R/node role.R
In jmruzek17/Syrinx: Various Network Functions
Defines functions
node.role
Documented in
node.role
#' Title
#'
#' @param Thresh.Adj.Mat A thresholded square adjecency matrix
#' @param threshold the threshold that thresholded the adjecency matrix
#' @param nnodes.mod number of nodes in the smallest possible module, default is 5
#'
#' @return the node roles based on the modified module size, and a the network with the outmodule vertices removed
#' @export
#'
#'
node.role <- function(Thresh.Adj.Mat, threshold, nnodes.mod = 5)
{
#revised nole role labelling based on z-score and
#participation coefficient
require(igraph)
require(brainGraph)
require(WGCNA)
#Read in a thresholded, no singleton square correlation matrix
threshmat <- Thresh.Adj.Mat
rownames(threshmat) <- colnames(threshmat)
oli.Threshold <- threshold
#Build a weighted graph and an unweighted graph
weight.g <- graph_from_adjacency_matrix(as.matrix(threshmat),mode="lower",weighted=TRUE,diag=FALSE)
unweight.adjmat <- signumAdjacencyFunction(threshmat,oli.Threshold)
unweight.g <- graph_from_adjacency_matrix(as.matrix(unweight.adjmat),mode="lower",diag=FALSE)
#Apply clustering algorithm on the weighted graph. Should give same results as unweighted graph
oliclus <- cluster_fast_greedy(weight.g,weights = abs(E(weight.g)$weight))
oliclus.membs <- as.factor(oliclus$membership) #make the group numbers factors
oli.sim.mod <- modularity(oliclus) #retain modularity
V(weight.g)$module <- oliclus.membs #set the module ids for each vertex
#Now we need to separate out the network so that labels are correctly
#applied to species that are members of modules of nnodes.mod or more species.
species <- as.data.frame(matrix(nrow=nrow(unweight.adjmat),ncol=2))
species[,1] <- colnames(unweight.adjmat)
species[,2] <- oliclus.membs
species.table <- table(species$V2)
species.goodmods <- as.numeric(names(species.table[which(species.table >= nnodes.mod)]))
species.badmods <- as.numeric(names(species.table[which(species.table < nnodes.mod)]))
species.goodmods <- species[which(species$V2 %in% species.goodmods),]
species.badmods2 <- species[which(species$V2 %in% species.badmods),]
smaller.network <- weight.g - vertices(species.badmods2$V1)
#Now we calculate the within module z-scores and participation coefficients
#at the network level first, because the P coefficient needs information
#about the whole network, including modules with < 5 species. This code is divided
#into two sections. First, we need to define within and out of module connections
#and then count, for each species, how many inbound/out of module connections they have
#Second, we then need to calculate for each species their Z and pcoef scores and then
#assign the network roles for that species.
#Build the species matrix and calculate in/out of module degree/strength
#Edge type designation accounting for module connection types#
edge.df <- data.frame(as_edgelist(weight.g,names=TRUE))
edge.df[,1] <- as.character(edge.df[,1])
edge.df[,2] <- as.character(edge.df[,2])
edge.df2 <- data.frame(edge.df[,1],seq(1,nrow(edge.df),1))
colnames(edge.df2) <- c("V1","V2")
edge.df3 <- data.frame(cbind(V(weight.g)$name,V(weight.g)$module))
colnames(edge.df3) <- c("V1","V2")
edge.df4 <- merge(edge.df2,edge.df3,by="V1")
edge.df4 <- edge.df4[order(edge.df4$V2.x),]
edge.df5 <- data.frame(edge.df[,2],seq(1,nrow(edge.df),1))
colnames(edge.df5) <- c("V1","V2")
edge.df6 <- data.frame(cbind(V(weight.g)$name,V(weight.g)$module))
colnames(edge.df6) <- c("V1","V2")
edge.df7 <- merge(edge.df5,edge.df6,by="V1")
edge.df7 <- edge.df7[order(edge.df7$V2.x),]
edge.dflist <- data.frame(cbind(edge.df4,edge.df7))
colnames(edge.dflist) <- c("spp1","index","module1","spp2","index","module2")
edge.dflist$modcontype <- ifelse(edge.dflist$module1==edge.dflist$module2,"InModule","OutModule")
E(weight.g)$connectiontype <- edge.dflist$modcontype
#Now build the species matrix
spp.mat <- igraph::degree(weight.g)
