gueulemer MadameThenardier 1
Enjolras JeanValjean 4
Zephine Blacheville 3
Joly LAigleDeMeauxBossuet 7
Enjolras Javert 6
Claquesous JeanValjean 1
MotherPlutarch Mabeuf 3
LAigleDeMeauxBossuet Courfeyrac 12
Anzelma Thenardier 2
Javert Thenardier 5
Javert Fantine 5
LieutenantTheoduleGillenormand MademoiselleGillenormand 2
Claquesous Eponine 1
CountessDeLo BishopCharles-Francois-BienvenuMyriel 1
Napoleon BishopCharles-Francois-BienvenuMyriel 1
Listolier FelixTholomyes 4
JeanProuvaire Gavroche 1
Joly Gavroche 3
Courfeyrac Mabeuf 2
Dahlia Fameuil 3
Cochepaille Chenildieu 2
Eponine MadameThenardier 2
Favourite Fameuil 3
Babet Javert 2
BaronessDeThenard Gillenormand 1
Combeferre Enjolras 15
Gueulemer Gavroche 1
JeanValjean MadameMagloire 3
Cosette MadameThenardier 4
Marius FelixTholomyes 1
Feuilly Combeferre 5
Cosette Thenardier 1
Thenardier MadameThenardier 13
Child1 Gavroche 2
Cochepaille Judge 2
JeanProuvaire Combeferre 2
Grantaire Bahorel 1
Montparnasse Gueulemer 2
Marius Thenardier 2
Courfeyrac Combeferre 13
Enjolras Gavroche 7
Montparnasse Thenardier 1
Joly Bahorel 5
Woman2 Cosette 1
Cochepaille Brevet 2
JeanValjean BaptistineMyrie 3
MadamePontmercy MademoiselleGillenormand 1
Babet MadameThenardier 1
Woman2 JeanValjean 3
Montparnasse Javert 1
LAigleDeMeauxBossuet JeanProuvaire 2
Favourite Listolier 3
Combeferre Gavroche 6
Feuilly Mabeuf 1
Woman1 JeanValjean 2
Montparnasse Babet 2
Fameuil FelixTholomyes 4
Courfeyrac Enjolras 17
Marius Eponine 5
Zephine FelixTholomyes 3
Chenildieu Champmathieu 2
MadamePontmercy Pontmercy 1
MadameBurgon Jondrette 1
Feuilly Gavroche 2
Mabeuf Eponine 1
Thenardier Fantine 1
Monsieur Geborand BishopCharles-Francois-BienvenuMyriel 1
Champmathieu JeanValjean 3
Fantine Blacheville 3
Feuilly Enjolras 6
BaptistineMyrie BishopCharles-Francois-BienvenuMyriel 8
Champtercier BishopCharles-Francois-BienvenuMyriel 1
Bahorel Gavroche 5
Babet Gavroche 1
MadameHucheloup Enjolras 1
Enjolras Mabeuf 1
Montparnasse JeanValjean 1
Javert MadameThenardier 1
Claquesous Enjolras 1
Claquesous Thenardier 4
Claquesous Gueulemer 4
Toussaint JeanValjean 1
Bamatabois Javert 1
LAigleDeMeauxBossuet Mabeuf 1
Courfeyrac Gavroche 7
LAigleDeMeauxBossuet Enjolras 10
Joly Combeferre 5
Magnon MadameThenardier 1
Grantaire Courfeyrac 2
MotherInnocent Fauchelevent 3
Grantaire Gavroche 1
Brevet Champmathieu 2
Gribier Fauchelevent 2
Cosette JeanValjean 31
Anzelma Eponine 2
Brevet Bamatabois 1
Marius Gavroche 4
LAigleDeMeauxBossuet Feuilly 6
Feuilly Marius 1
MademoiselleGillenormand Gillenormand 9
Cravatte BishopCharles-Francois-BienvenuMyriel 1
Claquesous Javert 1
Child2 Child1 3
SisterPerpetue Fantine 1
Chenildieu JeanValjean 2
Chenildieu Brevet 2
Blacheville FelixTholomyes 4
Fauchelevent JeanValjean 8
Brujon Gavroche 1
Gueulemer Eponine 1
Boulatruelle Thenardier 1
Bamatabois Fantine 1
Zephine Fameuil 3
Claquesous Babet 4
Montparnasse Eponine 1
SisterSimplice JeanValjean 3
Champmathieu Judge 3
Favourite FelixTholomyes 3
MadameThenardier Fantine 2
JeanValjean BishopCharles-Francois-BienvenuMyriel 5
Magnon Gillenormand 1
Blacheville Fameuil 4
Gavroche Thenardier 1
Babet Eponine 1
Champmathieu Bamatabois 2
Fantine Favourite 4
Cochepaille JeanValjean 2
MadameMagloire BaptistineMyrie 6
Woman2 Javert 1
Dahlia Favourite 5
MadameMagloire BishopCharles-Francois-BienvenuMyriel 10
Fantine FelixTholomyes 3
Woman1 Javert 1
