R/DrugClustPAM.R

In DrugClust: Implementation of a Machine Learning Framework for Predicting Drugs Side Effects

#FinalDrugClust with PAM algorithm
#' DrugClustPAM
#'
#' Function Implementing DrugClust with PAM algorithm
#'
#'
#' @name DrugClustPAM
#' @param num_folds number of folds
#' @param num_clusters number of clusters
#' @param num_iterations number of iterations
#' @param features features matrix
#' @param side_effects side_effects matrix
#' @return (list(AUCFinal,AUPRFinal)) first value is the mean AUC on the various folders, second value is the mean AUPR on the various folders
#' @examples
#' # num_folds=3
#' # num_clusters=4
#' # num_iterations= 5
#' #features is the features matrix (see InitFeatures function)
#' # side effects is the matrix containing side effects (see InitSideEffects function)
#' #result<-DrugClustPAM(num_folds,num_clusters,num_iterations,features,side_effects)
#' @export

DrugClustPAM<-function(num_folds,num_clusters,num_iterations,features,side_effects){
  #vectors where the means of the AUC will be stored
  vectorMedieAUC<-numeric()

  #vectors where the means of the AUPR will be stored
  vectorMedieAUPR<-numeric()

  #various Iterations of the k-folds cross validation procedure
  for(j in 1:num_iterations){
    i=0 # variable to trace that is the first time of the k-fold cross validation procedure
    vectorAUC<-numeric() #instantiate a vecotr of AUC for first iteration of the k-fold cross validation procedure
    vectorAUPR<-numeric() # vector of AUPR for first iteration of the k-folds cross validation procedure

    folds<-CreateFolds(features,num_folds)
    train = features[folds != i,]
    trainpharmat = side_effects[folds != i,]
    test = features[folds == i,]
    testpharmat = side_effects[folds == i,]

    #PAM clustering
    pamx<-PAM(train,num_clusters)

    #PAM clustering method application

    A<-PAM_Model(pamx,num_clusters,trainpharmat,train)

    #KSeeds predictions for drugs in the test set
    predizioni<-PredictionPAM(A,pamx,test)

    #Function for obtaining AUC
    vectorAUC<-AUC(predizioni,testpharmat,vectorAUC,"PAM")

    #Function for obtaining AUPR
    vectorAUPR<-AUPR(predizioni,testpharmat,vectorAUPR,"PAM")

    for(i in 1:(num_folds-1)){
      train = features[folds != i,]
      trainpharmat = side_effects[folds != i,]
      test = features[folds == i,]
      testpharmat = side_effects[folds == i,]

      #KMeans clustering and model
      pamx<-PAM(train,num_clusters)
      A<-PAM_Model(pamx,num_clusters,trainpharmat,train)

      #KSeeds predictions for drugs in the test set
      predizioni<-PredictionPAM(A,pamx,test)

      #Function for obtaining AUC
      vectorAUC<-AUC(predizioni,testpharmat,vectorAUC,"PAM")

      #Function for obtaining AUPR
      vectorAUPR<-AUPR(predizioni,testpharmat,vectorAUPR,"PAM")
    }
    vectorMedieAUC<-c(vectorMedieAUC,mean(vectorAUC))
    vectorMedieAUPR <-c(vectorMedieAUC,mean(vectorAUPR))
    j=j+1

  }

  AUCFinal<-mean(vectorMedieAUC)
  AUPRFinal<-mean(vectorMedieAUPR)
  return(list(AUCFinal,AUPRFinal))

}

#######################################
#FinalDrugClust with PAM algorithm and enrichment
#' DrugClustPAMEnrichment
#'
#' Function Implementing DrugClust with PAM algorithm and Enrichment
#'
#'
#' @name DrugClustPAMEnrichment
#' @param num_clusters number of clusters desired
#' @param features matrix of features
#' @return number of pathways for various clusters
#' @examples
#' #features is the features matrix
#' #resultSeeds<-DrugClustPAMEnrichment(4,features)
#' @export

DrugClustPAMEnrichment<-function(num_clusters,features){
  source('enrichmentGO.R')

  #Return the list of clusters
  pamx<-PAM(features,num_clusters)
  #clusters<-clusters2$cluster
  all_pathways<-Enrichment_Proteins(features,num_clusters,pamx$clustering)

  return (all_pathways)

}