Corrmotif Conc Analysis

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Last updated: 2025-03-10

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📌 0.1 Micromolar

📌 Fit Limma Model Functions

## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits

limmafit.default <- function(exprs,groupid,compid) {
  limmafits  <- list()
  compnum    <- nrow(compid)
  genenum    <- nrow(exprs)
  limmat     <- matrix(0,genenum,compnum)
  limmas2    <- rep(0,compnum)
  limmadf    <- rep(0,compnum)
  limmav0    <- rep(0,compnum)
  limmag1num <- rep(0,compnum)
  limmag2num <- rep(0,compnum)

  rownames(limmat)  <- rownames(exprs)
  colnames(limmat)  <- rownames(compid)
  names(limmas2)    <- rownames(compid)
  names(limmadf)    <- rownames(compid)
  names(limmav0)    <- rownames(compid)
  names(limmag1num) <- rownames(compid)
  names(limmag2num) <- rownames(compid)

  for(i in 1:compnum) {
    selid1 <- which(groupid == compid[i,1])
    selid2 <- which(groupid == compid[i,2])
    eset   <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
    g1num  <- length(selid1)
    g2num  <- length(selid2)
    designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
    fit <- lmFit(eset,designmat)
    fit <- eBayes(fit)
    limmat[,i] <- fit$t[,2]
    limmas2[i] <- fit$s2.prior
    limmadf[i] <- fit$df.prior
    limmav0[i] <- fit$var.prior[2]
    limmag1num[i] <- g1num
    limmag2num[i] <- g2num
    limmafits[[i]] <- fit

    # log odds
    # w<-sqrt(1+fit$var.prior[2]/(1/g1num+1/g2num))
    # log(0.99)+dt(fit$t[1,2],g1num+g2num-2+fit$df.prior,log=TRUE)-log(0.01)-dt(fit$t[1,2]/w, g1num+g2num-2+fit$df.prior, log=TRUE)+log(w)
  }
  names(limmafits) <- rownames(compid)
  limmacompnum<-nrow(compid)
  result<-list(t       = limmat,
               v0      = limmav0,
               df0     = limmadf,
               s20     = limmas2,
               g1num   = limmag1num,
               g2num   = limmag2num,
               compnum = limmacompnum,
               fits    = limmafits)
}

limmafit.counts <-
  function (exprs, groupid, compid, norm.factor.method = "TMM", voom.normalize.method = "none")
  {
    limmafits  <- list()
    compnum    <- nrow(compid)
    genenum    <- nrow(exprs)
    limmat     <- matrix(NA,genenum,compnum)
    limmas2    <- rep(0,compnum)
    limmadf    <- rep(0,compnum)
    limmav0    <- rep(0,compnum)
    limmag1num <- rep(0,compnum)
    limmag2num <- rep(0,compnum)

    rownames(limmat)  <- rownames(exprs)
    colnames(limmat)  <- rownames(compid)
    names(limmas2)    <- rownames(compid)
    names(limmadf)    <- rownames(compid)
    names(limmav0)    <- rownames(compid)
    names(limmag1num) <- rownames(compid)
    names(limmag2num) <- rownames(compid)

    for (i in 1:compnum) {
      message(paste("Running limma for comparision",i,"/",compnum))
      selid1 <- which(groupid == compid[i, 1])
      selid2 <- which(groupid == compid[i, 2])
      # make a new count data frame
      counts <- cbind(exprs[, selid1], exprs[, selid2])

      # remove NAs
      not.nas <- which(apply(counts, 1, function(x) !any(is.na(x))) == TRUE)

      # runn voom/limma
      d <- DGEList(counts[not.nas,])
      d <- calcNormFactors(d, method = norm.factor.method)
      g1num <- length(selid1)
      g2num <- length(selid2)
      designmat <- cbind(base = rep(1, (g1num + g2num)), delta = c(rep(0,
                                                                       g1num), rep(1, g2num)))

      y <- voom(d, designmat, normalize.method = voom.normalize.method)
      fit <- lmFit(y, designmat)
      fit <- eBayes(fit)

      limmafits[[i]] <- fit
      limmat[not.nas, i] <- fit$t[, 2]
      limmas2[i] <- fit$s2.prior
      limmadf[i] <- fit$df.prior
      limmav0[i] <- fit$var.prior[2]
      limmag1num[i] <- g1num
      limmag2num[i] <- g2num
    }
    limmacompnum <- nrow(compid)
    names(limmafits) <- rownames(compid)
    result <- list(t       = limmat,
                   v0      = limmav0,
                   df0     = limmadf,
                   s20     = limmas2,
                   g1num   = limmag1num,
                   g2num   = limmag2num,
                   compnum = limmacompnum,
                   fits    = limmafits)
  }

limmafit.list <-
  function (fitlist, cmp.idx=2)
  {
    compnum    <- length(fitlist)

    genes <- c()
    for (i in 1:compnum) genes <- unique(c(genes, rownames(fitlist[[i]])))

    genenum    <- length(genes)
    limmat     <- matrix(NA,genenum,compnum)
    limmas2    <- rep(0,compnum)
    limmadf    <- rep(0,compnum)
    limmav0    <- rep(0,compnum)
    limmag1num <- rep(0,compnum)
    limmag2num <- rep(0,compnum)

    rownames(limmat)  <- genes
    colnames(limmat)  <- names(fitlist)
    names(limmas2)    <- names(fitlist)
    names(limmadf)    <- names(fitlist)
    names(limmav0)    <- names(fitlist)
    names(limmag1num) <- names(fitlist)
    names(limmag2num) <- names(fitlist)

    for (i in 1:compnum) {
      this.t <- fitlist[[i]]$t[,cmp.idx]
      limmat[names(this.t),i] <- this.t

      limmas2[i]    <- fitlist[[i]]$s2.prior
      limmadf[i]    <- fitlist[[i]]$df.prior
      limmav0[i]    <- fitlist[[i]]$var.prior[cmp.idx]
      limmag1num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==0)
      limmag2num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==1)
    }

    limmacompnum <- compnum
    result <- list(t       = limmat,
                   v0      = limmav0,
                   df0     = limmadf,
                   s20     = limmas2,
                   g1num   = limmag1num,
                   g2num   = limmag2num,
                   compnum = limmacompnum,
                   fits    = limmafits)

  }

## Rank genes based on statistics
generank<-function(x) {
  xcol<-ncol(x)
  xrow<-nrow(x)
  result<-matrix(0,xrow,xcol)
  z<-(1:1:xrow)
  for(i in 1:xcol) {
    y<-sort(x[,i],decreasing=TRUE,na.last=TRUE)
    result[,i]<-match(x[,i],y)
    result[,i]<-order(result[,i])
  }
  result
}

## Log-likelihood for moderated t under H0
modt.f0.loglike<-function(x,df) {
  a<-dt(x, df, log=TRUE)
  result<-as.vector(a)
  flag<-which(is.na(result)==TRUE)
  result[flag]<-0
  result
}

## Log-likelihood for moderated t under H1
## param=c(df,g1num,g2num,v0)
modt.f1.loglike<-function(x,param) {
  df<-param[1]
  g1num<-param[2]
  g2num<-param[3]
  v0<-param[4]
  w<-sqrt(1+v0/(1/g1num+1/g2num))
  dt(x/w, df, log=TRUE)-log(w)
  a<-dt(x/w, df, log=TRUE)-log(w)
  result<-as.vector(a)
  flag<-which(is.na(result)==TRUE)
  result[flag]<-0
  result
}