MadameHucheloup LAigleDeMeauxBossuet 1
MadameHucheloup Bahorel 1
LAigleDeMeauxBossuet Combeferre 9
Judge Bamatabois 2
Bahorel Marius 1
Joly Courfeyrac 5
Marguerite JeanValjean 1
JeanValjean JacquinLabarre 1
MadameDeR JeanValjean 1
SisterSimplice SisterPerpetue 2
LAigleDeMeauxBossuet Gavroche 5
Joly Enjolras 5
MademoiselleGillenormand JeanValjean 2
MademoiselleVaubois MademoiselleGillenormand 1
Anzelma MadameThenardier 1
Babet Gueulemer 6
Chenildieu Judge 2
############################################################################################
############################################################################################
# Plotting networks in R - an example how to plot a network and
# customize its appearance in Cytoscape directly from R using
# the RCy3 package
############################################################################################
############################################################################################
# Clear workspace
# rm(list = ls())
############################################################################################
# Read a data set.
# Data format: dataframe with 3 variables; variables 1 & 2 correspond to interactions; variable 3 is weight of interaction
dataSet <- read.table("lesmis.txt", header = FALSE, sep = "\t")
# Create a graph. Use simplyfy to ensure that there are no duplicated edges or self loops
gD <- igraph::simplify(igraph::graph.data.frame(dataSet, directed=FALSE))
# Print number of nodes and edges
# igraph::vcount(gD)
# igraph::ecount(gD)
############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities
# Calculate degree for all nodes
degAll <- 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))
rm(betAll)
#Calculate Dice similarities between all pairs of nodes
dsAll <- igraph::similarity.dice(gD, vids = igraph::V(gD), mode = "all")
############################################################################################
# Add new node and edge attributes based on the calculated node properties/similarities
gD <- igraph::set.vertex.attribute(gD, "degree", index = igraph::V(gD), value = degAll)
gD <- igraph::set.vertex.attribute(gD, "betweenness", index = igraph::V(gD), value = betAll.norm)
# Check the attributes
# summary(gD)
F1 <- function(x) {data.frame(V4 = dsAll[which(igraph::V(gD)$name == as.character(x$V1)), which(igraph::V(gD)$name == as.character(x$V2))])}
dataSet.ext <- plyr::ddply(dataSet, .variables=c("V1", "V2", "V3"), function(x) data.frame(F1(x)))
gD <- igraph::set.edge.attribute(gD, "weight", index = igraph::E(gD), value = 0)
gD <- igraph::set.edge.attribute(gD, "similarity", index = igraph::E(gD), value = 0)
# The order of interactions in dataSet.ext is not the same as it is in dataSet or as it is in the edge list
# and for that reason these values cannot be assigned directly
for (i in 1:nrow(dataSet.ext))
{
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$weight <- as.numeric(dataSet.ext$V3)
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$similarity <- as.numeric(dataSet.ext$V4)
}
# Check the attributes
# summary(gD)
rm(dataSet,dsAll, i, F1)
############################################################################################
# Now, let's do Cytoscape plots
# First, we need to transform our network from the igraph to graphnel format
gD.cyt <- igraph::as_graphnel(gD)
