R/data_preprocess.R

In dSimer: Integration of Disease Similarity Methods

#' convert x-y associations
#' 
#' concert x-y associations (e.g. disease-gene associations) from data.frame 
#' to list
#' @param x2ydf data.frame of x-y associations
#' @param xcol col of x in x2ydf
#' @param ycol col of y in x2ydf
#' @return a list of x-y associations
#' @export
#' @author Peng Ni, Min Li
#' @examples
#' options(stringsAsFactors = FALSE)
#' 
#' d2g_fundo_sample<-read.table(text = "DOID:5218    IL6
#' DOID:8649  EGFR
#' DOID:8649	PTGS2
#' DOID:8649	VHL
#' DOID:8649	ERBB2
#' DOID:8649	PDCD1
#' DOID:8649	KLRC1
#' DOID:5214	MPZ
#' DOID:5214	EGR2
#' DOID:5210	AMH")
#' 
#' d2g_fundo_list<-x2y_df2list(d2g_fundo_sample)
x2y_df2list<-function(x2ydf,xcol=1,ycol=2){
  colnames(x2ydf) <- as.character(c(1:ncol(x2ydf)))
  colnames(x2ydf)[c(xcol,ycol)]<-c('V1','V2')
  
  x2ydf[,xcol]<-as.character(x2ydf[,xcol])
  x2ydf[,ycol]<-as.character(x2ydf[,ycol])
  
  xname<-unique(as.data.frame(x2ydf)[,xcol])
  xnamelen<-length(xname)
  x2y_list<-list(length=xnamelen)
  for(i in 1:xnamelen){
    x2y_list[[i]]<-unique(c(x2ydf[x2ydf$V1==xname[i],]$V2))
  }
  names(x2y_list)<-xname
  return(x2y_list)
}

#' convert x2ylist to y2xlist
#' 
#' convert list of x-y associations to list of y-x associations
#' @param x2ylist a list which the names are xs and the elements are ys 
#' of each x
#' @return a list of y2x
#' @export
#' @author Peng Ni, Min Li
#' @examples
#' data(go2g_sample)
#' g2go_sample<-x2y_conv2_y2x(go2g_sample[1:100])
x2y_conv2_y2x<-function(x2ylist){
  xnames<-names(x2ylist)
  ynames<-unique(unlist(x2ylist))
  y2xlist<-list()
  for(n in xnames){
   gofn<-x2ylist[[n]]
   for(g in gofn){
     y2xlist[[g]]<-union(y2xlist[[g]],n)
   }
  }
  return(y2xlist)
}

## AnnotationDbi using org.Hs.eg.db, GO-gene association remove IEAs
## only BP
## return a list
#' get GO-gene associations
#' 
#' get GO-gene associations from GO.db and org.Hs.eg.db
#' @param GOONTOLOGY "BP" or "MF" or "CC
#' @param geneid gene id type, "ENTREZID" or "SYMBOL" or"OMIM"
#' @param rm.IEAs logical value, remove GO terms with evidence "IEA" or 
#' not
#' @param rm.termlessthan3genes logical value, remove terms whose number 
#' of annotated genes are less than 3 or not
#' @return a list which names are GO term IDs and elements are gene ids 
#' or symbols annotated with GO terms
#' @importMethodsFrom AnnotationDbi as.list
#' @importFrom GO.db GOBPOFFSPRING
#' @importFrom GO.db GOMFOFFSPRING
#' @importFrom GO.db GOCCOFFSPRING
#' @importMethodsFrom AnnotationDbi keys
#' @importMethodsFrom AnnotationDbi select
#' @import org.Hs.eg.db
#' @export
#' @author Peng Ni, Min Li
#' @references Mathur S, Dinakarpandian D. Finding disease similarity based 
#' on implicit semantic similarity[J]. Journal of biomedical informatics, 
#' 2012, 45(2): 363-371.
#' @seealso \code{\link{PSB}}, \code{\link{Sun_function}}
#' @examples
#' go2g<-get_GOterm2GeneAssos(GOONTOLOGY="BP", geneid="SYMBOL")
#' go2g
get_GOterm2GeneAssos<-function(GOONTOLOGY=c("BP","MF","CC"), 
                               geneid=c("ENTREZID", "SYMBOL", "OMIM"), 
                               rm.IEAs=TRUE, 
                               rm.termlessthan3genes=TRUE){
  entKeys <- keys(org.Hs.eg.db, keytype="ENTREZID")
  geneid<-match.arg(geneid)
  cols<-c(geneid,"ONTOLOGY","GO","EVIDENCE")
  asso<-select(org.Hs.eg.db, keys=entKeys, columns=cols, 
               keytype="ENTREZID")
  