## Correlation Motif Fit
cmfit.X<-function(x, type, K=1, tol=1e-3, max.iter=100) {
  ## initialize
  xrow <- nrow(x)
  xcol <- ncol(x)
  loglike0 <- list()
  loglike1 <- list()
  p <- rep(1, K)/K
  q <- matrix(runif(K * xcol), K, xcol)
  q[1, ] <- rep(0.01, xcol)
  for (i in 1:xcol) {
    f0 <- type[[i]][[1]]
    f0param <- type[[i]][[2]]
    f1 <- type[[i]][[3]]
    f1param <- type[[i]][[4]]
    loglike0[[i]] <- f0(x[, i], f0param)
    loglike1[[i]] <- f1(x[, i], f1param)
  }
  condlike <- list()
  for (i in 1:xcol) {
    condlike[[i]] <- matrix(0, xrow, K)
  }
  loglike.old <- -1e+10
  for (i.iter in 1:max.iter) {
    if ((i.iter%%50) == 0) {
      print(paste("We have run the first ", i.iter, " iterations for K=",
                  K, sep = ""))
    }
    err <- tol + 1
    clustlike <- matrix(0, xrow, K)
    #templike <- matrix(0, xrow, 2)
    templike1 <- rep(0, xrow)
    templike2 <- rep(0, xrow)
    for (j in 1:K) {
      for (i in 1:xcol) {
        templike1 <- log(q[j, i]) + loglike1[[i]]
        templike2 <- log(1 - q[j, i]) + loglike0[[i]]
        tempmax <- Rfast::Pmax(templike1, templike2)

        templike1 <- exp(templike1 - tempmax)
        templike2 <- exp(templike2 - tempmax)

        tempsum <- templike1 + templike2
        clustlike[, j] <- clustlike[, j] + tempmax +
          log(tempsum)
        condlike[[i]][, j] <- templike1/tempsum
      }
      clustlike[, j] <- clustlike[, j] + log(p[j])
    }
    #tempmax <- apply(clustlike, 1, max)
    tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
    for (j in 1:K) {
      clustlike[, j] <- exp(clustlike[, j] - tempmax)
    }
    #tempsum <- apply(clustlike, 1, sum)
    tempsum <- Rfast::rowsums(clustlike)
    for (j in 1:K) {
      clustlike[, j] <- clustlike[, j]/tempsum
    }
    #p.new <- (apply(clustlike, 2, sum) + 1)/(xrow + K)
    p.new <- (Rfast::colsums(clustlike) + 1)/(xrow + K)
    q.new <- matrix(0, K, xcol)
    for (j in 1:K) {
      clustpsum <- sum(clustlike[, j])
      for (i in 1:xcol) {
        q.new[j, i] <- (sum(clustlike[, j] * condlike[[i]][,
                                                           j]) + 1)/(clustpsum + 2)
      }
    }
    err.p <- max(abs(p.new - p)/p)
    err.q <- max(abs(q.new - q)/q)
    err <- max(err.p, err.q)
    loglike.new <- (sum(tempmax + log(tempsum)) + sum(log(p.new)) +
                      sum(log(q.new) + log(1 - q.new)))/xrow
    p <- p.new
    q <- q.new
    loglike.old <- loglike.new
    if (err < tol) {
      break
    }
  }
  clustlike <- matrix(0, xrow, K)
  for (j in 1:K) {
    for (i in 1:xcol) {
      templike1 <- log(q[j, i]) + loglike1[[i]]
      templike2 <- log(1 - q[j, i]) + loglike0[[i]]
      tempmax <- Rfast::Pmax(templike1, templike2)

      templike1 <- exp(templike1 - tempmax)
      templike2 <- exp(templike2 - tempmax)

      tempsum <- templike1 + templike2
      clustlike[, j] <- clustlike[, j] + tempmax + log(tempsum)
      condlike[[i]][, j] <- templike1/tempsum
    }
    clustlike[, j] <- clustlike[, j] + log(p[j])
  }
  #tempmax <- apply(clustlike, 1, max)
  tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
  for (j in 1:K) {
    clustlike[, j] <- exp(clustlike[, j] - tempmax)
  }
  #tempsum <- apply(clustlike, 1, sum)
  tempsum <- Rfast::rowsums(clustlike)
  for (j in 1:K) {
    clustlike[, j] <- clustlike[, j]/tempsum
  }
  p.post <- matrix(0, xrow, xcol)
  for (j in 1:K) {
    for (i in 1:xcol) {
      p.post[, i] <- p.post[, i] + clustlike[, j] * condlike[[i]][,
                                                                  j]
    }
  }
  loglike.old <- loglike.old - (sum(log(p)) + sum(log(q) +
                                                    log(1 - q)))/xrow
  loglike.old <- loglike.old * xrow
  result <- list(p.post = p.post, motif.prior = p, motif.q = q,
                 loglike = loglike.old, clustlike=clustlike, condlike=condlike)
}

## Fit using (0,0,...,0) and (1,1,...,1)
cmfitall<-function(x, type, tol=1e-3, max.iter=100) {
  ## initialize
  xrow<-nrow(x)
  xcol<-ncol(x)
  loglike0<-list()
  loglike1<-list()
  p<-0.01

  ## compute loglikelihood
  L0<-matrix(0,xrow,1)
  L1<-matrix(0,xrow,1)
  for(i in 1:xcol) {
    f0<-type[[i]][[1]]
    f0param<-type[[i]][[2]]
    f1<-type[[i]][[3]]
    f1param<-type[[i]][[4]]
    loglike0[[i]]<-f0(x[,i],f0param)
    loglike1[[i]]<-f1(x[,i],f1param)
    L0<-L0+loglike0[[i]]
    L1<-L1+loglike1[[i]]
  }


  ## EM algorithm to get MLE of p and q
  loglike.old <- -1e10
  for(i.iter in 1:max.iter) {
    if((i.iter%%50) == 0) {
      print(paste("We have run the first ", i.iter, " iterations",sep=""))
    }
    err<-tol+1

    ## compute posterior cluster membership
    clustlike<-matrix(0,xrow,2)
    clustlike[,1]<-log(1-p)+L0
    clustlike[,2]<-log(p)+L1

    tempmax<-apply(clustlike,1,max)
    for(j in 1:2) {
      clustlike[,j]<-exp(clustlike[,j]-tempmax)
    }
    tempsum<-apply(clustlike,1,sum)

    ## update motif occurrence rate
    for(j in 1:2) {
      clustlike[,j]<-clustlike[,j]/tempsum
    }

    p.new<-(sum(clustlike[,2])+1)/(xrow+2)

    ## evaluate convergence
    err<-abs(p.new-p)/p

    ## evaluate whether the log.likelihood increases
    loglike.new<-(sum(tempmax+log(tempsum))+log(p.new)+log(1-p.new))/xrow

    loglike.old<-loglike.new
    p<-p.new

    if(err<tol) {
      break;
    }
  }

  ## compute posterior p
  clustlike<-matrix(0,xrow,2)
  clustlike[,1]<-log(1-p)+L0
  clustlike[,2]<-log(p)+L1

  tempmax<-apply(clustlike,1,max)
  for(j in 1:2) {
    clustlike[,j]<-exp(clustlike[,j]-tempmax)
  }
  tempsum<-apply(clustlike,1,sum)

  for(j in 1:2) {
    clustlike[,j]<-clustlike[,j]/tempsum
  }

  p.post<-matrix(0,xrow,xcol)
  for(i in 1:xcol) {
    p.post[,i]<-clustlike[,2]
  }

  ## return

  #calculate back loglikelihood
  loglike.old<-loglike.old-(log(p)+log(1-p))/xrow
  loglike.old<-loglike.old*xrow
  result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}

## Fit each dataset separately
cmfitsep<-function(x, type, tol=1e-3, max.iter=100) {
  ## initialize
  xrow<-nrow(x)
  xcol<-ncol(x)
  loglike0<-list()
  loglike1<-list()
  p<-0.01*rep(1,xcol)
  loglike.final<-rep(0,xcol)

  ## compute loglikelihood
  for(i in 1:xcol) {
    f0<-type[[i]][[1]]
    f0param<-type[[i]][[2]]
    f1<-type[[i]][[3]]
    f1param<-type[[i]][[4]]
    loglike0[[i]]<-f0(x[,i],f0param)
    loglike1[[i]]<-f1(x[,i],f1param)
  }

  p.post<-matrix(0,xrow,xcol)

  ## EM algorithm to get MLE of p
  for(coli in 1:xcol) {
    loglike.old <- -1e10
    for(i.iter in 1:max.iter) {
      if((i.iter%%50) == 0) {
        print(paste("We have run the first ", i.iter, " iterations",sep=""))
      }
      err<-tol+1