# Check if attributes have been passed
# graph::nodeData(gD.cyt, igraph::V(gD)$name, 'degree')
# graph::nodeData(gD.cyt, igraph::V(gD)$name, 'betweenness')
# graph::edgeData(gD.cyt, as.character(dataSet.ext$V1), as.character(dataSet.ext$V2), 'weight')
# graph::edgeData(gD.cyt, as.character(dataSet.ext$V1), as.character(dataSet.ext$V2), 'similarity')
# We have to create attributes for graphNEL
# We'll keep the same names as before
# In RCytoscape, this would ensure that the values of attributes are passed directly from igraph.
# However, this does not work with RCy3 right now (not sure if it is a bug or a feature has changed).
# Thus, we need to do send attributes to Cytoscape
gD.cyt <- RCy3::initNodeAttribute(gD.cyt, 'degree', 'numeric', 0)
gD.cyt <- RCy3::initNodeAttribute(gD.cyt, 'betweenness', 'numeric', 0)
gD.cyt <- RCy3::initEdgeAttribute (gD.cyt, "weight", 'integer', 0)
gD.cyt <- RCy3::initEdgeAttribute (gD.cyt, "similarity", 'numeric', 0)
# Next, we will create a new graph window in cytoscape
gDCW <- RCy3::CytoscapeWindow("Les Miserables", graph = gD.cyt, overwriteWindow = TRUE)
# We can display graph, with defaults color/size scheme
RCy3::displayGraph(gDCW)
# Now let's send/load node and edge attributes into Cytoscape
##########
# This should theoretically work, but there are some problems with attributes when networks
# are created from data frames (see https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape/issues/25)
# I'll keep this code uncommented, but right now, it doesn't do anything
# setNodeAttributes should transfer the specified node attributes, for all nodes, the named node attribute
# from the R graph (found in obj@graph) to Cytoscape.
attribute.names <- RCy3::noa.names(gDCW@graph)
# Print list of attribute names to see if they are ok
# attribute.names
# All nodes should already be in
RCy3::sendNodes(gDCW)
for (attribute.name in attribute.names){
RCy3::setNodeAttributes(gDCW, attribute.name)
}
attribute.names <- RCy3::eda.names(gDCW@graph)
# All edges should already be in
RCy3::sendEdges(gDCW)
for (attribute.name in attribute.names){
RCy3::setEdgeAttributes(gDCW, attribute.name)
}
RCy3::displayGraph(gDCW)
##########
# Thealternative, when we set attributes directly, works fine,
# so we will use it now (although, it seems kind of repetative)
RCy3::setNodeAttributesDirect(gDCW, 'degree', 'numeric', igraph::V(gD)$name, igraph::V(gD)$degree)
RCy3::setNodeAttributesDirect(gDCW, 'betweenness', 'numeric', igraph::V(gD)$name, igraph::V(gD)$betweenness)
RCy3::setEdgeAttributesDirect(gDCW, 'weight', 'integer', as.character (RCy3::cy2.edge.names (gDCW@graph)), graph::edgeData(gD.cyt, as.character(dataSet.ext$V1), as.character(dataSet.ext$V2), 'weight'))
RCy3::setEdgeAttributesDirect(gDCW, 'similarity', 'numeric', as.character (RCy3::cy2.edge.names (gDCW@graph)), graph::edgeData(gD.cyt, as.character(dataSet.ext$V1), as.character(dataSet.ext$V2), 'similarity'))
##########
# Now let's decide on a layout
# If you also want to choose a layout from R, a list of available layouts can be accessed as follow:
cy <- RCy3::CytoscapeConnection()
hlp <-RCy3::getLayoutNames(cy)
# We'll select the "fruchterman-rheingold" layout. This layout is the layout number 10
# To see properties for the given layout, use:
# RCy3::getLayoutPropertyNames(cy, hlp[10])
# We can choose any property we want and provide them as a list
RCy3::setLayoutProperties (gDCW, hlp[10], list (gravity_multiplier = 'similarity', nIterations = 1000))
RCy3::layoutNetwork(gDCW, hlp[10])