spp.strmat <- igraph::strength(weight.g)
mod.conmat <- as.data.frame(matrix(nrow=length(spp.mat),ncol=7))
for(p in 1:nrow(mod.conmat)){
mod.conmat[p,1] <- names(spp.mat)[p]
mod.conmat[p,2] <- as.numeric(igraph::degree(weight.g)[p])
Snoop <- paste0(mod.conmat[p,1])
iso.sps1 <- edge.dflist[(edge.dflist$spp1 == Snoop),]
iso.sps2 <- edge.dflist[(edge.dflist$spp2 == Snoop),]
iso.sps <- rbind(iso.sps1, iso.sps2)
mod.conmat[p,3] <- nrow(iso.sps[which(iso.sps$modcontype == "InModule"),])
mod.conmat[p,4] <- nrow(iso.sps[which(iso.sps$modcontype == "OutModule"),])
#
#
spp.edge <- table(E(weight.g)$connectiontype)
}
# for(p in 1:nrow(mod.conmat)){
# mod.conmat[p,1] <- names(spp.mat)[p]
# mod.conmat[p,2] <- as.numeric(igraph::degree(weight.g)[p])
# spp.edge <-table(E(weight.g)$connectiontype)#
#
# if(length(spp.edge)==1){
# if(names(spp.edge)=="InModule"){
# mod.conmat[p,3] <- spp.edge
# }else {
# mod.conmat[p,4] <- spp.edge}}
# else{
# for(pp in 1:length(spp.edge)){
# if(names(spp.edge[pp])=="InModule"){
# mod.conmat[p,3] <- spp.edge[pp]}else {mod.conmat[p,4] <- spp.edge[pp]
# }
# }
# }
# }
for(p in 1:nrow(mod.conmat)){
mod.conmat[p,5] <- as.numeric(igraph::strength(weight.g))[p]
spp.edge <- table(E(weight.g)$connectiontype)
if(length(spp.edge)==1){
if(names(spp.edge)=="InModule"){
mod.conmat[p,6] <- sum(E(weight.g)[from(p)]$weight)
}else {mod.conmat[p,7] <- sum(E(weight.g)[from(p)]$weight)}}
else{
for(pp in 1:length(spp.edge)){
if(names(spp.edge[pp])=="InModule"){
df <- data.frame(E(weight.g)[from(p)]$connectiontype,E(weight.g)[from(p)]$weight)
mod.conmat[p,6] <- sum(df[which(df[,1]=="InModule"),2])
}
else {
df <-data.frame(E(weight.g)[from(p)]$connectiontype,E(weight.g)[from(p)]$weight)
mod.conmat[p,7] <- sum(df[which(df[,1]=="OutModule"),2])
}
}
}
}
mod.conmat[is.na(mod.conmat)] <- 0
netintramod.deg <- mod.conmat
netintramod.deg$module <- oliclus.membs
#calculation of node within module z-scores to define hub vs. nonhub
netintramod.deg$z.score <- within_module_deg_z_score(unweight.g, as.numeric(oliclus.membs))
#calculation of node degree participation coefficient
netintramod.deg$pcoef <- part_coeff(unweight.g, as.numeric(oliclus.membs))
#separate the species based on valid module size
species.goodmods <- species[which(species$V2 %in% species.goodmods$V2),]
species.badmods2 <- species[which(species$V2 %in% species.badmods),]
goodintramod.deg <- netintramod.deg[netintramod.deg[,1] %in% species.goodmods$V1,]
badintramod.deg <- netintramod.deg[netintramod.deg[,1] %in% species.badmods2$V1,]
#labels for species
goodintramod.deg$role <-ifelse(goodintramod.deg$z.score > 2.5 & goodintramod.deg$pcoef > 0.62,"NetHub",
ifelse(goodintramod.deg$z.score > 2.5 & goodintramod.deg$pcoef < 0.62, "ModHub",
ifelse(goodintramod.deg$z.score < 2.5 & goodintramod.deg$pcoef < 0.62, "Peripheral",
ifelse(goodintramod.deg$z.score < 2.5 & goodintramod.deg$pcoef > 0.62,"Connector","Nominal")
)
)
)
#Now we repeat, but for species who fall into modules of < 5 species, we need to
#correctly label them at the network level, because it is possible for
#these small species modules to be connected with other modules, thus
#increasing their participation coefficient and making them possibly
#connectors.
badintramod.deg$role <- ifelse(badintramod.deg$z.score < 2.5 & badintramod.deg$pcoef < 0.62,
"Peripheral","Connector")
#now we combine the data, label the columns, and reorder it so that the
#species are in the same order as the original network matrix
goodbadintramod.mat <- rbind(goodintramod.deg,badintramod.deg)
colnames(goodbadintramod.mat) <- c("spp", "kTotal","kIn","kOut","Strength",
"StrengthIn","StrengthOut","module","Z.score","pcoef",
"Role")
netintramod.deg2 <- goodbadintramod.mat[order(match(rownames(goodbadintramod.mat),
rownames(netintramod.deg))), ]
return(netintramod.deg2) #This should have the roles of the species now accurately defined
}
jmruzek17/Syrinx documentation built on Nov. 4, 2019, 3:02 p.m.