      ## compute posterior cluster membership
      clustlike<-matrix(0,xrow,2)
      clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
      clustlike[,2]<-log(p[coli])+loglike1[[coli]]

      tempmax<-apply(clustlike,1,max)
      for(j in 1:2) {
        clustlike[,j]<-exp(clustlike[,j]-tempmax)
      }
      tempsum<-apply(clustlike,1,sum)

      ## evaluate whether the log.likelihood increases
      loglike.new<-sum(tempmax+log(tempsum))/xrow

      ## update motif occurrence rate
      for(j in 1:2) {
        clustlike[,j]<-clustlike[,j]/tempsum
      }

      p.new<-(sum(clustlike[,2]))/(xrow)

      ## evaluate convergence
      err<-abs(p.new-p[coli])/p[coli]
      loglike.old<-loglike.new
      p[coli]<-p.new

      if(err<tol) {
        break;
      }
    }

    ## compute posterior p
    clustlike<-matrix(0,xrow,2)
    clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
    clustlike[,2]<-log(p[coli])+loglike1[[coli]]

    tempmax<-apply(clustlike,1,max)
    for(j in 1:2) {
      clustlike[,j]<-exp(clustlike[,j]-tempmax)
    }
    tempsum<-apply(clustlike,1,sum)

    for(j in 1:2) {
      clustlike[,j]<-clustlike[,j]/tempsum
    }

    p.post[,coli]<-clustlike[,2]
    loglike.final[coli]<-loglike.old
  }


  ## return
  loglike.final<-loglike.final*xrow
  result<-list(p.post=p.post, motif.prior=p, loglike=loglike.final)
}

## Fit the full model
cmfitfull<-function(x, type, tol=1e-3, max.iter=100) {
  ## initialize
  xrow<-nrow(x)
  xcol<-ncol(x)
  loglike0<-list()
  loglike1<-list()
  K<-2^xcol
  p<-rep(1,K)/K
  pattern<-rep(0,xcol)
  patid<-matrix(0,K,xcol)

  ## compute loglikelihood
  for(i in 1:xcol) {
    f0<-type[[i]][[1]]
    f0param<-type[[i]][[2]]
    f1<-type[[i]][[3]]
    f1param<-type[[i]][[4]]
    loglike0[[i]]<-f0(x[,i],f0param)
    loglike1[[i]]<-f1(x[,i],f1param)
  }
  L<-matrix(0,xrow,K)
  for(i in 1:K)
  {
    patid[i,]<-pattern
    for(j in 1:xcol) {
      if(pattern[j] < 0.5) {
        L[,i]<-L[,i]+loglike0[[j]]
      } else {
        L[,i]<-L[,i]+loglike1[[j]]
      }
    }

    if(i < K) {
      pattern[xcol]<-pattern[xcol]+1
      j<-xcol
      while(pattern[j] > 1) {
        pattern[j]<-0
        j<-j-1
        pattern[j]<-pattern[j]+1
      }
    }
  }

  ## EM algorithm to get MLE of p and q
  loglike.old <- -1e10
  for(i.iter in 1:max.iter) {
    if((i.iter%%50) == 0) {
      print(paste("We have run the first ", i.iter, " iterations",sep=""))
    }
    err<-tol+1

    ## compute posterior cluster membership
    clustlike<-matrix(0,xrow,K)
    for(j in 1:K) {
      clustlike[,j]<-log(p[j])+L[,j]
    }

    tempmax<-apply(clustlike,1,max)
    for(j in 1:K) {
      clustlike[,j]<-exp(clustlike[,j]-tempmax)
    }
    tempsum<-apply(clustlike,1,sum)

    ## update motif occurrence rate
    for(j in 1:K) {
      clustlike[,j]<-clustlike[,j]/tempsum
    }

    p.new<-(apply(clustlike,2,sum)+1)/(xrow+K)

    ## evaluate convergence
    err<-max(abs(p.new-p)/p)

    ## evaluate whether the log.likelihood increases
    loglike.new<-(sum(tempmax+log(tempsum))+sum(log(p.new)))/xrow

    loglike.old<-loglike.new
    p<-p.new

    if(err<tol) {
      break;
    }
  }

  ## compute posterior p
  clustlike<-matrix(0,xrow,K)
  for(j in 1:K) {
    clustlike[,j]<-log(p[j])+L[,j]
  }

  tempmax<-apply(clustlike,1,max)
  for(j in 1:K) {
    clustlike[,j]<-exp(clustlike[,j]-tempmax)
  }
  tempsum<-apply(clustlike,1,sum)

  for(j in 1:K) {
    clustlike[,j]<-clustlike[,j]/tempsum
  }

  p.post<-matrix(0,xrow,xcol)
  for(j in 1:K) {
    for(i in 1:xcol) {
      if(patid[j,i] > 0.5) {
        p.post[,i]<-p.post[,i]+clustlike[,j]
      }
    }
  }

  ## return
  #calculate back loglikelihood
  loglike.old<-loglike.old-sum(log(p))/xrow
  loglike.old<-loglike.old*xrow
  result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}

generatetype<-function(limfitted)
{
  jtype<-list()
  df<-limfitted$g1num+limfitted$g2num-2+limfitted$df0
  for(j in 1:limfitted$compnum)
  {
    jtype[[j]]<-list(f0=modt.f0.loglike, f0.param=df[j], f1=modt.f1.loglike, f1.param=c(df[j],limfitted$g1num[j],limfitted$g2num[j],limfitted$v0[j]))
  }
  jtype
}

cormotiffit <- function(exprs, groupid=NULL, compid=NULL, K=1, tol=1e-3,
                        max.iter=100, BIC=TRUE, norm.factor.method="TMM",
                        voom.normalize.method = "none", runtype=c("logCPM","counts","limmafits"), each=3)
{
  # first I want to do some typechecking. Input can be either a normalized
  # matrix, a count matrix, or a list of limma fits. Dispatch the correct
  # limmafit accordingly.
  # todo: add some typechecking here
  limfitted <- list()
  if (runtype=="counts") {
    limfitted <- limmafit.counts(exprs,groupid,compid, norm.factor.method, voom.normalize.method)
  } else if (runtype=="logCPM") {
    limfitted <- limmafit.default(exprs,groupid,compid)
  } else if (runtype=="limmafits") {
    limfitted <- limmafit.list(exprs)
  } else {
    stop("runtype must be one of 'logCPM', 'counts', or 'limmafits'")
  }


  jtype<-generatetype(limfitted)
  fitresult<-list()
  ks <- rep(K, each = each)
  fitresult <- bplapply(1:length(ks), function(i, x, type, ks, tol, max.iter) {
    cmfit.X(x, type, K = ks[i], tol = tol, max.iter = max.iter)
  }, x=limfitted$t, type=jtype, ks=ks, tol=tol, max.iter=max.iter)

  best.fitresults <- list()
  for (i in 1:length(K)) {
    w.k <- which(ks==K[i])
    this.bic <- c()
    for (j in w.k) this.bic[j] <- -2 * fitresult[[j]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
    w.min <- which(this.bic == min(this.bic, na.rm = TRUE))[1]
    best.fitresults[[i]] <- fitresult[[w.min]]
  }
  fitresult <- best.fitresults

  bic <- rep(0, length(K))
  aic <- rep(0, length(K))
  loglike <- rep(0, length(K))
  for (i in 1:length(K)) loglike[i] <- fitresult[[i]]$loglike
  for (i in 1:length(K)) bic[i] <- -2 * fitresult[[i]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
  for (i in 1:length(K)) aic[i] <- -2 * fitresult[[i]]$loglike + 2 * (K[i] - 1 + K[i] * limfitted$compnum)
  if(BIC==TRUE) {
    bestflag=which(bic==min(bic))
  }
  else {
    bestflag=which(aic==min(aic))
  }
  result<-list(bestmotif=fitresult[[bestflag]],bic=cbind(K,bic),
               aic=cbind(K,aic),loglike=cbind(K,loglike), allmotifs=fitresult)