# I've noticed that if I change property to attraction_multiplier
# RCy3::setLayoutProperties (gDCW, hlp[10], list (attraction_multiplier = 'similarity', nIterations = 1000))
# RCy3::layoutNetwork(gDCW, hlp[10])
# And then go back to the original one
# RCy3::setLayoutProperties (gDCW, hlp[10], list (gravity_multiplier = 'similarity', nIterations = 1000))
# RCy3::layoutNetwork(gDCW, hlp[10])
# The layout won't go back to the original one. I am not sure if this is a bug or not
##########
# Finally, we can define rules for nodes:
RCy3::setNodeColorRule(gDCW, 'degree', c(min(degAll), mean(degAll), max(degAll)), c('#F5DEB3', '#FFA500', '#FF7F50', '#FF4500', '#FF0000'), mode = 'interpolate')
RCy3::setNodeSizeRule(gDCW, 'betweenness', c(min(betAll.norm), mean(betAll.norm), max(betAll.norm)), c(30, 45, 60, 80, 100), mode = 'interpolate')
# And edges:
RCy3::setEdgeLineWidthRule(gDCW, 'weight', dataSet.ext$V3, dataSet.ext$V3)
RCy3::setEdgeColorRule(gDCW, 'weight', c(min(as.numeric(dataSet.ext$V3)), mean(as.numeric(dataSet.ext$V3)), max(as.numeric(dataSet.ext$V3))), c('#FFFF00', '#00FFFF', '#00FF7F', '#228B22', '#006400'), mode='interpolate')
# While I get the "Successfully set rule" for both of the Edge rules, the view in the Cytoscape did not
# change accordning the rules - setEdgeLineWidthRule command did not make any changes and the
# setEdgeColorRule command made all edges white.
# One of the GitHub solved issues suggests to first set all rule-based functions and then the direct ones, but
# but it didn't work here (https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape/issues/21 and
# https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape/issues/20)
# We will define our own default color/size schema after we defined node and edge rules, due to
# possible issues when using rules
RCy3::setDefaultBackgroundColor(gDCW, '#D3D3D3')
RCy3::setDefaultEdgeColor(gDCW, '#CDC9C9')
RCy3::setDefaultEdgeLineWidth(gDCW, 4)
RCy3::setDefaultNodeBorderColor(gDCW, '#000000')
RCy3::setDefaultNodeBorderWidth(gDCW, 3)
RCy3::setDefaultNodeShape(gDCW, 'ellipse')
RCy3::setDefaultNodeColor(gDCW, '#87CEFA')
RCy3::setDefaultNodeSize(gDCW, 60)
RCy3::setDefaultNodeFontSize(gDCW, 20)
RCy3::setDefaultNodeLabelColor(gDCW, '#000000')
# Running these commands will set color to all edges back to black and set their width to 4,
# ignoring the rules specified above
############################################################################################
sessionInfo()
# R version 3.3.1 (2016-06-21)
# Platform: x86_64-redhat-linux-gnu (64-bit)
# Running under: Fedora 23 (Workstation Edition)
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods
# [7] base
#
# loaded via a namespace (and not attached):
# [1] httr_1.2.1 R6_2.1.2 plyr_1.8.4
# [4] magrittr_1.5 parallel_3.3.1 tools_3.3.1
# [7] igraph_1.0.1 RCurl_1.95-4.8 curl_1.1
# [10] Rcpp_0.12.6 RJSONIO_1.3-0 BiocGenerics_0.18.0
# [13] RCy3_1.2.0 bitops_1.0-6 stats4_3.3.1
# [16] graph_1.50.0
#
#####
# Cytoscape version: 3.4.0
# Java version: 1.8.0_101
# cyREST version: 3.3.4
############################################################################################
############################################################################################
############################################################################################
# Plotting networks in R - an example how to plot a network and
# customize its appearance in Cytoscape directly from R using
# the RCy3 package
############################################################################################
############################################################################################
# Clear workspace
# rm(list = ls())
############################################################################################
