R/SurvPlsClass.R

In MicrobiomeSurv: Biomarker Validation for Microbiome-Based Survival Classification and Prediction

#'Survival PLS and Classification for microbiome data
#'
#' The function performs partial least squares (PLS) and principal component regression on microbiome matrix and fit Cox proportional hazard model with covariates using the first PLS scores as covariates.
#'
#' This function reduces larger microbiome matrix to smaller version using supervised pca approach.
#' The function performs the PLS on the reduced microbiome matrix and fit Cox proportional hazard model with first PLS scores as a covariate afterwards.
#' And classifier is then built based on the first PLS scores multiplied by its estimated regression coefficient.
#' Patients are classified using mean of the risk scores as default. However, user can choose any quantile.
#' This function can handle single and multiple taxa. Prognostic factors can also be included to enhance classification.
#' @param Survival A vector of survival time with length equals to number of subjects
#' @param Micro.mat A large or small microbiome profile matrix. A matrix with microbiome profiles where the number of rows should be equal to the number of taxa and number of columns should be equal to number of patients.
#' @param Censor A vector of censoring indicator
#' @param Reduce A boolean parameter indicating if the microbiome profile matrix should be reduced, default is TRUE and larger microbiome profile matrix is reduced by supervised pca approach and first pca is extracted from the reduced matrix to be used in the classifier.
#' @param Select Number of  taxa (default is 5) to be selected from supervised PCA. This is valid only if the argument Reduce=TRUE
#' @param Prognostic A dataframe containing possible prognostic(s) factor and/or treatment effect to be used in the model.
#' @param Plots A boolean parameter indicating if the plots should be shown. Default is FALSE
#' @param Mean The cut off value for the classifier, default is the mean cutoff
#' @param Quantile If user want to use quantile as cutoff point. They need to specify Mean = FALSE and a quantile that they want to use. The default is the median cutoff

#' @return A object is returned with the following values
#'   \item{Survfit}{The cox proportional regression result using the first PCA}
#'   \item{Riskscores}{A vector of risk scores which is equal to the number of patents.}
#'   \item{Riskgroup}{The classification of the subjects based on the PCA into low or high risk group}
#'   \item{pc1}{The First PCA scores based on either the reduced Metabolite matrix or the full matrix}
#' \item{KMplot}{The Kaplan-Meier survival plot of the riskgroup}
#'  \item{SurvBPlot}{The distribution of the survival in the riskgroup}
#'  \item{Riskpls}{The plot of  Risk scores vs first PLS}
#' @author Thi Huyen Nguyen, \email{thihuyen.nguyen@@uhasselt.be}
#' @author Olajumoke Evangelina Owokotomo, \email{olajumoke.x.owokotomo@@gsk.com}
#' @author Ziv Shkedy
#' @seealso \code{\link[survival]{coxph}},
#' \code{\link[MicrobiomeSurv]{EstimateHR}}, \code{\link[pls]{plsr}},
#'  \code{\link[MicrobiomeSurv]{SurvPcaClass}}
#' @examples
#' # Prepare data
#' data(Week3_response)
#' Week3_response = data.frame(Week3_response)
#' surv_fam_shan_w3 = data.frame(cbind(as.numeric(Week3_response$T1Dweek),
#' as.numeric(Week3_response$T1D)))
#' colnames(surv_fam_shan_w3) = c("Survival", "Censor")
#' prog_fam_shan_w3 = data.frame(factor(Week3_response$Treatment_new))
#' colnames(prog_fam_shan_w3) = c("Treatment")
#' data(fam_shan_trim_w3)
#' names_fam_shan_trim_w3 =
#' c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
#' fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
#' rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3

#' # Using the function
#' SPLS_fam_shan_w3 = SurvPlsClass(Survival = surv_fam_shan_w3$Survival,
#'                                 Micro.mat = fam_shan_trim_w3,
#'                                 Censor = surv_fam_shan_w3$Censor,
#'                                 Reduce=TRUE,
#'                                 Select=5,
#'                                 Prognostic = prog_fam_shan_w3,
#'                                 Plots = TRUE,
#'                                 Mean = TRUE)
#'
#' # Getting the survival regression output
#' SPLS_fam_shan_w3$SurvFit
#'
#' # Getting the riskscores
#' SPLS_fam_shan_w3$Riskscores
#'
#' # Getting the riskgroup
#' SPLS_fam_shan_w3$Riskgroup
#'
#' # Obtaining the first principal component scores
#' SPLS_fam_shan_w3$pc1

#' @import stats
#' @import superpc
#' @import survival
#' @import lmtest
#' @import ggplot2
#' @import pls
#' @export SurvPlsClass


SurvPlsClass=function(
  Survival,
  Micro.mat,
  Censor,
  Reduce=TRUE,
  Select=150,
  Prognostic=NULL,
  Plots = FALSE,
  Mean = TRUE,
  Quantile = 0.5){

  if (missing(Survival)) stop("Argument 'Survival' is missing...")
  if (missing(Micro.mat)) stop("Argument 'Micro.mat' is missing...")
  if (missing(Censor)) stop("Argument 'Censor' is missing...")

