R/CIBERSORT.R

In changwn/DeconvoLib: Comprehensive Tissue Deconvolution Method Library

#
# CIBERSORT FUNCTION
# https://github.com/singha53/amritr/blob/master/R/supportFunc_cibersort.R#L43
# version 0.1 : similar to above website, and excute successful without bug.
# The result is same with CIBERSORT website.

#===========================
# function 1

CoreAlg <- function(X, y){

  #try different values of nu
  svn_itor <- 3

  res <- function(i){
    if(i==1){nus <- 0.25}
    if(i==2){nus <- 0.5}
    if(i==3){nus <- 0.75}
    model<-svm(X,y,type="nu-regression",kernel="linear",nu=nus,scale=F)
    model
  }

  if(Sys.info()['sysname'] == 'Windows') out <- mclapply(1:svn_itor, res, mc.cores=1) else
    out <- mclapply(1:svn_itor, res, mc.cores=svn_itor)

  nusvm <- rep(0,svn_itor)
  corrv <- rep(0,svn_itor)

  #do cibersort
  t <- 1
  while(t <= svn_itor) {
    weights = t(out[[t]]$coefs) %*% out[[t]]$SV
    weights[which(weights<0)]<-0
    sum_weights = sum(weights)
    if(sum(weights)==0){
        sum_weights <- 1
    }
    w <- weights/sum_weights
    #w<-weights/sum(weights)
    u <- sweep(X,MARGIN=2,w,'*')
    k <- apply(u, 1, sum)
    nusvm[t] <- sqrt((mean((k - y)^2)))
    corrv[t] <- cor(k, y)
    t <- t + 1
  }

  #pick best model
  rmses <- nusvm
  mn <- which.min(rmses)
  model <- out[[mn]]

  #get and normalize coefficients
  q <- t(model$coefs) %*% model$SV
  q[which(q<0)]<-0  ###???
  w <- (q/sum(q))

  mix_rmse <- rmses[mn]
  mix_r <- corrv[mn]

  newList <- list("w" = w, "mix_rmse" = mix_rmse, "mix_r" = mix_r)

}

#=========================
# function 2

CIBERSORT <- function(sig_matrix, mixture_file, perm=0, QN=F){
  library(e1071)
  library(parallel)
  library(preprocessCore)

  #read in data
  #X <- read.table(sig_matrix,header=T,sep="\t",row.names=1,check.names=F)
  #Y <- read.table(mixture_file, header=T, sep="\t", row.names=1,check.names=F)

  #X <- data.matrix(X)
  #Y <- data.matrix(Y)
  X <- data.matrix(sig_matrix)
  Y <- data.matrix(mixture_file)

  #order
  X <- X[order(rownames(X)),]
  Y <- Y[order(rownames(Y)),]

  P <- perm #number of permutations

  #anti-log if max < 50 in mixture file
  if(max(Y) < 50) {Y <- 2^Y}

  #quantile normalization of mixture file
  if(QN == TRUE){
    tmpc <- colnames(Y)
    tmpr <- rownames(Y)
    Y <- normalize.quantiles(Y)
    colnames(Y) <- tmpc
    rownames(Y) <- tmpr
  }

  #intersect genes
  Xgns <- row.names(X)
  Ygns <- row.names(Y)
  YintX <- Ygns %in% Xgns
  Y <- Y[YintX,]
  XintY <- Xgns %in% row.names(Y)
  X <- X[XintY,]

  #standardize sig matrix
  X <- (X - mean(X)) / sd(as.vector(X))

  #empirical null distribution of correlation coefficients
  if(P > 0) {nulldist <- sort(doPerm(P, X, Y)$dist)}

  #print(nulldist)

  header <- c('Mixture',colnames(X),"P-value","Correlation","RMSE")
  #print(header)

  output <- matrix()
  itor <- 1
  mixtures <- dim(Y)[2]
  print(paste("number of iteration:",mixtures,sep=""))
  pval <- 9999

  st <- 0
  #iterate through mixtures
  while(itor <= mixtures){
    
    #show progress bar!
    ss<-"%"
    if((itor/mixtures)*100>st)
    {
      print(paste("Complete: ",st,ss,sep=""))
      st<-st+10
    }
   
    #print(paste("Complete: 100",ss,sep=""))

    #print(itor)
    y <- Y[,itor]


    #standardize mixture
    y <- (y - mean(y)) / sd(y)

    #run SVR core algorithm
    result <- CoreAlg(X, y)

    #get results
    w <- result$w
    mix_r <- result$mix_r
    mix_rmse <- result$mix_rmse

    #calculate p-value
    if(P > 0) {pval <- 1 - (which.min(abs(nulldist - mix_r)) / length(nulldist))}

    #print output
    out <- c(colnames(Y)[itor],w,pval,mix_r,mix_rmse)
    if(itor == 1) {output <- out}
    else {output <- rbind(output, out)}

    itor <- itor + 1

  }

  #save results
  write.table(rbind(header,output), file="CIBERSORT-Results.txt", sep="\t", row.names=F, col.names=F, quote=F)

  #return matrix object containing all results
  obj <- rbind(header,output)
  obj <- obj[,-1]
  obj <- obj[-1,]
  obj <- matrix(as.numeric(unlist(obj)),nrow=nrow(obj))
  rownames(obj) <- colnames(Y)
  colnames(obj) <- c(colnames(X),"P-value","Correlation","RMSE")
  obj
}

#==========================
# function 3

doPerm <- function(perm, X, Y){
  itor <- 1
  Ylist <- as.list(data.matrix(Y))
  dist <- matrix()

  while(itor <= perm){
    #print(itor)

    #random mixture
    yr <- as.numeric(Ylist[sample(length(Ylist),dim(X)[1])])

    #standardize mixture
    yr <- (yr - mean(yr)) / sd(yr)

    #run CIBERSORT core algorithm
    result <- CoreAlg(X, yr)

    mix_r <- result$mix_r

    #store correlation
    if(itor == 1) {dist <- mix_r}
    else {dist <- rbind(dist, mix_r)}

    itor <- itor + 1
  }
  newList <- list("dist" = dist)
}