# Read a data set.
# Data format: dataframe with 3 variables; variables 1 & 2 correspond to interactions; variable 3 is weight of interaction
dataSet <- read.table("lesmis.txt", header = FALSE, sep = "\t")
# Create a graph. Use simplyfy to ensure that there are no duplicated edges or self loops
gD <- igraph::simplify(igraph::graph.data.frame(dataSet, directed=FALSE))
# Print number of nodes and edges
# igraph::vcount(gD)
# igraph::ecount(gD)
############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities
# Calculate degree for all nodes
degAll <- 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))
rm(betAll)
#Calculate Dice similarities between all pairs of nodes
dsAll <- igraph::similarity.dice(gD, vids = igraph::V(gD), mode = "all")
############################################################################################
# Add new node and edge attributes based on the calculated node properties/similarities
gD <- igraph::set.vertex.attribute(gD, "degree", index = igraph::V(gD), value = degAll)
gD <- igraph::set.vertex.attribute(gD, "betweenness", index = igraph::V(gD), value = betAll.norm)
# Check the attributes
# summary(gD)
F1 <- function(x) {data.frame(V4 = dsAll[which(igraph::V(gD)$name == as.character(x$V1)), which(igraph::V(gD)$name == as.character(x$V2))])}
dataSet.ext <- plyr::ddply(dataSet, .variables=c("V1", "V2", "V3"), function(x) data.frame(F1(x)))
gD <- igraph::set.edge.attribute(gD, "weight", index = igraph::E(gD), value = 0)
gD <- igraph::set.edge.attribute(gD, "similarity", index = igraph::E(gD), value = 0)
# The order of interactions in dataSet.ext is not the same as it is in dataSet or as it is in the edge list
# and for that reason these values cannot be assigned directly
for (i in 1:nrow(dataSet.ext))
{
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$weight <- as.numeric(dataSet.ext$V3)
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$similarity <- as.numeric(dataSet.ext$V4)
}
# Check the attributes
# summary(gD)
rm(dataSet,dsAll, i, F1)
############################################################################################