  n.mi.full = dim(Micro.mat)[1]
  n.obs = dim(Micro.mat)[2]
  coef = exp.coef = p.value.LRT = c(1 : n.mi.full)

  if (Reduce) {
    if (is.null(Prognostic)){
      DataForReduction=list(x=Micro.mat,y=Survival, censoring.status=Censor, mi.names=rownames(Micro.mat))
      TentativeList=names(sort(abs(superpc::superpc.train(DataForReduction, type="survival")$feature.scores),decreasing =TRUE))[1:Select]
      TentativeList

      ReduMicro.mat=Micro.mat[TentativeList, ]
    }

    if (!is.null(Prognostic)){
      if (is.data.frame(Prognostic)) {
        nPrgFac=ncol(Prognostic)
        NameProg=colnames(Prognostic)
      }

      if (dim(Prognostic)[2] == 1){
        cox.prog = eval(parse(text = paste("survival::coxph(survival::Surv(Survival, Censor)", paste(" ~ Prognostic[ ,1])"))))
      } else{
        cox.prog = eval(parse(text = paste("survival::coxph(survival::Surv(Survival, Censor) ~ NameProg[1]", paste("+", NameProg[2:nPrgFac], sep="", collapse =""), paste(")"))))
      }

      for(i in 1 : n.mi.full){
        xi = Micro.mat[i, ]
        datai = data.frame(Survival, Censor, xi, Prognostic)
        modeli = eval(parse(text = paste("survival::coxph(survival::Surv(Survival, Censor) ~ xi", paste("+", NameProg[1:nPrgFac], sep="", collapse =""), ",data=datai)" , sep="" )))
        coef[i] = round(summary(modeli)$coefficients[1,1], 4)
        exp.coef[i] = round(summary(modeli)$coefficients[1,2], 4)
        p.value.LRT[i] = round(lmtest::lrtest(cox.prog, modeli)[2,5], 4)
      }

      p.value = round(stats::p.adjust(p.value.LRT, method = "BH", n = length(p.value.LRT)), 4)
      summary = cbind(coef, exp.coef, p.value.LRT, p.value)
      rownames(summary) = rownames(Micro.mat)
      colnames(summary) = c("coef", "exp.coef", "p.value.LRT", "p.value")
      TentativeList=names(sort(abs(summary[ ,"p.value.LRT"]),decreasing =TRUE))[1:Select]
      TentativeList

      ReduMicro.mat = Micro.mat[TentativeList, ]
      summary.reduced = summary[TentativeList, ]
    }

  } else {
    ReduMicro.mat = Micro.mat
  }


  n.mi=nrow(ReduMicro.mat)
  if (is.matrix(ReduMicro.mat)) {

    DataPLS=data.frame(1:n.obs)
    DataPLS$fam=as.matrix(t(ReduMicro.mat))
    colnames(DataPLS)[1]=c("Survival")
    DataPLS[,1]=Survival

    plsr.1 = pls::plsr(Survival ~ fam, method="simpls", ncomp = 1, scale =TRUE, data = DataPLS, validation =  "CV")
    pc1=pls::scores(plsr.1)[,1] # extract the first column
  } else {
    pc1=ReduMicro.mat
  }

  if (is.null(Prognostic)) {

    cdata = data.frame(Survival,Censor,pc1)
    m0 = survival::coxph(survival::Surv(Survival, Censor==1) ~ pc1,data=cdata)
  }
  if (!is.null(Prognostic)) {
    if (is.data.frame(Prognostic)) {
      nPrgFac=ncol(Prognostic)
      cdata = data.frame(Survival,Censor,pc1,Prognostic)
      NameProg=colnames(Prognostic)
      eval(parse(text=paste( "m0 =survival::coxph(survival::Surv(Survival, Censor==1) ~ pc1",paste("+",NameProg[1:nPrgFac],sep="",collapse =""),",data=cdata)" ,sep="")))
    } else {

      stop(" Argument 'Prognostic' is NOT a data frame ")
    }

  }
  Riskscores = Riskgroup = NULL

  #risk Score
  TrtandPC1=summary(m0)[[7]][c("pc1"), 1]
  p1 = TrtandPC1*pc1
  TempRes= EstimateHR(Risk.Scores = p1, Data.Survival = cdata, Prognostic = Prognostic,
                       Plots = TRUE, Mean = TRUE, Quantile = 0.5)

  gg = data.frame(Riskscores = p1,Riskgroup = TempRes$Riskgroup,pc1 = pc1)
  ab = ggplot2::ggplot(gg, ggplot2::aes(x=Riskscores, y=pc1, shape=Riskgroup, color=Riskgroup)) + ggplot2::geom_point()

  tempp=list(SurvFit=TempRes$SurvResult,Riskscores = p1, Riskgroup=TempRes$Riskgroup,pc1=pc1)
  class(tempp)="SurvPls"

  temp=list(SurvFit=TempRes$SurvResult,Riskscores = p1, Riskgroup=TempRes$Riskgroup,pc1=pc1, KMplot = TempRes$KMplot, SurvBPlot = TempRes$SurvBPlot, RiskPls = ab)
  class(temp)="SurvPls"

  if (Plots){
    return(temp)
  } else{
    return(tempp)
  }
}