R/dmGWAS.R
In Saadman/SigNetA:
#' dmGWAS Function
#'
#' This function allows you to analyze genes using algorithm mentioned in Ideker et.al. 2002
#' @param File ? Defaults to NULL.
#' @keywords dmGWAS
#' @export
#' @examples
#' dmGWAS() will analyze network for genes in signature from ilincs using ideker et. al. 2002 algorithm
dmGWAS<-function(File=NULL,layOut=1,proteinN=1,phy=FALSE,package=FALSE,nodeGoData=NULL,edgeGoData=NULL){
# library(dmGWAS)
# library(DLBCL)
# library(visNetwork)
# data(interactome)
# if(!is.null(File))
# { if(is.null(upload3)){
# File<-File
# }
# else{
# File<-read.csv(file=File,sep='\t')
# colnames(logic)<-c("signatureID","GeneID","GeneNames","coefficients","Pvals")
# }
# }
# else{
# File<-read.csv(file=system.file("extdata", "sig_try3.tsv", package = "SigNetA"),sep='\t')
#
# }
#logic<-File
#GeneNames and Pvals
if(proteinN==1){
gene2weight<-data.frame(gene=File$GeneNames,weight=File$Pvals)
sorted<-gene2weight[order(gene2weight$weight),]
topgenes<-head(sorted,301)
gene2weight<-topgenes
# print("stage 1 passed")
ppiGW.copy <- delete.edges(ppiGW, which(E(ppiGW)$weight <=0.7))
ppi <- rmSelfLoops(ppiGW.copy)
ppi=decompose.graph(ppi)[[1]]
# print(typeof(ppi))
# print(ppi)
#i_interactome<-igraph.from.graphNEL(interactome)
ppinetwork<-as.data.frame(get.edgelist(ppi))
ppinetwork$V1<-sub(" *\\(.*", "", ppinetwork$V1)
ppinetwork$V2<-sub(" *\\(.*", "", ppinetwork$V2)
#print(head(ppinetwork))
#p_val<-c()
#for(i in 1:length(gene2weight$weight))
# {
#if(gene2weight$weight[i]>=1)
#{
# gene2weight$weight[i]<-0.999
#append(p_val,gene2weight$weight[i])
#}
# else if(gene2weight$weight[i]<=0){
# gene2weight$weight[i]<-0.0000001
# }
#}
# print("stage 2 passed")
res.list = dms(ppinetwork, gene2weight, d=1, r=0.1) #works for 300 genes and d=1
##error while running res.list with different parameters
##100 genes error is Error in dm.result[[k]] : subscript out of bounds
##200 genes error is -Error in identical.idx[[k]] : subscript out of bounds
##201 and above works
##
# print("stage 3 passed")
selected = simpleChoose(res.list, top=100, plot=T)
#print("stage 4 passed")
#source("ExtraFiles/dmGWAS/plotmodule2.R")
logFC<-as.numeric(File$coefficients)
names(logFC)<-File$GeneNames
colorNet<-plotmodule2(selected$subnetwork, diff.expr =logFC)
}
else if(proteinN==2)
{
gene2weight<-data.frame(gene=File$GeneNames,weight=File$Pvals)
sorted<-gene2weight[order(gene2weight$weight),]
topgenes<-head(sorted,201)
gene2weight<-topgenes
#print("stage 1 passed")
i_interactome<-igraph.from.graphNEL(interactome)
ppinetwork<-as.data.frame(get.edgelist(i_interactome))
ppinetwork$V1<-sub(" *\\(.*", "", ppinetwork$V1)
ppinetwork$V2<-sub(" *\\(.*", "", ppinetwork$V2)
#print(head(ppinetwork))
#p_val<-c()
#for(i in 1:length(gene2weight$weight))
# {
#if(gene2weight$weight[i]>=1)
#{
# gene2weight$weight[i]<-0.999
#append(p_val,gene2weight$weight[i])
#}
# else if(gene2weight$weight[i]<=0){
# gene2weight$weight[i]<-0.0000001
# }
#}
#print("stage 2 passed")
res.list = dms(ppinetwork, gene2weight, d=2, r=0.1) #works for 300 genes and d=1
##error while running res.list with different parameters
##100 genes error is Error in dm.result[[k]] : subscript out of bounds
##200 genes error is -Error in identical.idx[[k]] : subscript out of bounds
##201 and above works
##
#print("stage 3 passed")
selected = simpleChoose(res.list, top=100, plot=T)