# Now, let's do Cytoscape plots
# Here, we will use the cyPlot function to create a graphNEL object with nodes, edges, and their
# attributes from the corresponding node and edge data frames.
node.tbl <- data.frame(person = igraph::V(gD)$name, degree = igraph::V(gD)$degree, betweenness = igraph::V(gD)$betweenness)
edge.tbl <- dataSet.ext
colnames(edge.tbl) <- c("person_1", "person_2", "weight", "similarity")
# gD.cyt <- RCy3::cyPlot(node.tbl, edge.tbl)
# There were some issues with the original cyPlot function - it was assumed that there will be more than one
# data frame column of the character type or at least one numeric type column,
# and the lack of integer to numeric type conversion for edges, so I made changes and called a new function
# cyPlot_mod function
gD.cyt <- cyPlot_mod(node.tbl, edge.tbl)
# Next, we will create a new graph window in cytoscape
gDCW <- RCy3::CytoscapeWindow("Les Miserables using cyPlot", graph = gD.cyt, overwriteWindow = TRUE)
# We can display graph, with defaults color/size scheme
RCy3::displayGraph(gDCW)
# Now let's send/load node and edge attributes into Cytoscape
# Using the cyPlot function, all attributes are loaded into Cytoscape directly and we don't
# have to use additional functions to initialize or set node attributes
##########
# Now let's decide on a layout
# If you also want to choose a layout from R, a list of available layouts can be accessed as follow:
cy <- RCy3::CytoscapeConnection()
hlp <-RCy3::getLayoutNames(cy)
# We'll select the "fruchterman-rheingold" layout. This layout is the layout number 10
# To see properties for the given layout, use:
# RCy3::getLayoutPropertyNames(cy, hlp[10])
# We can choose any property we want and provide them as a list
RCy3::setLayoutProperties (gDCW, hlp[10], list (gravity_multiplier = 'similarity', nIterations = 1000))
RCy3::layoutNetwork(gDCW, hlp[10])
# RCy3::setLayoutProperties (gDCW, hlp[10], list (attraction_multiplier = 'similarity', nIterations = 1000))
# RCy3::layoutNetwork(gDCW, hlp[10])
# The layout looks different than before and does not change significalty based on whether we select gravity
# or attraction multiplier as its property
##########
# Finally, we can define rules for nodes:
RCy3::setNodeColorRule(gDCW, 'degree', c(min(degAll), mean(degAll), max(degAll)), c('#F5DEB3', '#FFA500', '#FF7F50', '#FF4500', '#FF0000'), mode = 'interpolate')
RCy3::setNodeSizeRule(gDCW, 'betweenness', c(min(betAll.norm), mean(betAll.norm), max(betAll.norm)), c(30, 45, 60, 80, 100), mode = 'interpolate')
# And edges:
RCy3::setEdgeLineWidthRule(gDCW, 'weight', dataSet.ext$V3, dataSet.ext$V3)
RCy3::setEdgeColorRule(gDCW, 'weight', c(min(as.numeric(dataSet.ext$V3)), mean(as.numeric(dataSet.ext$V3)), max(as.numeric(dataSet.ext$V3))), c('#FFFF00', '#00FFFF', '#00FF7F', '#228B22', '#006400'), mode='interpolate')
# While I get the "Successfully set rule" for both of the Edge rules, the view in the Cytoscape did not
# change accordning the rules - setEdgeLineWidthRule command did not make any changes and the
# setEdgeColorRule command made all edges white.
# Thus, this approach does not solve this issue
# We will define our own default color/size schema after we defined node and edge rules, due to
# possible issues when using rules
RCy3::setDefaultBackgroundColor(gDCW, '#D3D3D3')
RCy3::setDefaultEdgeColor(gDCW, '#CDC9C9')
RCy3::setDefaultEdgeLineWidth(gDCW, 4)
RCy3::setDefaultNodeBorderColor(gDCW, '#000000')
RCy3::setDefaultNodeBorderWidth(gDCW, 3)
RCy3::setDefaultNodeShape(gDCW, 'ellipse')
RCy3::setDefaultNodeColor(gDCW, '#87CEFA')
RCy3::setDefaultNodeSize(gDCW, 60)
RCy3::setDefaultNodeFontSize(gDCW, 20)
RCy3::setDefaultNodeLabelColor(gDCW, '#000000')
# Running these commands will set color to all edges back to black and set their width to 4,
# ignoring the rules specified above
############################################################################################
sessionInfo()
# R version 3.3.1 (2016-06-21)
# Platform: x86_64-redhat-linux-gnu (64-bit)
# Running under: Fedora 23 (Workstation Edition)
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods
# [7] base
#
# loaded via a namespace (and not attached):
# [1] httr_1.2.1 R6_2.1.2 plyr_1.8.4
# [4] magrittr_1.5 parallel_3.3.1 tools_3.3.1
# [7] igraph_1.0.1 RCurl_1.95-4.8 curl_1.1
# [10] Rcpp_0.12.6 RJSONIO_1.3-0 BiocGenerics_0.18.0
# [13] RCy3_1.2.0 bitops_1.0-6 stats4_3.3.1
# [16] graph_1.50.0
#
#####
# Cytoscape version: 3.4.0
# Java version: 1.8.0_101
# cyREST version: 3.3.4
############################################################################################
############################################################################################
############################################################################################
# Plotting networks in R - an example how to plot a network and
# customize its appearance in Cytoscape directly from R using
# the RCy3 package
############################################################################################
############################################################################################
# Clear workspace
# rm(list = ls())
############################################################################################
