R/targetRank.R

In timma: Target Inhibition Interaction using Maximization and Minimization Averaging

#' Generate the list of ranked target combinations
#' 
#' A function to provide a list of target combiantions ranked by their predicted synergy scores
#' 
#' @param profile_select the drug-target interaction profile for the selected targets
#' @param predicted_matrix the predicted efficacy matrix
#' 
#' @return a matrix containing the list of target combinations
#' @author Jing Tang \email{jing.tang@@helsinki.fi} 
#' @references Tang J, Karhinen L, Xu T, Szwajda A, Yadav B, Wennerberg K, Aittokallio T. 
#' Target inhibition networks: predicting selective combinations of druggable targets to block cancer 
#' survival pathways. PLOS Computational Biology 2013; 9: e1003226.
#' @examples
#' \dontrun{
#' data(tyner_interaction_binary)
#' data(tyner_sensitivity)
#' float<-sffsBinary(tyner_interaction_binary, tyner_sensitivity[, 1], max_k = 8)
#' k_select<-float$k_sel
#' x<-data.frame(tyner_interaction_binary)
#' kinase_names <- dimnames(x)[[2]]
#' select_kinase_names <- findSameSet(x, k_select, kinase_names)
#' gc_timma <- graycode3(length(k_select))
#' gc_names <- graycodeNames(length(k_select), select_kinase_names, gc_timma$gc_row, gc_timma$gc_col)
#' nr <- gc_names$nr
#' nc <- t(gc_names$nc)
#' timma_row <- nrow(nr) + nrow(nc)
#' timma_col <- ncol(nr) + ncol(nc)
#' timma <- array("", dim = c(timma_row, timma_col))
#' timma[(nrow(nc) + 1):timma_row, 1:ncol(nr)] <- nr
#' timma[1:nrow(nc), (ncol(nr) + 1):timma_col] <- nc
#' timma[(nrow(nc) + 1):timma_row, (ncol(nr) + 1):timma_col] <- float$timma$dummy
#' profile_select<-data.frame(tyner_interaction_binary)[, k_select]
#' target_combo_rank<-targetRank(profile_select, timma)
#' }


targetRank <- function(profile_select, predicted_matrix) {
  profile_select <- cbind(dimnames(profile_select)[[1]], profile_select)
  ndrugs = dim(profile_select)[1]
  ntargets = dim(profile_select)[2] - 1
  
  timma <- predicted_matrix
  len1=(ntargets-floor(ntargets/2))
  len2=2^(floor(ntargets/2))
  len3=floor(ntargets/2)
  len4=2^(ntargets-floor(ntargets/2))
  
  if(len3==1) row_target = matrix(timma[c((1 + len1):(len2 + len1)),1],ncol=1) else row_target = apply(timma[c((1 + len1):(len2 + len1)), c(1:len3)], 2, as.character)
  if(len1==1) col_target = matrix(timma[1, c((1 + len3):(len4 + len3))],nrow=1) else col_target = apply(timma[c(1:len1), c((1 + len3):(len4 + len3))], 2, as.character)
  
  efficacy_mat = apply(timma[c((1 + len1):(len2 + len1)), c((1 + len3):(len4 + len3))], 2, as.numeric)
  # efficacy_mat = efficacy_mat/max(efficacy_mat)  # normalize the efficacy into range [0 1]
  efficacy_vec = matrix(efficacy_mat, length(c(efficacy_mat)), 1)
  
  # identical targets separated by '/'.  Replace ';' with '/' in timma1.csv Replace ';' with '/' in
  # selectedKinasse1.csv empty entries denoted by NA. Replace '-' with NA in timma1.csv
  row_target[which(row_target == "-")] = NA
  col_target[which(col_target == "-")] = NA
  row_target = gsub(";", "/", row_target)
  col_target = gsub(";", "/", col_target)
  
  names_mat = matrix(0,dim(row_target)[1], dim(col_target)[2])  # target combinations
  number_mat = matrix(0,dim(row_target)[1], dim(col_target)[2])  # number of target nodes in the combination
  for (i in 1:dim(row_target)[1]) {
    for (j in 1:dim(col_target)[2]) {
      str1 = paste(paste(row_target[i, ], collapse = " "), paste(col_target[, j], collapse = " "), collapse = "", 
                   sep = " ")
      # str2 = gsub('NA','',str1)
      str2 = gsub("\\<NA\\>", "", str1)  # exact match
      str3 = gsub("^\\s+|\\s+$", "", str2)  # remove leading and trailing space
      str4 = strsplit(str3, "\\s+")
      names_mat[i, j] = paste(unlist(str4), collapse = ";")
      number_mat[i, j] = length(str4[[1]])
    }
  }
  names_vec = matrix(names_mat, length(c(names_mat)), 1)
  number.vec = matrix(number_mat, length(c(number_mat)), 1)
  data_target = data.frame(cbind(names_vec, efficacy_vec, number.vec))
  colnames(data_target) = c("Gene", "timma", "Number")
  data_target$timma = as.numeric(as.character(data_target$timma))
  
  # deal with only single and pairwise targets
  data_single = data_target[which(data_target$Number == 1), ]
  data_pairwise = data_target[which(data_target$Number == 2), ]
  
  
  single_inhibition = matrix(0,dim(data_pairwise)[1], 5)
  colnames(single_inhibition) = c("timma1", "timma2", "timma.add", "timma.multi", "timma.highest")
  for (i in 1:dim(data_pairwise)[1]) {
    pair = data_pairwise$Gene[i]
    pair_name = unlist(strsplit(as.character(pair), ";"))
    # index = lapply(pair_name, function(x) grep(x, data_single$Gene, fixed = TRUE))
    index = lapply(pair_name, function(x) which(data_single$Gene==x))
    timma1 = data_single$timma[unlist(index[1])]
    timma2 = data_single$timma[unlist(index[2])]
    timma.add = data_pairwise$timma[i] - timma1 - timma2
    timma.multi = data_pairwise$timma[i] - timma1 * timma2
    # timma.multi2 = timma.add+timma1*timma2
    timma.highest = data_pairwise$timma[i] - max(timma1, timma2)
    single_inhibition[i, ] = c(timma1, timma2, timma.add, timma.multi, timma.highest)
  }
  data_pairwise = cbind(data_pairwise, single_inhibition)
  colnames(data_pairwise) = c("Target.pair","Sensitivity","Size","Target1.sen","Target2.sen","Synergy.add","Synergy.multi","Synergy.highest")
  
  return(data_pairwise)
}