#print("stage 4 passed")
#source("ExtraFiles/dmGWAS/plotmodule2.R")
logFC<-as.numeric(File$coefficients)
names(logFC)<-File$GeneNames
colorNet<-plotmodule2(selected$subnetwork, diff.expr =logFC)
}
if(layOut=="1"){
l<-layout_with_fr(colorNet$n)
visLay<-"layout_with_fr"
}
else if(layOut=="3"){
l<-layout_on_grid(colorNet$n)
visLay<-"layout_on_grid"
}
else if(layOut=="4"){
l<-layout_with_kk(colorNet$n)
visLay<-"layout_with_kk"
}
else if(layOut=="5"){
l<-layout_on_sphere(colorNet$n)
visLay<-"layout_on_sphere"
}
else if(layOut=="6"){
l<-layout_with_graphopt(colorNet$n)
visLay<-"layout_with_graphopt"
}
module<-igraph.to.graphNEL(selected$subnetwork)
conNodes<-function(x){
if(ltn[x]>0)
id[x]
}
id <- nodes(module) #interactome
ltn<-unlist(lapply(edgeL(module),function(x) length(x[[1]]))) #interactome
modNodes<-unlist(lapply(1:length(ltn),conNodes ))
id<-modNodes
name <- id # interactome and rcytoscapejs2
label<-id
nodeData <- data.frame(id, name, stringsAsFactors=FALSE) ##RCYTOSCAPEJS2
nodeVisData<-data.frame(id,label,stringsAsFactors = FALSE)
id<-nodes(module)
nodeData$color<- rep("#00FF0F",nrow(nodeData)) #changed color of nodes
nodeData$shape <- "ellipse" #default shape
nodeData$href <- paste0("http://www.ncbi.nlm.nih.gov/gene/",gsub("[\\(\\)]", "", regmatches(nodeData$name, gregexpr("\\(.*?\\)", nodeData$name))))
nodeData$geneID<-gsub("[\\(\\)]", "", regmatches(nodeData$name, gregexpr("\\(.*?\\)", nodeData$name)))
nodeData$name<-sub(" *\\(.*", "", nodeData$name)
nodeData$Diff_Exp="none"
nodeData$x="none" #x and y are the columns required for manual layouts. Also, use "preset" as layout mode in rcytoscapejs2.R
nodeData$y="none"
for(i in 1:length(name)){
nodeData[i,3]<-colorNet$c[i];
nodeData[i,7]<-colorNet$d[[i]];
# nodeData[i,8]<-(l[i,1]+1)*100
# nodeData[i,9]<-(l[i,2] +1)*100
}
statNet<<-nodeData
nodeVisData$color.background<-rep("blue",nrow(nodeVisData))
nodeVisData$borderWidth <- 2
nodeVisData$color.border <- "black"
nodeVisData$title<-"<p>Hello world</p>"
nodeVisData$shape<-"dot"
nodeVisData$size<-0
nodeVisData$id<-sub(" *\\(.*", "", nodeVisData$id)
nodeVisData$label<-sub(" *\\(.*", "", nodeVisData$label)
normalize <- function(x) {
return (((x - min(x)) / (max(x) - min(x)))*50)
}
#print(label)
for(i in 1:length(label)){
nodeVisData[i,3]<-colorNet$c[i];
nodeVisData[i,6]<-paste0("<p><b>Gene name:</b>",statNet$name[i],"</p><br><p><b>Differential Expression:</b>",statNet$Diff_Exp[i])
# nodeVisData[i,6]<-paste0("GENE NAME");
if(colorNet$d[[i]]<0)
{
nodeVisData[i,8]<-colorNet$d[[i]] * -1
}
else{
nodeVisData[i,8]<-colorNet$d[[i]]
}
}
#print(nodeVisData)
nodeVisData<-data.frame(nodeVisData[1:7], apply(nodeVisData["size"],2, normalize) )
for( l in 1:length(nodeVisData$size)){
#print(nodeVisData$size[l])
if(nodeVisData$size[l]<1)
{
nodeVisData$size[l]<-1
}
}
#print(nodeVisData)
ltn<-unlist(lapply(edgeL(module),function(x) length(x[[1]])))
sourceVis<-unlist(lapply(1:length(ltn),function(x) rep(id[x],ltn[x])))
targetVis<-unlist(lapply(edgeL(module), function(x) id[unlist(x)]))
vect<-c()
for(i in 1:length(targetVis)) #extracting the value from the key value pair
vect[i]<-targetVis[[i]]
sourceVis<-sub(" *\\(.*", "", sourceVis)
targetVis<-sub(" *\\(.*", "", targetVis)
edgeVisData <- data.frame(from=sourceVis, to=targetVis, stringsAsFactors=FALSE)
for (i in 1:nrow(edgeVisData))