# Read a data set.
# Data format: dataframe with 3 variables; variables 1 & 2 correspond to interactions; variable 3 is weight of interaction
dataSet <- read.table("lesmis.txt", header = FALSE, sep = "\t")
# Create a graph. Use simplyfy to ensure that there are no duplicated edges or self loops
gD <- igraph::simplify(igraph::graph.data.frame(dataSet, directed=FALSE))
# Print number of nodes and edges
# igraph::vcount(gD)
# igraph::ecount(gD)
############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities
# Calculate degree for all nodes
degAll <- 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))
rm(betAll)
#Calculate Dice similarities between all pairs of nodes
dsAll <- igraph::similarity.dice(gD, vids = igraph::V(gD), mode = "all")
############################################################################################
# Add new node and edge attributes based on the calculated node properties/similarities
gD <- igraph::set.vertex.attribute(gD, "degree", index = igraph::V(gD), value = degAll)
gD <- igraph::set.vertex.attribute(gD, "betweenness", index = igraph::V(gD), value = betAll.norm)
# Check the attributes
# summary(gD)
F1 <- function(x) {data.frame(V4 = dsAll[which(igraph::V(gD)$name == as.character(x$V1)), which(igraph::V(gD)$name == as.character(x$V2))])}
dataSet.ext <- plyr::ddply(dataSet, .variables=c("V1", "V2", "V3"), function(x) data.frame(F1(x)))
gD <- igraph::set.edge.attribute(gD, "weight", index = igraph::E(gD), value = 0)
gD <- igraph::set.edge.attribute(gD, "similarity", index = igraph::E(gD), value = 0)
# The order of interactions in dataSet.ext is not the same as it is in dataSet or as it is in the edge list
# and for that reason these values cannot be assigned directly
for (i in 1:nrow(dataSet.ext))
{
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$weight <- as.numeric(dataSet.ext$V3)
igraph::E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$similarity <- as.numeric(dataSet.ext$V4)
}
# Check the attributes
# summary(gD)
rm(dataSet,dsAll, i, F1)
############################################################################################