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Defines functions node.role
Documented in node.role
#' Title
#'
#' @param Thresh.Adj.Mat A thresholded square adjecency matrix
#' @param threshold the threshold that thresholded the adjecency matrix
#' @param nnodes.mod number of nodes in the smallest possible module, default is 5
#'
#' @return the node roles based on the modified module size, and a the network with the outmodule vertices removed
#' @export
#'
#'
node.role <- function(Thresh.Adj.Mat, threshold, nnodes.mod = 5)
{
#revised nole role labelling based on z-score and
#participation coefficient
require(igraph)
require(brainGraph)
require(WGCNA)
#Read in a thresholded, no singleton square correlation matrix
threshmat <- Thresh.Adj.Mat
rownames(threshmat) <- colnames(threshmat)
oli.Threshold <- threshold
#Build a weighted graph and an unweighted graph
weight.g <- graph_from_adjacency_matrix(as.matrix(threshmat),mode="lower",weighted=TRUE,diag=FALSE)
unweight.adjmat <- signumAdjacencyFunction(threshmat,oli.Threshold)
unweight.g <- graph_from_adjacency_matrix(as.matrix(unweight.adjmat),mode="lower",diag=FALSE)
#Apply clustering algorithm on the weighted graph. Should give same results as unweighted graph
oliclus <- cluster_fast_greedy(weight.g,weights = abs(E(weight.g)$weight))
oliclus.membs <- as.factor(oliclus$membership) #make the group numbers factors
oli.sim.mod <- modularity(oliclus) #retain modularity
V(weight.g)$module <- oliclus.membs #set the module ids for each vertex
#Now we need to separate out the network so that labels are correctly
#applied to species that are members of modules of nnodes.mod or more species.
species <- as.data.frame(matrix(nrow=nrow(unweight.adjmat),ncol=2))
species[,1] <- colnames(unweight.adjmat)
species[,2] <- oliclus.membs
species.table <- table(species$V2)
species.goodmods <- as.numeric(names(species.table[which(species.table >= nnodes.mod)]))
species.badmods <- as.numeric(names(species.table[which(species.table < nnodes.mod)]))
species.goodmods <- species[which(species$V2 %in% species.goodmods),]
species.badmods2 <- species[which(species$V2 %in% species.badmods),]
smaller.network <- weight.g - vertices(species.badmods2$V1)
#Now we calculate the within module z-scores and participation coefficients
#at the network level first, because the P coefficient needs information
#about the whole network, including modules with < 5 species. This code is divided
#into two sections. First, we need to define within and out of module connections
#and then count, for each species, how many inbound/out of module connections they have
#Second, we then need to calculate for each species their Z and pcoef scores and then
#assign the network roles for that species.
#Build the species matrix and calculate in/out of module degree/strength
#Edge type designation accounting for module connection types#
edge.df <- data.frame(as_edgelist(weight.g,names=TRUE))
edge.df[,1] <- as.character(edge.df[,1])
edge.df[,2] <- as.character(edge.df[,2])
edge.df2 <- data.frame(edge.df[,1],seq(1,nrow(edge.df),1))
colnames(edge.df2) <- c("V1","V2")
edge.df3 <- data.frame(cbind(V(weight.g)$name,V(weight.g)$module))
colnames(edge.df3) <- c("V1","V2")
edge.df4 <- merge(edge.df2,edge.df3,by="V1")
edge.df4 <- edge.df4[order(edge.df4$V2.x),]
edge.df5 <- data.frame(edge.df[,2],seq(1,nrow(edge.df),1))
colnames(edge.df5) <- c("V1","V2")
edge.df6 <- data.frame(cbind(V(weight.g)$name,V(weight.g)$module))
colnames(edge.df6) <- c("V1","V2")
edge.df7 <- merge(edge.df5,edge.df6,by="V1")
edge.df7 <- edge.df7[order(edge.df7$V2.x),]
edge.dflist <- data.frame(cbind(edge.df4,edge.df7))
colnames(edge.dflist) <- c("spp1","index","module1","spp2","index","module2")
edge.dflist$modcontype <- ifelse(edge.dflist$module1==edge.dflist$module2,"InModule","OutModule")
E(weight.g)$connectiontype <- edge.dflist$modcontype
#Now build the species matrix
spp.mat <- igraph::degree(weight.g)