  GOONTOLOGY<-match.arg(GOONTOLOGY)
  asso<-asso[which(asso$ONTOLOGY==GOONTOLOGY),]
  
  if(rm.IEAs==TRUE){
    asso<-asso[which(asso$EVIDENCE!="IEA"),]
  }
  bpnames<-unique(asso$GO)
  switch(GOONTOLOGY,BP={
    go_offsp<-as.list(GOBPOFFSPRING)
  },MF={
    go_offsp<-as.list(GOMFOFFSPRING)
  },CC={
    go_offsp<-as.list(GOCCOFFSPRING)
  })
  
  go_offsp<-go_offsp[which(names(go_offsp) %in% bpnames)]
  go_offsp<-go_offsp[!(is.na(go_offsp))]
  go2g<-x2y_df2list(asso[,c("GO", geneid)])
  
  go2gfull<-go2g_full(go2g,go_offsp)
  
  if(rm.termlessthan3genes==TRUE){
    go2gfull<-filterGO2Glists(go2gfull)
  }
  return(go2gfull)
}

filterGO2Glists<-function(go2g){
  p_i=1
  resultlist<-list()
  resultname<-list()
  go2glen<-length(go2g)
  for(i in 1:go2glen){
    if(length(go2g[[i]])>2){
      resultlist[[p_i]]<-go2g[[i]]
      resultname[[p_i]]<-names(go2g)[i]
      p_i=p_i+1
    }
  }
  names(resultlist)<-unlist(resultname)
  return(resultlist)
}


#' convert data.frame of HumanNet log-likelihood Score to list
#' 
#' convert HumanNet normalized log-likelihood score from data.frame to list,
#'  which will be used in FunSim method
#' @param LLSn data.frame of gene-gene normalized log-likelihood score in 
#' HumanNet
#' @return  a list of normalized log-likelihood score
##' @importFrom stats setNames
#' @export
#' @author Peng Ni, Min Li
#' @references Cheng L, Li J, Ju P, et al. SemFunSim: a new method for measuring 
#' disease similarity by integrating semantic and gene functional association[J]. 
#' PloS one, 2014, 9(6): e99415.
#' @seealso \code{\link{FunSim}}
#' @examples
#' ## see examples in function FunSim
#' data(HumanNet_sample)
#' llsnlist<-LLSn2List(HumanNet_sample[1:100,])
#' llsnlist
LLSn2List<-function(LLSn){
  LLSn[,1]<-as.character(LLSn[,1])
  LLSn[,2]<-as.character(LLSn[,2])
  
  names<-unique(union(LLSn[,1],LLSn[,2]))
  
  LLSnSMat<-vector("list",length = length(names))
  names(LLSnSMat)<-names
  llsnsmlen<-length(LLSnSMat)
  for(i in 1:llsnsmlen){
    LLSnSMat[[i]]<-vector("numeric")
  }
  for(n in names){
    subsetLLSn<-rbind(LLSn[LLSn[,1]==n,],
                      setNames(LLSn[LLSn[,2]==n,][,c(2,1,3)],
                               names(LLSn)))
    LLSnSMat[[n]]<-subsetLLSn[,3]
    names(LLSnSMat[[n]])<-subsetLLSn[,2]
  }
  return(LLSnSMat)
}