{
edgeVisData[i, ] = sort(edgeVisData[i, ])
}
edgeVisData = edgeVisData[!duplicated(edgeVisData),]
##STRING TWO(ADDING EDGE VALUES)#
if(proteinN=="1"){
edgeVisData$title<-"ppi"
edgeVisDataMod<-merge(edgeVisData,s,by.x = c("from","to"),by.y =c("a","b"),all.x = TRUE)
for(i in 1:nrow(edgeVisDataMod)){
edgeVisDataMod$title[i]<-paste0("<p><b>Neighborhood score:</b>",edgeVisDataMod$f.neighborhood[i],"</p><b>Fusion score:</b>",edgeVisDataMod$f.fusion[i],"</p><b>Cooccurence score:</b>",edgeVisDataMod$f.cooccurence[i],"</p><b>Coexpression score:</b>",edgeVisDataMod$f.coexpression[i],"</p><b>Experimental score:</b>",edgeVisDataMod$f.experimental[i],"</p><b>Database score:</b>",edgeVisDataMod$f.database[i],"</p><b>Textmining score:</b>",edgeVisDataMod$f.textmining[i],"</p><b>Combined score:</b>",edgeVisDataMod$f.combined_score[i],"</p>")
}
edgeVisData<-edgeVisDataMod
}
##END...STRING TWO (ADDING EDGE VALUES)##
if(is.null(nodeGoData) & is.null(edgeGoData))
{
visObj<- visNetwork(nodeVisData, edgeVisData,height="800px",width="900px")
}
else{
visObj<- visNetwork(nodeGoData, edgeGoData,height="800px",width="900px")
# visObj<-enrichObj
}
visObj<-visExport(visObj,type ="png", name="network",float="right")
if(phy){
# print("physics active")
}
else{
visObj<-visIgraphLayout(visObj,layout=visLay)
}
if(package==TRUE)
{
visNetwork(nodeVisData, edgeVisData,height="800px",width="900px")
}
else{
visObj<-visInteraction(visObj,navigationButtons = TRUE)
visObj<-visOptions(visObj,manipulation = TRUE)
}
return(list("networkObj"=visObj,"networkData"=statNet,"edgeData"=edgeVisData,"nodeData"=nodeVisData))
}
Saadman/SigNetA documentation built on May 29, 2019, 2:32 p.m.
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#' dmGWAS Function
#'
#' This function allows you to analyze genes using algorithm mentioned in Ideker et.al. 2002
#' @param File ? Defaults to NULL.
#' @keywords dmGWAS
#' @export
#' @examples
#' dmGWAS() will analyze network for genes in signature from ilincs using ideker et. al. 2002 algorithm
dmGWAS<-function(File=NULL,layOut=1,proteinN=1,phy=FALSE,package=FALSE,nodeGoData=NULL,edgeGoData=NULL){
# library(dmGWAS)
# library(DLBCL)
# library(visNetwork)
# data(interactome)
# if(!is.null(File))
# { if(is.null(upload3)){
# File<-File
# }
# else{
# File<-read.csv(file=File,sep='\t')
# colnames(logic)<-c("signatureID","GeneID","GeneNames","coefficients","Pvals")
# }
# }
# else{
# File<-read.csv(file=system.file("extdata", "sig_try3.tsv", package = "SigNetA"),sep='\t')
#
# }
#logic<-File
#GeneNames and Pvals
if(proteinN==1){
gene2weight<-data.frame(gene=File$GeneNames,weight=File$Pvals)
sorted<-gene2weight[order(gene2weight$weight),]
topgenes<-head(sorted,301)
gene2weight<-topgenes
# print("stage 1 passed")
ppiGW.copy <- delete.edges(ppiGW, which(E(ppiGW)$weight <=0.7))
ppi <- rmSelfLoops(ppiGW.copy)
ppi=decompose.graph(ppi)[[1]]
# print(typeof(ppi))
# print(ppi)
#i_interactome<-igraph.from.graphNEL(interactome)
ppinetwork<-as.data.frame(get.edgelist(ppi))
ppinetwork$V1<-sub(" *\\(.*", "", ppinetwork$V1)
ppinetwork$V2<-sub(" *\\(.*", "", ppinetwork$V2)
#print(head(ppinetwork))
#p_val<-c()
#for(i in 1:length(gene2weight$weight))
# {
#if(gene2weight$weight[i]>=1)
#{
# gene2weight$weight[i]<-0.999
#append(p_val,gene2weight$weight[i])
#}
# else if(gene2weight$weight[i]<=0){
# gene2weight$weight[i]<-0.0000001
# }
#}