spp.strmat <- igraph::strength(weight.g)
mod.conmat <- as.data.frame(matrix(nrow=length(spp.mat),ncol=7))
for(p in 1:nrow(mod.conmat)){
mod.conmat[p,1] <- names(spp.mat)[p]
mod.conmat[p,2] <- as.numeric(igraph::degree(weight.g)[p])
Snoop <- paste0(mod.conmat[p,1])
iso.sps1 <- edge.dflist[(edge.dflist$spp1 == Snoop),]
iso.sps2 <- edge.dflist[(edge.dflist$spp2 == Snoop),]
iso.sps <- rbind(iso.sps1, iso.sps2)
mod.conmat[p,3] <- nrow(iso.sps[which(iso.sps$modcontype == "InModule"),])
mod.conmat[p,4] <- nrow(iso.sps[which(iso.sps$modcontype == "OutModule"),])
#
#
spp.edge <- table(E(weight.g)$connectiontype)
}
# for(p in 1:nrow(mod.conmat)){
# mod.conmat[p,1] <- names(spp.mat)[p]
# mod.conmat[p,2] <- as.numeric(igraph::degree(weight.g)[p])
# spp.edge <-table(E(weight.g)$connectiontype)#
#
# if(length(spp.edge)==1){
# if(names(spp.edge)=="InModule"){
# mod.conmat[p,3] <- spp.edge
# }else {
# mod.conmat[p,4] <- spp.edge}}
# else{
# for(pp in 1:length(spp.edge)){
# if(names(spp.edge[pp])=="InModule"){
# mod.conmat[p,3] <- spp.edge[pp]}else {mod.conmat[p,4] <- spp.edge[pp]
# }
# }
# }
# }
for(p in 1:nrow(mod.conmat)){
mod.conmat[p,5] <- as.numeric(igraph::strength(weight.g))[p]
spp.edge <- table(E(weight.g)$connectiontype)
if(length(spp.edge)==1){
if(names(spp.edge)=="InModule"){
mod.conmat[p,6] <- sum(E(weight.g)[from(p)]$weight)
}else {mod.conmat[p,7] <- sum(E(weight.g)[from(p)]$weight)}}
else{
for(pp in 1:length(spp.edge)){
if(names(spp.edge[pp])=="InModule"){
df <- data.frame(E(weight.g)[from(p)]$connectiontype,E(weight.g)[from(p)]$weight)
mod.conmat[p,6] <- sum(df[which(df[,1]=="InModule"),2])
}
else {
df <-data.frame(E(weight.g)[from(p)]$connectiontype,E(weight.g)[from(p)]$weight)
mod.conmat[p,7] <- sum(df[which(df[,1]=="OutModule"),2])
}
}
}
}
mod.conmat[is.na(mod.conmat)] <- 0
netintramod.deg <- mod.conmat
netintramod.deg$module <- oliclus.membs
#calculation of node within module z-scores to define hub vs. nonhub
netintramod.deg$z.score <- within_module_deg_z_score(unweight.g, as.numeric(oliclus.membs))
#calculation of node degree participation coefficient
netintramod.deg$pcoef <- part_coeff(unweight.g, as.numeric(oliclus.membs))
#separate the species based on valid module size
species.goodmods <- species[which(species$V2 %in% species.goodmods$V2),]
species.badmods2 <- species[which(species$V2 %in% species.badmods),]
goodintramod.deg <- netintramod.deg[netintramod.deg[,1] %in% species.goodmods$V1,]
badintramod.deg <- netintramod.deg[netintramod.deg[,1] %in% species.badmods2$V1,]
#labels for species
goodintramod.deg$role <-ifelse(goodintramod.deg$z.score > 2.5 & goodintramod.deg$pcoef > 0.62,"NetHub",
ifelse(goodintramod.deg$z.score > 2.5 & goodintramod.deg$pcoef < 0.62, "ModHub",
ifelse(goodintramod.deg$z.score < 2.5 & goodintramod.deg$pcoef < 0.62, "Peripheral",
ifelse(goodintramod.deg$z.score < 2.5 & goodintramod.deg$pcoef > 0.62,"Connector","Nominal")
)
)
)
#Now we repeat, but for species who fall into modules of < 5 species, we need to
#correctly label them at the network level, because it is possible for
#these small species modules to be connected with other modules, thus
#increasing their participation coefficient and making them possibly
#connectors.
badintramod.deg$role <- ifelse(badintramod.deg$z.score < 2.5 & badintramod.deg$pcoef < 0.62,
"Peripheral","Connector")
#now we combine the data, label the columns, and reorder it so that the
#species are in the same order as the original network matrix
goodbadintramod.mat <- rbind(goodintramod.deg,badintramod.deg)
colnames(goodbadintramod.mat) <- c("spp", "kTotal","kIn","kOut","Strength",
"StrengthIn","StrengthOut","module","Z.score","pcoef",
"Role")
netintramod.deg2 <- goodbadintramod.mat[order(match(rownames(goodbadintramod.mat),
rownames(netintramod.deg))), ]
return(netintramod.deg2) #This should have the roles of the species now accurately defined
}
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