}

cormotiffitall<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
  limfitted<-limmafit(exprs,groupid,compid)
  jtype<-generatetype(limfitted)
  fitresult<-cmfitall(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}

cormotiffitsep<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
  limfitted<-limmafit(exprs,groupid,compid)
  jtype<-generatetype(limfitted)
  fitresult<-cmfitsep(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}

cormotiffitfull<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
  limfitted<-limmafit(exprs,groupid,compid)
  jtype<-generatetype(limfitted)
  fitresult<-cmfitfull(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}

plotIC<-function(fitted_cormotif)
{
  oldpar<-par(mfrow=c(1,2))
  plot(fitted_cormotif$bic[,1], fitted_cormotif$bic[,2], type="b",xlab="Motif Number", ylab="BIC", main="BIC")
  plot(fitted_cormotif$aic[,1], fitted_cormotif$aic[,2], type="b",xlab="Motif Number", ylab="AIC", main="AIC")
}

plotMotif<-function(fitted_cormotif,title="")
{
  layout(matrix(1:2,ncol=2))
  u<-1:dim(fitted_cormotif$bestmotif$motif.q)[2]
  v<-1:dim(fitted_cormotif$bestmotif$motif.q)[1]
  image(u,v,t(fitted_cormotif$bestmotif$motif.q),
        col=gray(seq(from=1,to=0,by=-0.1)),xlab="Study",yaxt = "n",
        ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
  axis(2,at=1:length(v))
  for(i in 1:(length(u)+1))
  {
    abline(v=(i-0.5))
  }
  for(i in 1:(length(v)+1))
  {
    abline(h=(i-0.5))
  }
  Ng=10000
  if(is.null(fitted_cormotif$bestmotif$p.post)!=TRUE)
    Ng=nrow(fitted_cormotif$bestmotif$p.post)
  genecount=floor(fitted_cormotif$bestmotif$motif.p*Ng)
  NK=nrow(fitted_cormotif$bestmotif$motif.q)
  plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
       frame.plot=FALSE,axes=FALSE,xlab="No. of genes",ylab="", main=paste(title,"frequency",sep=" "))
  segments(0,0.7,fitted_cormotif$bestmotif$motif.p[1],0.7)
  rect(0,1:NK-0.3,fitted_cormotif$bestmotif$motif.p,1:NK+0.3,
       col="dark grey")
  mtext(1:NK,at=1:NK,side=2,cex=0.8)
  text(fitted_cormotif$bestmotif$motif.p+0.15,1:NK,
       labels=floor(fitted_cormotif$bestmotif$motif.p*Ng))
}

📌 Load Required Libraries

library(Cormotif)
library(Rfast)
library(dplyr)
library(BiocParallel)
library(gprofiler2)
library(ggplot2)

📌 Corrmotif Model 0.1 Micromolar

📌 Load Corrmotif Data

# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene

# Filter for 0.1 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.1", colnames(Corrmotif_df))])


# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.1", colnames(groupid))])


compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]

📌 Corrmotif Model 0.1 Micromolar (K=1:8)

📌 Fit Corrmotif Model (K=1:8) (0.1 Micromolar)

set.seed(11111)
# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.1 <- cormotiffit(
  exprs = exprs.corrmotif,
  groupid = groupid_df,
  compid = compid_df,
  K = 1:8,
  max.iter = 1000,
  BIC = TRUE,
  runtype = "logCPM"
)

gene_prob_0.1 <- motif.fitted_0.1$bestmotif$p.post
rownames(gene_prob_0.1) <- rownames(Corrmotif_df)
motif_prob_0.1 <- motif.fitted_0.1$bestmotif$clustlike
rownames(motif_prob_0.1) <- rownames(gene_prob_0.1)
write.csv(motif_prob_0.1,"data/cormotif_probability_genelist_0.1.csv")

📌 Plot motif (0.1 Micromolar)

cormotif_0.1 <- readRDS("data/Corrmotif/cormotif_0.1.RDS")

cormotif_0.1$bic

     K      bic
[1,] 1 291696.5
[2,] 2 284585.3
[3,] 3 283482.9
[4,] 4 283551.8
[5,] 5 283620.7
[6,] 6 283689.6
[7,] 7 283758.5
[8,] 8 283827.4

plotIC(cormotif_0.1)

Past versions of Model1-1.png

Version	Author	Date
41cd1be	sayanpaul01	2025-02-27
ce4b325	sayanpaul01	2025-02-25

plotMotif(cormotif_0.1)

Past versions of Model1-2.png

Version	Author	Date
41cd1be	sayanpaul01	2025-02-27
ce4b325	sayanpaul01	2025-02-25

📌 Extract Gene Probabilities (0.1 Micromolar)

# Extract posterior probabilities for genes
gene_prob_tran_0.1 <- cormotif_0.1$bestmotif$p.post

rownames(gene_prob_tran_0.1) <- rownames(Corrmotif_df)

# Define gene probability groups
prob_1_0.1  <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] < 0.5 & gene_prob_tran_0.1[,6]<0.5),])
length(prob_1_0.1)

[1] 12308

prob_2_0.1  <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] >0.5 & gene_prob_tran_0.1[,3] >0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_2_0.1)

[1] 415

prob_3_0.1  <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_3_0.1)

[1] 1551

📌 Distribution of Gene Clusters Identified by Corrmotif (0.1 micromolar)

# Load necessary library
library(ggplot2)

# Data
data <- data.frame(
  Category = c("Non response (0.1 µM)", "CX-DOX mid-late response (0.1 µM)", "DOX only mid-late (0.1 µM)"),
  Value = c(12308, 415, 1551)
)

# Define custom colors
custom_colors <- c("Non response (0.1 µM)" = "#FF9999",
                   "CX-DOX mid-late response (0.1 µM)" = "#66B2FF",
                   "DOX only mid-late (0.1 µM)" = "#99FF99")

# Create pie chart
ggplot(data, aes(x = "", y = Value, fill = Category)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start = 0) +
  geom_text(aes(label = Value),
            position = position_stack(vjust = 0.5),
            size = 4, color = "black") +
  labs(title = "Pie Chart (0.1 micromolar Corrmotif)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)

Past versions of pie-chart_0.1-1.png

Version	Author	Date
2de67e6	sayanpaul01	2025-02-27

📌 Save Corr-motif datasets for Gene Ontology analysis (0.1 Micromolar)

write.csv(data.frame(Entrez_ID = prob_1_0.1), "data/prob_1_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.1), "data/prob_2_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.1), "data/prob_3_0.1.csv", row.names = FALSE)

📌 0.1 Micromolar abs logFC

# Load Required Libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)

# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3  <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")

DOX_0.1_3  <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")

# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
  CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
  CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
  DOX_0.1_3  %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
  DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
  DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
      Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
      Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Compute Absolute logFC
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(absFC = abs(logFC))

# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(
    Drug = factor(Drug, levels = c("CX.5461", "DOX")),
    Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group,
                            levels = c(
                              "DOX only mid-late response\n(0.1 micromolar)",
                              "CX-DOX mid-late response\n(0.1 micromolar)",
                              "Non response\n(0.1 micromolar)"  # Reversed Order
                            ))
  )

# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.1, aes(x = Drug, y = absFC, fill = Drug)) +
  geom_boxplot() +
  scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +  # Custom color palette
  facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) +  # Ensure Proper Wrapping
  theme_bw() +
  labs(
    x = "Drugs",
    y = "|Log Fold Change|",
    title = "|Log Fold| for 0.1 micromolar"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.line = element_line(linewidth = 1.5),
    strip.background = element_rect(fill = "gray"),  # Gray background for facet labels
    strip.text = element_text(size = 12, color = "black", face = "bold"),  # Bold styling for facet labels
    axis.text.x = element_text(size = 10, color = "black", angle = 15)
  )

Past versions of "0.1abs logFC-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.1 Micromolar mean (Abs logFC) across timepoints

# Load Required Libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)

# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3  <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")

DOX_0.1_3  <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")

# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Drug = "CX.5461", Timepoint = "3"),
  CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
  CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
  DOX_0.1_3  %>% mutate(Drug = "DOX", Timepoint = "3"),
  DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
  DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
      Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
      Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
  mutate(abs_logFC = abs(logFC)) %>%  # Take absolute log fold change
  group_by(Response_Group, Drug, Timepoint) %>%
  dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
  as.data.frame()

# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
  Response_Group = unique(all_toptables_0.1$Response_Group),
  Drug = unique(all_toptables_0.1$Drug),
  Timepoint = c("3", "24", "48")
)

# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))

# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0

# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX only mid-late response\n(0.1 micromolar)",
      "CX-DOX mid-late response\n(0.1 micromolar)",
      "Non response\n(0.1 micromolar)"  # Reversed Order
    ))
  )

# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")

# **Plot the Line Plot for Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
  geom_point(size = 3) +
  geom_line(size = 1.2) +
  scale_color_manual(values = drug_palette) +
  ylim(0, 2.5) +  # Adjust the Y-axis for better visualization
  facet_wrap(~ Response_Group, ncol = 1) +  # Facet by Response Group (Reversed Order)
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean |Log Fold Change|",
    title = "Mean |Log Fold Change| Across Response Groups (0.1 micromolar)",
    color = "Drug"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text = element_text(size = 12, color = "black"),
    strip.text = element_text(size = 12, color = "black", face = "bold"),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  )

Past versions of "0.1 mean-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.1 Micromolar logFC

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)

# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3  <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")

DOX_0.1_3  <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")

# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
  CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
  CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
  DOX_0.1_3  %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
  DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
  DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
      Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
      Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX only mid-late response\n(0.1 micromolar)",
      "CX-DOX mid-late response\n(0.1 micromolar)",
      "Non response\n(0.1 micromolar)"  # Reversed Order
    ))
  )

# **Plot the Boxplot**
ggplot(all_toptables_0.1, aes(x = Drug, y = logFC, fill = Drug)) +
  geom_boxplot() +
  scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
  facet_grid(Response_Group ~ Timepoint) +
  theme_bw() +
  labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.1 Micromolar") +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of "0.1 logFC-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.1 Micromolar mean logFC across timepoints

# Compute Mean logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
  group_by(Response_Group, Drug, Timepoint) %>%
  dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
  as.data.frame()

# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX only mid-late response\n(0.1 micromolar)",
      "CX-DOX mid-late response\n(0.1 micromolar)",
      "Non response\n(0.1 micromolar)"  # Reversed Order
    ))
  )

# **Plot the Line Plot**
ggplot(data_summary, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
  geom_point(size = 3) +
  geom_line(size = 1.2) +
  scale_color_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
  ylim(-2, 1.5) +  # Adjust the Y-axis for better visualization
  facet_wrap(~ Response_Group, ncol = 1) +  # Facet by Response Group (Reversed Order)
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean Log Fold Change",
    title = "Mean Log Fold Change Across Response Groups (0.1 micromolar)",
    color = "Drug"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text = element_text(size = 12, color = "black"),
    strip.text = element_text(size = 12, color = "black", face = "bold"),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  )

Past versions of "0.1 mean2-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 Corrmotif Model 0.5 Micromolar

📌 Load Corrmotif Data

# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene

# Filter for 0.5 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.5", colnames(Corrmotif_df))])


# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.5", colnames(groupid))])


compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]

📌 Corrmotif Model 0.5 Micromolar (K=1:8)

📌 Fit Corrmotif Model (K=1:8) (0.5 Micromolar)

# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.5 <- cormotiffit(
  exprs = exprs.corrmotif,
  groupid = groupid_df,
  compid = compid_df,
  K = 1:8,
  max.iter = 1000,
  BIC = TRUE,
  runtype = "logCPM"
)

gene_prob_0.5 <- motif.fitted_0.5$bestmotif$p.post
rownames(gene_prob_0.5) <- rownames(Corrmotif_df)
motif_prob_0.5 <- motif.fitted_0.5$bestmotif$clustlike
rownames(motif_prob_0.5) <- rownames(gene_prob_0.5)
write.csv(motif_prob_0.5,"data/cormotif_probability_genelist_0.5.csv")

📌 Plot motif (0.5 Micromolar)

cormotif_0.5 <- readRDS("data/Corrmotif/cormotif_0.5.RDS")

cormotif_0.5$bic

     K      bic
[1,] 1 352140.7
[2,] 2 346785.8
[3,] 3 344812.9
[4,] 4 344860.1
[5,] 5 344751.9
[6,] 6 344820.8
[7,] 7 344889.7
[8,] 8 344966.6

plotIC(cormotif_0.5)

Past versions of Motif_0.5-1.png

Version	Author	Date
41cd1be	sayanpaul01	2025-02-27

plotMotif(cormotif_0.5)

Past versions of Motif_0.5-2.png

Version	Author	Date
41cd1be	sayanpaul01	2025-02-27

📌 Extract Gene Probabilities (0.5 Micromolar)

# Extract posterior probabilities for genes
gene_prob_tran_0.5 <- cormotif_0.5$bestmotif$p.post

rownames(gene_prob_tran_0.5) <- rownames(Corrmotif_df)

# Define gene probability groups
prob_1_0.5  <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6]<0.5),])
length(prob_1_0.5)

[1] 7134

prob_2_0.5  <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] >0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>=0.02),])
length(prob_2_0.5)

[1] 179

prob_3_0.5  <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_3_0.5)

[1] 6450

prob_4_0.5  <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] >= 0.1 & gene_prob_tran_0.5[,2] > 0.5 & gene_prob_tran_0.5[,3] > 0.5 & gene_prob_tran_0.5[,4] >= 0.02 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6] < 0.5),]) 

length(prob_4_0.5)

[1] 142

prob_5_0.5  <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] >0.5 & gene_prob_tran_0.5[,3] >0.5 & gene_prob_tran_0.5[,4] >=0.02 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_5_0.5)

[1] 221

📌 Distribution of Gene Clusters Identified by Corrmotif (0.5 micromolar)

# Load necessary library
library(ggplot2)

# Data
data <- data.frame(
  Category = c("Non response (0.5)", "DOX-specific response (0.5 µM)", "DOX only mid-late response (0.5 µM)", "CX + DOX (early) response (0.5 µM)", "DOX + CX (mid-late) response (0.5 µM)"),
  Value = c(7134,179,6450,142,221)
)

# Add values to category names (to be displayed in the legend)
data$Category <- paste0(data$Category, " (", data$Value, ")")

# Define custom colors with updated category names
custom_colors <- setNames(
  c("#FF9999", "#FF66CC", "#66B2FF", "#99FF99", "#FFD700"),
  data$Category  # Ensures color names match updated categories
)

# Create pie chart without number labels inside
ggplot(data, aes(x = "", y = Value, fill = Category)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start = 0) +
  labs(title = "Pie Chart (0.5 micromolar Corrmotif)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)

Past versions of pie-chart_0.5-1.png

Version	Author	Date
d9ff853	sayanpaul01	2025-03-03
2de67e6	sayanpaul01	2025-02-27

📌 Combined pie charts for concentrations

# Load necessary libraries
library(ggplot2)
library(dplyr)

# Data for 0.1 µM
data_0.1 <- data.frame(
  Category = c("Non response", "CX-DOX mid-late response", "DOX only mid-late"),
  Value = c(12308, 415, 1551),
  Concentration = "0.1 µM"
)

# Data for 0.5 µM
data_0.5 <- data.frame(
  Category = c("Non response", "DOX specific response", "DOX only mid-late response", 
               "CX + DOX (early) response", "DOX + CX (mid-late) response"),
  Value = c(7134, 179, 6450, 142, 221),
  Concentration = "0.5 µM"
)

# Combine both datasets
combined_data <- bind_rows(data_0.1, data_0.5)

# Add values to category names (for legend only)
combined_data$Category_Legend <- paste0(combined_data$Category, " (", combined_data$Value, ")")