# print("stage 2 passed")
res.list = dms(ppinetwork, gene2weight, d=1, r=0.1) #works for 300 genes and d=1
##error while running res.list with different parameters
##100 genes error is Error in dm.result[[k]] : subscript out of bounds
##200 genes error is -Error in identical.idx[[k]] : subscript out of bounds
##201 and above works
##
# print("stage 3 passed")
selected = simpleChoose(res.list, top=100, plot=T)
#print("stage 4 passed")
#source("ExtraFiles/dmGWAS/plotmodule2.R")
logFC<-as.numeric(File$coefficients)
names(logFC)<-File$GeneNames
colorNet<-plotmodule2(selected$subnetwork, diff.expr =logFC)
}
else if(proteinN==2)
{
gene2weight<-data.frame(gene=File$GeneNames,weight=File$Pvals)
sorted<-gene2weight[order(gene2weight$weight),]
topgenes<-head(sorted,201)
gene2weight<-topgenes
#print("stage 1 passed")
i_interactome<-igraph.from.graphNEL(interactome)
ppinetwork<-as.data.frame(get.edgelist(i_interactome))
ppinetwork$V1<-sub(" *\\(.*", "", ppinetwork$V1)
ppinetwork$V2<-sub(" *\\(.*", "", ppinetwork$V2)
#print(head(ppinetwork))
#p_val<-c()
#for(i in 1:length(gene2weight$weight))
# {
#if(gene2weight$weight[i]>=1)
#{
# gene2weight$weight[i]<-0.999
#append(p_val,gene2weight$weight[i])
#}
# else if(gene2weight$weight[i]<=0){
# gene2weight$weight[i]<-0.0000001
# }
#}
#print("stage 2 passed")
res.list = dms(ppinetwork, gene2weight, d=2, r=0.1) #works for 300 genes and d=1
##error while running res.list with different parameters
##100 genes error is Error in dm.result[[k]] : subscript out of bounds
##200 genes error is -Error in identical.idx[[k]] : subscript out of bounds
##201 and above works
##
#print("stage 3 passed")
selected = simpleChoose(res.list, top=100, plot=T)
#print("stage 4 passed")
#source("ExtraFiles/dmGWAS/plotmodule2.R")
logFC<-as.numeric(File$coefficients)
names(logFC)<-File$GeneNames
colorNet<-plotmodule2(selected$subnetwork, diff.expr =logFC)
}
if(layOut=="1"){
l<-layout_with_fr(colorNet$n)
visLay<-"layout_with_fr"
}
else if(layOut=="3"){
l<-layout_on_grid(colorNet$n)
visLay<-"layout_on_grid"
}
else if(layOut=="4"){
l<-layout_with_kk(colorNet$n)
visLay<-"layout_with_kk"
}
else if(layOut=="5"){
l<-layout_on_sphere(colorNet$n)
visLay<-"layout_on_sphere"
}
else if(layOut=="6"){
l<-layout_with_graphopt(colorNet$n)
visLay<-"layout_with_graphopt"
}
module<-igraph.to.graphNEL(selected$subnetwork)
conNodes<-function(x){
if(ltn[x]>0)
id[x]
}
id <- nodes(module) #interactome
ltn<-unlist(lapply(edgeL(module),function(x) length(x[[1]]))) #interactome
modNodes<-unlist(lapply(1:length(ltn),conNodes ))
id<-modNodes
name <- id # interactome and rcytoscapejs2
label<-id
nodeData <- data.frame(id, name, stringsAsFactors=FALSE) ##RCYTOSCAPEJS2
nodeVisData<-data.frame(id,label,stringsAsFactors = FALSE)
id<-nodes(module)
nodeData$color<- rep("#00FF0F",nrow(nodeData)) #changed color of nodes
nodeData$shape <- "ellipse" #default shape
nodeData$href <- paste0("http://www.ncbi.nlm.nih.gov/gene/",gsub("[\\(\\)]", "", regmatches(nodeData$name, gregexpr("\\(.*?\\)", nodeData$name))))
nodeData$geneID<-gsub("[\\(\\)]", "", regmatches(nodeData$name, gregexpr("\\(.*?\\)", nodeData$name)))
nodeData$name<-sub(" *\\(.*", "", nodeData$name)
nodeData$Diff_Exp="none"
nodeData$x="none" #x and y are the columns required for manual layouts. Also, use "preset" as layout mode in rcytoscapejs2.R
