R/ne.PCA.R

In wf-frank2019/ne.PCA: The node and edge Prioritization based Community Analysis framework

#' Node and edge Prioritization-based Community Analysis framework
#'
#' @title ne.PCA function
#' @param nodes data.frame, two columns:object,label(0:candidates/1:seeds).
#' @param edges data.frame, two columns:gene symbol A,gene symbol B.
#' @param r default 0.8,Restart probability of random walk,interval is (0,1).
#' @param core default 10,Top percentile of ranking for object,interval is (0,100].
#' @param druGet TRUE or FALSE,default TRUE,module drugability statistics and prediction will implement if druGet=TRUE.
#' @import igraph dplyr dnet GOSemSim reshape2 ggplot2
#' @export ne.PCA
#' @author Fan Wang <justin15751821901@gmail.com>

ne.PCA <- function(nodes,
                   edges,
                   r=0.8,
                   core=10,
                   druGet=TRUE) {
  #Get the core network
  RWRs.CN(nodes,edges,r,core)
  coreNet<-read.table(paste('top',core,'%_CoreNet.txt',sep=''),header=F)
  colnames(coreNet)=c("name1","name2")
  #Check input format
  if(!ncol(edges) == 2)
  {
    stop("Param 'edges' input error!
         Only two columns needed!
         for example: TP53,EGFR
                      TP53,KRAS
                      EGFR,KRAS")
  }
  #Get the edge weight TFC
  TFCs(edges)
  ecount=nrow(edges)
  weight<-read.table(paste('TFC_',ecount,'interactions.txt',sep=''),header=T)
  WCN=merge(coreNet,weight,by=c("name1","name2"))
  #Export weight core network(WCN)
  write.table(WCN,"WCN.txt",sep="\t",quote=F,
              row.names=F)
  #Identify robust modules in WCN
  contrName=(deparse(substitute(druGet)))
  MODULEs(coreNet,tarGet=contrName)
  #Module drugability statistics and prediction
  if(druGet){
    edge<-read.table("edge_Module.txt",header=T)
    tarM<-read.table("Modules_druGable.txt",header=T)
    tarM$Avg.TFC=0
    tarM$RC=rank(-tarM[,2])
    tarM$RP=rank(-tarM[,3])
    tarM$RTFC=0
    tarEdge<-merge(weight,edge,by=c("name1","name2"))
    for(i in 1:nrow(tarM))
    {
      tarM$Avg.TFC[i]<-mean(tarEdge[which(tarEdge[,4]==i),3])
      tarM$RTFC=rank(-tarM$Avg.TFC)
    }
    tarM$Drugable=tarM$RC+tarM$RP+tarM$RTFC
    message("***********************************************", appendLF = T)
    message(paste(c("Largest number of drug targets is: Module ",
                    tarM[which(tarM$RC==1),1]), collapse = ""), appendLF = T)
    message(paste(c("Highest drug targets proportion is: Module ",
                    tarM[which(tarM$RP==1),1]), collapse = ""), appendLF = T)
    message(paste(c("Potential drug target area is: Module ",
                    tarM[which.min(tarM$Drugable),1]), collapse = ""),appendLF = T)
    message("***********************************************", appendLF = T)
    write.table(tarM[,1:4],"Modules_druGable.txt",sep="\t",quote=F,row.names=F)
  }
}