# Define custom colors for updated categories
custom_colors <- c(
  "Non response (12308)" = "#FF9999",
  "CX-DOX mid-late response (415)" = "#66B2FF",
  "DOX only mid-late (1551)" = "#99FF99",
  "Non response (7134)" = "#FF9999",
  "DOX specific response (179)" = "#FF66CC",
  "DOX only mid-late response (6450)" = "#99FF99",
  "CX + DOX (early) response (142)" = "#FFD700",
  "DOX + CX (mid-late) response (221)" = "#8A2BE2"
)

# Ensure categories appear in a specific order
combined_data$Category_Legend <- factor(combined_data$Category_Legend, levels = names(custom_colors))

# Create faceted pie charts without numbers inside the slices
pie_chart <- ggplot(combined_data, aes(x = "", y = Value, fill = Category_Legend)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start = 0) +
  facet_wrap(~ Concentration) +  # Facet by concentration (side-by-side)
  labs(title = "Corrmotif Pie Charts for 0.1 µM and 0.5 µM", x = NULL, y = NULL, fill = "Category") +
  theme_void() +
  theme(
    panel.border = element_rect(color = "black", fill = NA, linewidth = 1.5),  # Adds box around facets
    strip.background = element_rect(fill = "white", color = "black", linewidth = 1),  # Box for facet titles
    strip.text = element_text(size = 12, face = "bold", color = "black"),
    legend.title = element_text(size = 12, face = "bold"),  # Bold legend title
    legend.text = element_text(size = 10)  # Adjust legend text size
  ) +
  scale_fill_manual(values = custom_colors)

# Display the plot
print(pie_chart)

Past versions of pie-chart_comb-1.png

Version	Author	Date
ef3a951	sayanpaul01	2025-03-09

📌 Save Corr-motif datasets for Gene Ontology analysis (0.5 Micromolar)

write.csv(data.frame(Entrez_ID = prob_1_0.5), "data/prob_1_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.5), "data/prob_2_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.5), "data/prob_3_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_4_0.5), "data/prob_4_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_5_0.5), "data/prob_5_0.5.csv", row.names = FALSE)

📌 0.5 Micromolar abs logFC

# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)

# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3  <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")

DOX_0.5_3  <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")

# Convert datasets to DataFrames
Toptable_CX_0.5_3_df  <- data.frame(CX_0.5_3)
Toptable_CX_0.5_24_df <- data.frame(CX_0.5_24)
Toptable_CX_0.5_48_df <- data.frame(CX_0.5_48)

Toptable_DOX_0.5_3_df  <- data.frame(DOX_0.5_3)
Toptable_DOX_0.5_24_df <- data.frame(DOX_0.5_24)
Toptable_DOX_0.5_48_df <- data.frame(DOX_0.5_48)

# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
  Toptable_CX_0.5_3_df  %>% mutate(Drug = "CX.5461", Timepoint = "3"),
  Toptable_CX_0.5_24_df %>% mutate(Drug = "CX.5461", Timepoint = "24"),
  Toptable_CX_0.5_48_df %>% mutate(Drug = "CX.5461", Timepoint = "48"),
  Toptable_DOX_0.5_3_df  %>% mutate(Drug = "DOX", Timepoint = "3"),
  Toptable_DOX_0.5_24_df %>% mutate(Drug = "DOX", Timepoint = "24"),
  Toptable_DOX_0.5_48_df %>% mutate(Drug = "DOX", Timepoint = "48")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
      Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
      Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
      Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
      Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
      TRUE ~ NA_character_
    )
  )

# Remove NA Values (Genes Not in Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>% filter(!is.na(Response_Group))

# Compute Absolute logFC
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(absFC = abs(logFC))

# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Drug = factor(Drug, levels = c("CX.5461", "DOX")),
    Timepoint = factor(Timepoint, levels = c("3", "24", "48"),
                       labels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group,
                            levels = c("DOX + CX (mid-late) response\n(0.5 micromolar)",
                                       "CX + DOX (early) response\n(0.5 micromolar)",
                                       "DOX only mid-late response\n(0.5 micromolar)",
                                       "DOX-specific response\n(0.5 micromolar)",
                                       "Non response\n(0.5 micromolar)")) # Reversed Order
  )

# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.5, aes(x = Drug, y = absFC, fill = Drug)) +
  geom_boxplot() +
  scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +  # Custom color palette
  facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) +  # Ensure Proper Wrapping
  theme_bw() +
  labs(
    x = "Drugs",
    y = "|Log Fold Change|",
    title = "|Log Fold| for 0.5 micromolar"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.line = element_line(linewidth = 1.5),
    strip.background = element_rect(fill = "gray"),  # Gray background for facet labels
    strip.text = element_text(size = 12, color = "black", face = "bold"),  # Bold styling for facet labels
    axis.text.x = element_text(size = 10, color = "black", angle = 15)
  )

Past versions of "0.5abs logFC-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.5 Micromolar mean (Abs logFC) across timepoints

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)

# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3  <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")

DOX_0.5_3  <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")

# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Drug = "CX.5461", Timepoint = "3"),
  CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
  CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
  DOX_0.5_3  %>% mutate(Drug = "DOX", Timepoint = "3"),
  DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
  DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
      Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
      Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
      Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
      Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))  # Remove NA values

# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.5 %>%
  mutate(abs_logFC = abs(logFC)) %>%
  group_by(Response_Group, Drug, Timepoint) %>%
  dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
  as.data.frame()

# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
  Response_Group = unique(all_toptables_0.5$Response_Group),
  Drug = unique(all_toptables_0.5$Drug),
  Timepoint = c("3", "24", "48")
)

# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))

# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0

# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX + CX (mid-late) response\n(0.5 micromolar)",
                                       "CX + DOX (early) response\n(0.5 micromolar)",
                                       "DOX only mid-late response\n(0.5 micromolar)",
                                       "DOX-specific response\n(0.5 micromolar)",
                                       "Non response\n(0.5 micromolar)"  # Reversed order
    ))
  )

# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")

# **Plot the Line Plot for Mean Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
  geom_point(size = 3) +
  geom_line(size = 1.2) +
  scale_color_manual(values = drug_palette) +
  ylim(0, 2.5) +  # Adjust the Y-axis for better visualization
  facet_wrap(~ Response_Group, ncol = 1) +  # Facet by Response Group (Reversed Order)
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean |Log Fold Change|",
    title = "Mean |Log Fold Change| Across Response Groups (0.5 micromolar)",
    color = "Drug"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text = element_text(size = 12, color = "black"),
    strip.text = element_text(size = 12, color = "black", face = "bold"),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  )

Past versions of "0.5 mean-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.5 Micromolar logFC

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)

# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3  <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")

DOX_0.5_3  <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")

# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
  CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
  CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
  DOX_0.5_3  %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
  DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
  DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)

# Assign Response Groups
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
      Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
      Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
      Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
      Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX + CX (mid-late) response\n(0.5 micromolar)",
                                       "CX + DOX (early) response\n(0.5 micromolar)",
                                       "DOX only mid-late response\n(0.5 micromolar)",
                                       "DOX-specific response\n(0.5 micromolar)",
                                       "Non response\n(0.5 micromolar)"  # Reversed Order
    ))
  )

# **Plot the Boxplot**
ggplot(all_toptables_0.5, aes(x = Drug, y = logFC, fill = Drug)) +
  geom_boxplot() +
  scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
  facet_grid(Response_Group ~ Timepoint) +
  theme_bw() +
  labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.5 Micromolar") +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of 0.5logFC-1.png

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 0.5 Micromolar mean logFC across timepoints

# Load Required Libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)

# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3  <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")

DOX_0.5_3  <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")

# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
  CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
  CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
  DOX_0.5_3  %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
  DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
  DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)

# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)

# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
      Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
      Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
      Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
      Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Compute Mean logFC for Line Plot
data_summary_0.5 <- all_toptables_0.5 %>%
  group_by(Response_Group, Drug, Timepoint) %>%
  dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
  as.data.frame()

# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
  Response_Group = unique(all_toptables_0.5$Response_Group),
  Drug = unique(all_toptables_0.5$Drug),
  Timepoint = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")
)