nodeData$y="none"
for(i in 1:length(name)){
nodeData[i,3]<-colorNet$c[i];
nodeData[i,7]<-colorNet$d[[i]];
# nodeData[i,8]<-(l[i,1]+1)*100
# nodeData[i,9]<-(l[i,2] +1)*100
}
statNet<<-nodeData
nodeVisData$color.background<-rep("blue",nrow(nodeVisData))
nodeVisData$borderWidth <- 2
nodeVisData$color.border <- "black"
nodeVisData$title<-"<p>Hello world</p>"
nodeVisData$shape<-"dot"
nodeVisData$size<-0
nodeVisData$id<-sub(" *\\(.*", "", nodeVisData$id)
nodeVisData$label<-sub(" *\\(.*", "", nodeVisData$label)
normalize <- function(x) {
return (((x - min(x)) / (max(x) - min(x)))*50)
}
#print(label)
for(i in 1:length(label)){
nodeVisData[i,3]<-colorNet$c[i];
nodeVisData[i,6]<-paste0("<p><b>Gene name:</b>",statNet$name[i],"</p><br><p><b>Differential Expression:</b>",statNet$Diff_Exp[i])
# nodeVisData[i,6]<-paste0("GENE NAME");
if(colorNet$d[[i]]<0)
{
nodeVisData[i,8]<-colorNet$d[[i]] * -1
}
else{
nodeVisData[i,8]<-colorNet$d[[i]]
}
}
#print(nodeVisData)
nodeVisData<-data.frame(nodeVisData[1:7], apply(nodeVisData["size"],2, normalize) )
for( l in 1:length(nodeVisData$size)){
#print(nodeVisData$size[l])
if(nodeVisData$size[l]<1)
{
nodeVisData$size[l]<-1
}
}
#print(nodeVisData)
ltn<-unlist(lapply(edgeL(module),function(x) length(x[[1]])))
sourceVis<-unlist(lapply(1:length(ltn),function(x) rep(id[x],ltn[x])))
targetVis<-unlist(lapply(edgeL(module), function(x) id[unlist(x)]))
vect<-c()
for(i in 1:length(targetVis)) #extracting the value from the key value pair
vect[i]<-targetVis[[i]]
sourceVis<-sub(" *\\(.*", "", sourceVis)
targetVis<-sub(" *\\(.*", "", targetVis)
edgeVisData <- data.frame(from=sourceVis, to=targetVis, stringsAsFactors=FALSE)
for (i in 1:nrow(edgeVisData))
{
edgeVisData[i, ] = sort(edgeVisData[i, ])
}
edgeVisData = edgeVisData[!duplicated(edgeVisData),]
##STRING TWO(ADDING EDGE VALUES)#
if(proteinN=="1"){
edgeVisData$title<-"ppi"
edgeVisDataMod<-merge(edgeVisData,s,by.x = c("from","to"),by.y =c("a","b"),all.x = TRUE)
for(i in 1:nrow(edgeVisDataMod)){
edgeVisDataMod$title[i]<-paste0("<p><b>Neighborhood score:</b>",edgeVisDataMod$f.neighborhood[i],"</p><b>Fusion score:</b>",edgeVisDataMod$f.fusion[i],"</p><b>Cooccurence score:</b>",edgeVisDataMod$f.cooccurence[i],"</p><b>Coexpression score:</b>",edgeVisDataMod$f.coexpression[i],"</p><b>Experimental score:</b>",edgeVisDataMod$f.experimental[i],"</p><b>Database score:</b>",edgeVisDataMod$f.database[i],"</p><b>Textmining score:</b>",edgeVisDataMod$f.textmining[i],"</p><b>Combined score:</b>",edgeVisDataMod$f.combined_score[i],"</p>")
}
edgeVisData<-edgeVisDataMod
}
##END...STRING TWO (ADDING EDGE VALUES)##
if(is.null(nodeGoData) & is.null(edgeGoData))
{
visObj<- visNetwork(nodeVisData, edgeVisData,height="800px",width="900px")
}
else{
visObj<- visNetwork(nodeGoData, edgeGoData,height="800px",width="900px")
# visObj<-enrichObj
}
visObj<-visExport(visObj,type ="png", name="network",float="right")
if(phy){
# print("physics active")
}
else{
visObj<-visIgraphLayout(visObj,layout=visLay)
}
if(package==TRUE)
{
visNetwork(nodeVisData, edgeVisData,height="800px",width="900px")
}
else{
visObj<-visInteraction(visObj,navigationButtons = TRUE)
visObj<-visOptions(visObj,manipulation = TRUE)
}
return(list("networkObj"=visObj,"networkData"=statNet,"edgeData"=edgeVisData,"nodeData"=nodeVisData))
}
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