# Now, let's do Cytoscape plots
# Here, we will use the cyPlot function to create a graphNEL object with nodes, edges, and their
# attributes from the corresponding node and edge data frames.
devtools::install_github("tmuetze/Bioconductor_RCy3_the_new_RCytoscape")
library(RCy3)
node.tbl <- data.frame(person = igraph::V(gD)$name, degree = igraph::V(gD)$degree, betweenness = igraph::V(gD)$betweenness)
edge.tbl <- dataSet.ext
colnames(edge.tbl) <- c("person_1", "person_2", "weight", "similarity")
# gD.cyt <- cyPlot(node.tbl, edge.tbl)
# Some of the issues with the original cyPlot function are fixed, but it still lacks
# the integer to numeric type conversion for edges. So we will call the cyPlot_mod function again
gD.cyt <- cyPlot_mod(node.tbl, edge.tbl)
# Next, we will create a new graph window in cytoscape
gDCW <- CytoscapeWindow("Les Miserables using cyPlot from GitHub", graph = gD.cyt, overwriteWindow = TRUE)
# We can display graph, with defaults color/size scheme
displayGraph(gDCW)
# Now let's send/load node and edge attributes into Cytoscape
# Using the cyPlot function, all attributes are loaded into Cytoscape directly and we don't
# have to use additional functions to initialize or set node attributes
##########
# Now let's decide on a layout
# If you also want to choose a layout from R, a list of available layouts can be accessed as follow:
cy <- CytoscapeConnection()
hlp <-getLayoutNames(cy)
# We'll select the "fruchterman-rheingold" layout. This layout is the layout number 10
# To see properties for the given layout, use:
# getLayoutPropertyNames(cy, hlp[10])
# We can choose any property we want and provide them as a list
setLayoutProperties (gDCW, hlp[10], list (gravity_multiplier = 'similarity', nIterations = 1000))
layoutNetwork(gDCW, hlp[10])
# setLayoutProperties (gDCW, hlp[10], list (attraction_multiplier = 'similarity', nIterations = 1000))
# layoutNetwork(gDCW, hlp[10])
# The layout looks different than before and does not change significalty based on whether we select gravity
# or attraction multiplier as its property
##########
# Finally, we can define rules for nodes:
setNodeColorRule(gDCW, 'degree', c(min(degAll), mean(degAll), max(degAll)), c('#F5DEB3', '#FFA500', '#FF7F50', '#FF4500', '#FF0000'), mode = 'interpolate')
setNodeSizeRule(gDCW, 'betweenness', c(min(betAll.norm), mean(betAll.norm), max(betAll.norm)), c(30, 45, 60, 80, 100), mode = 'interpolate')
# And edges:
setEdgeLineWidthRule(gDCW, 'weight', dataSet.ext$V3, dataSet.ext$V3)
setEdgeColorRule(gDCW, 'weight', c(min(as.numeric(dataSet.ext$V3)), mean(as.numeric(dataSet.ext$V3)), max(as.numeric(dataSet.ext$V3))), c('#FFFF00', '#00FFFF', '#00FF7F', '#228B22', '#006400'), mode='interpolate')
# While I get the "Successfully set rule" for both of the Edge rules, the view in the Cytoscape did not
# change accordning the rules - setEdgeLineWidthRule command did not make any changes and the
# setEdgeColorRule command made all edges white.
# Thus, this approach does not solve this issue
# We will define our own default color/size schema after we defined node and edge rules, due to
# possible issues when using rules
setDefaultBackgroundColor(gDCW, '#D3D3D3')
setDefaultEdgeColor(gDCW, '#CDC9C9')
setDefaultEdgeLineWidth(gDCW, 4)
setDefaultNodeBorderColor(gDCW, '#000000')
setDefaultNodeBorderWidth(gDCW, 3)
setDefaultNodeShape(gDCW, 'ellipse')
setDefaultNodeColor(gDCW, '#87CEFA')
setDefaultNodeSize(gDCW, 60)
setDefaultNodeFontSize(gDCW, 20)
setDefaultNodeLabelColor(gDCW, '#000000')
# Running these commands will set color to all edges back to black and set their width to 4,
# ignoring the rules specified above
############################################################################################
sessionInfo()
# R version 3.3.1 (2016-06-21)
# Platform: x86_64-redhat-linux-gnu (64-bit)
# Running under: Fedora 23 (Workstation Edition)
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
# [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
# [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] parallel stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] RCy3_1.2.1 graph_1.50.0 BiocGenerics_0.18.0
#
# loaded via a namespace (and not attached):
# [1] Rcpp_0.12.6 withr_1.0.2 digest_0.6.10 bitops_1.0-6 plyr_1.8.4
# [6] R6_2.1.2 git2r_0.15.0 stats4_3.3.1 magrittr_1.5 BiocInstaller_1.22.3
# [11] httr_1.2.1 curl_1.1 devtools_1.12.0 RJSONIO_1.3-0 tools_3.3.1
# [16] RCurl_1.95-4.8 igraph_1.0.1 memoise_1.0.0
#
#####
# Cytoscape version: 3.4.0
# Java version: 1.8.0_101
# cyREST version: 3.3.4
############################################################################################
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