R/MSData.R

In MetabolicSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature

#' Generate Artificial Metabolic Survival Data
#'
#' The Function generates metabolic profile and survival dataset of any number of patients and also their survival information.
#'
#' The function generates the metabolic profile where small set of metabolites (30) are informative and rest of them are set as noisy metabolites.
#'Also, the survival time and Censoring information are generated based on first right singular vectors of \code{\link[base]{svd}} of the metabolic profile matrix.
#' It also generates other prognostic factors such as Age, Stage and Gender which are slightly correlated with survival time.
#' @param nPatients The number of patients
#' @param nMet The number of metabolites
#' @param Prop The proportion of patients having low risk
#' @return An object of class list is returned with the following items .
#'   \item{Censor}{The censoring/event indicator}
#'   \item{Survival}{The Survival time}
#'   \item{Met.names}{The vector of metabolites name}
#'   \item{Mdata}{The metabolic profile matrix}
#'   \item{Prognostic}{A data frame with prognostic factors.}
#' @author Olajumoke Evangelina Owokotomo, \email{olajumoke.owokotomo@@uhasselt.be}
#' @author Ziv Shkedy
#' @seealso \code{\link[survival]{coxph}}
#' @examples
#' #GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
#'
#' Data<-MSData(nPatients=100,nMet=150,Prop=0.5)
#'
#' SurvTime<-Data$Survival
#' Censor<-Data$Censor
#' ProgFact<-Data$Prognostic
#' MetData<-Data$Mdata
#' Metnames<-Data$Met.names
#' @import stats
#' @export MSData


MSData<-function(nPatients=100,nMet=150,Prop=0.5){

  Mdata<-matrix(rnorm(nMet*nPatients),ncol=nPatients)
  Mdata[1:10,]<-matrix(rnorm(10*nPatients,sd=1,mean=3),ncol=nPatients)
  Mdata[11:30,]<-matrix(rnorm(20*nPatients,sd=1,mean=2),ncol=nPatients)

  Survival<-c(abs(10+svd(Mdata[1:floor(nPatients*Prop),])$v[1:floor(nPatients*Prop),1]+
                    2*rnorm(floor(nPatients*Prop))),
              abs(8 +svd(Mdata[1:floor(nPatients*Prop),])$v[(floor(nPatients*Prop)+1):nPatients,1]+
                    3*rnorm(nPatients-floor(nPatients*Prop))))

  CenTime<-abs(10+svd(Mdata[1:floor(nPatients),])$v[1:floor(nPatients),1]+
                 1*rnorm(floor(nPatients)))

  Censor<- ifelse(Survival>CenTime,0,1)

  metabolitenames <- paste("Met",as.character(1:nMet),sep="")
  rownames(Mdata)<-metabolitenames

  ProgFact<-data.frame(Age=floor(Survival*0.68+rnorm(nPatients,30,10)),
                       Stage=sample(1:4,nPatients,replace=T),Gender=rbinom(nPatients, 1, Prop))

  Mdata = t(Mdata)
  return(list(Censor=Censor,Survival=Survival,Met.names=metabolitenames,Mdata=Mdata,Prognostic=ProgFact))
}