# **Merge to keep missing timepoints**
data_summary_0.5 <- full_join(timepoints_full, data_summary_0.5, by = c("Response_Group", "Drug", "Timepoint"))

# **Replace NA mean_logFC with 0 if no genes were present**
data_summary_0.5$mean_logFC[is.na(data_summary_0.5$mean_logFC)] <- 0

# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary_0.5 <- data_summary_0.5 %>%
  mutate(
    Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
    Response_Group = factor(Response_Group, levels = c(
      "DOX + CX (mid-late) response\n(0.5 micromolar)",
                                       "CX + DOX (early) response\n(0.5 micromolar)",
                                       "DOX only mid-late response\n(0.5 micromolar)",
                                       "DOX-specific response\n(0.5 micromolar)",
                                       "Non response\n(0.5 micromolar)"  # Reversed Order
    ))
  )

# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")

# **Plot the Line Plot for Mean logFC**
ggplot(data_summary_0.5, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
  geom_point(size = 3) +
  geom_line(size = 1.2) +
  scale_color_manual(values = drug_palette) +
  ylim(-2, 1.5) +  # Adjust the Y-axis for better visualization
  facet_wrap(~ Response_Group, ncol = 1) +  # Facet by Response Group (Reversed Order)
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean Log Fold Change",
    title = "Mean Log Fold Change Across Response Groups (0.5 micromolar)",
    color = "Drug"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text = element_text(size = 12, color = "black"),
    strip.text = element_text(size = 12, color = "black", face = "bold"),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  )

Past versions of "0.5 mean2-1.png"

Version	Author	Date
91a7ce4	sayanpaul01	2025-03-03
d9ff853	sayanpaul01	2025-03-03

📌 Proportion of DNA Damage Repair Genes (0.1 micromolar)

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)

Warning: package 'tidyr' was built under R version 4.3.3

library(org.Hs.eg.db)

Warning: package 'AnnotationDbi' was built under R version 4.3.2

Warning: package 'IRanges' was built under R version 4.3.1

Warning: package 'S4Vectors' was built under R version 4.3.1

# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)

# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
  mutate(Entrez_ID = mapIds(org.Hs.eg.db,
                            keys = DNA_damage$Symbol,
                            column = "ENTREZID",
                            keytype = "SYMBOL",
                            multiVals = "first"))

DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)

# **🔹 Load Corrmotif Groups for 0.1 Concentration**
prob_groups_0.1 <- list(
  "Non Response (0.1)" = read.csv("data/prob_1_0.1.csv")$Entrez_ID,
   "CX_DOX mid-late (0.1)" = read.csv("data/prob_2_0.1.csv")$Entrez_ID,
  "DOX only mid-late (0.1)"= read.csv("data/prob_3_0.1.csv")$Entrez_ID
)

# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.1 <- bind_rows(
  lapply(prob_groups_0.1, function(ids) {
    data.frame(Entrez_ID = ids)
  }),
  .id = "Response_Group"
)

# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.1 <- corrmotif_df_0.1 %>%
  mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))

# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.1 %>%
  group_by(Response_Group, DNA_Damage) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Response_Group) %>%
  mutate(Percentage = (Count / sum(Count)) * 100)

# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))

# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.1)", "CX_DOX mid-late (0.1)","DOX only mid-late (0.1)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)

# **🔹 Perform Chi-Square Tests for "DOX only mid-late (0.1)" and "CX_DOX mid-late (0.1)" vs "Non Response (0.1)"**
non_response_counts <- proportion_data %>%
  filter(Response_Group == "Non Response (0.1)") %>%
  dplyr::select(DNA_Damage, Count) %>%
  {setNames(.$Count, .$DNA_Damage)}  # Convert to named vector

chi_results <- proportion_data %>%
  filter(Response_Group %in% c("CX_DOX mid-late (0.1)","DOX only mid-late (0.1)")) %>%
  group_by(Response_Group) %>%
  summarise(
    p_value = {
      group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
      if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
      if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)

      contingency_table <- matrix(c(
        group_counts[1], group_counts[2],
        non_response_counts["Yes"], non_response_counts["No"]
      ), nrow = 2, byrow = TRUE)

      # Perform chi-square test if all values are valid
      if (all(contingency_table >= 0 & is.finite(contingency_table))) {
        chisq.test(contingency_table)$p.value
      } else {
        NA
      }
    },
    .groups = "drop"
  ) %>%
  mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))

# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
  left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")

# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
  Response_Group = c("CX_DOX mid-late (0.1)", "DOX only mid-late (0.1)"),
  y_pos = 105,  # Fixed at 105% of Y-axis
  Significance = chi_results$Significance
)

# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
  geom_bar(stat = "identity", position = "stack") +  # Stacked bars
  geom_text(
    data = star_positions,
    aes(x = Response_Group, y = y_pos, label = Significance),  # Place stars at fixed 105%
    inherit.aes = FALSE,
    size = 6, color = "black", fontface = "bold", vjust = 0  # Keeps stars aligned
  ) +
  scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) +  # Fixed Y-axis to 100%
  scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +  # Yes (Red), No (Blue)
  labs(
    title = "Proportion of DNA Damage Repair Genes in\n0.1 Corrmotif Response Groups",
    x = "Response Groups (0.1 Concentration)",
    y = "Percentage",
    fill = "DNA Damage Repair"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
    legend.title = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
    strip.background = element_blank(),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of "prop DNA-1.png"

Version	Author	Date
81247f6	sayanpaul01	2025-03-06
96d0db1	sayanpaul01	2025-03-05

📌 Proportion of DNA Damage Repair Genes (0.5 micromolar)

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)

# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)

# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
  mutate(Entrez_ID = mapIds(org.Hs.eg.db,
                            keys = DNA_damage$Symbol,
                            column = "ENTREZID",
                            keytype = "SYMBOL",
                            multiVals = "first"))

DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)

# **🔹 Load Corrmotif Groups for 0.5 Concentration**
prob_groups_0.5 <- list(
  "Non Response (0.5)" = read.csv("data/prob_1_0.5.csv")$Entrez_ID,
  "DOX-specific response (0.5)" = read.csv("data/prob_2_0.5.csv")$Entrez_ID,
  "DOX only mid-late response (0.5)" = read.csv("data/prob_3_0.5.csv")$Entrez_ID,
  "CX DOX (early) response (0.5)" = read.csv("data/prob_4_0.5.csv")$Entrez_ID,
  "DOX + CX (mid-late) response (0.5)" = read.csv("data/prob_5_0.5.csv")$Entrez_ID
)

# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.5 <- bind_rows(
  lapply(prob_groups_0.5, function(ids) {
    data.frame(Entrez_ID = ids)
  }),
  .id = "Response_Group"
)

# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.5 <- corrmotif_df_0.5 %>%
  mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))

# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.5 %>%
  group_by(Response_Group, DNA_Damage) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Response_Group) %>%
  mutate(Percentage = (Count / sum(Count)) * 100)

# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))

# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.5)", "DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                    "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)

# **🔹 Perform Chi-Square Tests for Each Response Group vs Non-Response**
non_response_counts <- proportion_data %>%
  filter(Response_Group == "Non Response (0.5)") %>%
  dplyr::select(DNA_Damage, Count) %>%
  {setNames(.$Count, .$DNA_Damage)}  # Convert to named vector

# **Comparing Each Group Against "Non Response (0.5)"**
chi_results <- proportion_data %>%
  filter(Response_Group %in% c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                               "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")) %>%
  group_by(Response_Group) %>%
  summarise(
    p_value = {
      group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
      if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
      if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)

      contingency_table <- matrix(c(
        group_counts[1], group_counts[2],  # Response group counts
        non_response_counts["Yes"], non_response_counts["No"]  # Non-response counts
      ), nrow = 2, byrow = TRUE)

      # Perform chi-square test if all values are valid
      if (all(contingency_table >= 0 & is.finite(contingency_table))) {
        chisq.test(contingency_table)$p.value
      } else {
        NA
      }
    },
    .groups = "drop"
  ) %>%
  mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))

# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
  left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")

# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
  Response_Group = c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                     "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)"),
  y_pos = 105,  # Fixed at 105% of Y-axis
  Significance = chi_results$Significance
)

# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
  geom_bar(stat = "identity", position = "stack") +  # Stacked bars
  geom_text(
    data = star_positions,
    aes(x = Response_Group, y = y_pos, label = Significance),  # Place stars at fixed 105%
    inherit.aes = FALSE,
    size = 6, color = "black", fontface = "bold", vjust = 0  # Keeps stars aligned
  ) +
  scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) +  # **Y-axis now limited to 110% for visibility**
  scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +  # Yes (Red), No (Blue)
  labs(
    title = "Proportion of DNA Damage Repair Genes in\n0.5 Corrmotif Response Groups",
    x = "Response Groups (0.5 Concentration)",
    y = "Percentage",
    fill = "DNA Damage Repair Genes"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
    legend.title = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
    strip.background = element_blank(),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of "prop DNA 0.5-1.png"

Version	Author	Date
96d0db1	sayanpaul01	2025-03-05

📌 Proportion of P53 target Genes Genes (0.1 micromolar)

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)

# **🔹 Read P53 Target Gene List**
P53_Target <- read.csv("data/P53_Target.csv", stringsAsFactors = FALSE)

# Convert gene symbols to Entrez IDs
P53_Target <- P53_Target %>%
  mutate(Entrez_ID = mapIds(org.Hs.eg.db,
                            keys = P53_Target$Symbol,
                            column = "ENTREZID",
                            keytype = "SYMBOL",
                            multiVals = "first"))

P53_Target_genes <- na.omit(P53_Target$Entrez_ID)

# **🔹 Load Corrmotif Groups for 0.1 Concentration**
prob_groups_0.1 <- list(
  "Non Response (0.1)" = read.csv("data/prob_1_0.1.csv")$Entrez_ID,
  "CX_DOX mid-late (0.1)" = read.csv("data/prob_2_0.1.csv")$Entrez_ID,
 "DOX only mid-late (0.1)"= read.csv("data/prob_3_0.1.csv")$Entrez_ID
)

# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.1 <- bind_rows(
  lapply(prob_groups_0.1, function(ids) {
    data.frame(Entrez_ID = ids)
  }),
  .id = "Response_Group"
)

# **🔹 Match Entrez_IDs with P53 Target Genes**
corrmotif_df_0.1 <- corrmotif_df_0.1 %>%
  mutate(P53_Target = ifelse(Entrez_ID %in% P53_Target_genes, "Yes", "No"))

# **🔹 Count P53 Target Genes in Each Response Group**
proportion_data <- corrmotif_df_0.1 %>%
  group_by(Response_Group, P53_Target) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Response_Group) %>%
  mutate(Percentage = (Count / sum(Count)) * 100)

# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$P53_Target <- factor(proportion_data$P53_Target, levels = c("Yes", "No"))

# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.1)", "CX_DOX mid-late (0.1)", "DOX only mid-late (0.1)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)

# **🔹 Perform Chi-Square Tests for "DOX only mid-late (0.1)" and "CX_DOX mid-late (0.1)" vs "Non Response (0.1)"**
non_response_counts <- proportion_data %>%
  filter(Response_Group == "Non Response (0.1)") %>%
  dplyr::select(P53_Target, Count) %>%
  {setNames(.$Count, .$P53_Target)}  # Convert to named vector

chi_results <- proportion_data %>%
  filter(Response_Group %in% c("CX_DOX mid-late (0.1)","DOX only mid-late (0.1)")) %>%
  group_by(Response_Group) %>%
  summarise(
    p_value = {
      group_counts <- Count[P53_Target %in% c("Yes", "No")]
      if (!"Yes" %in% P53_Target) group_counts <- c(group_counts, 0)
      if (!"No" %in% P53_Target) group_counts <- c(0, group_counts)

      contingency_table <- matrix(c(
        group_counts[1], group_counts[2],
        non_response_counts["Yes"], non_response_counts["No"]
      ), nrow = 2, byrow = TRUE)

      # Perform chi-square test if all values are valid
      if (all(contingency_table >= 0 & is.finite(contingency_table))) {
        chisq.test(contingency_table)$p.value
      } else {
        NA
      }
    },
    .groups = "drop"
  ) %>%
  mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))

# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
  left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")

# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
  Response_Group = c("CX_DOX mid-late (0.1)","DOX only mid-late (0.1)"),
  y_pos = 105,  # Fixed at 105% of Y-axis
  Significance = chi_results$Significance
)

# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = P53_Target)) +
  geom_bar(stat = "identity", position = "stack") +  # Stacked bars
  geom_text(
    data = star_positions,
    aes(x = Response_Group, y = y_pos, label = Significance),  # Place stars at fixed 105%
    inherit.aes = FALSE,
    size = 6, color = "black", fontface = "bold", vjust = 0  # Keeps stars aligned
  ) +
  scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) +  # Fixed Y-axis to 100%
  scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +  # Yes (Red), No (Blue)
  labs(
    title = "Proportion of P53 Target Genes in\n0.1 Corrmotif Response Groups",
    x = "Response Groups (0.1 Concentration)",
    y = "Percentage",
    fill = "P53 Target Genes"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
    legend.title = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
    strip.background = element_blank(),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of "prop P53 0.1-1.png"

Version	Author	Date
81247f6	sayanpaul01	2025-03-06
96d0db1	sayanpaul01	2025-03-05

📌 Proportion of P53 target Genes (0.5 micromolar)

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)

# **🔹 Read P53 Target Gene List**
P53_Target <- read.csv("data/P53_Target.csv", stringsAsFactors = FALSE)

# Convert gene symbols to Entrez IDs
P53_Target <- P53_Target %>%
  mutate(Entrez_ID = mapIds(org.Hs.eg.db,
                            keys = P53_Target$Symbol,
                            column = "ENTREZID",
                            keytype = "SYMBOL",
                            multiVals = "first"))

P53_Target_genes <- na.omit(P53_Target$Entrez_ID)

# **🔹 Load Corrmotif Groups for 0.5 Concentration**
prob_groups_0.5 <- list(
  "Non Response (0.5)" = read.csv("data/prob_1_0.5.csv")$Entrez_ID,
  "DOX-specific response (0.5)" = read.csv("data/prob_2_0.5.csv")$Entrez_ID,
  "DOX only mid-late response (0.5)" = read.csv("data/prob_3_0.5.csv")$Entrez_ID,
  "CX DOX (early) response (0.5)" = read.csv("data/prob_4_0.5.csv")$Entrez_ID,
  "DOX + CX (mid-late) response (0.5)" = read.csv("data/prob_5_0.5.csv")$Entrez_ID
)

# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.5 <- bind_rows(
  lapply(prob_groups_0.5, function(ids) {
    data.frame(Entrez_ID = ids)
  }),
  .id = "Response_Group"
)

# **🔹 Match Entrez_IDs with P53 Target Genes**
corrmotif_df_0.5 <- corrmotif_df_0.5 %>%
  mutate(P53_Target = ifelse(Entrez_ID %in% P53_Target_genes, "Yes", "No"))

# **🔹 Count P53 Target Genes in Each Response Group**
proportion_data <- corrmotif_df_0.5 %>%
  group_by(Response_Group, P53_Target) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Response_Group) %>%
  mutate(Percentage = (Count / sum(Count)) * 100)

# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$P53_Target <- factor(proportion_data$P53_Target, levels = c("Yes", "No"))

# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.5)", "DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                    "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)

# **🔹 Perform Chi-Square Tests for Each Response Group vs Non-Response**
non_response_counts <- proportion_data %>%
  filter(Response_Group == "Non Response (0.5)") %>%
  dplyr::select(P53_Target, Count) %>%
  {setNames(.$Count, .$P53_Target)}  # Convert to named vector

# **Comparing Each Group Against "Non Response (0.5)"**
chi_results <- proportion_data %>%
  filter(Response_Group %in% c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                               "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")) %>%
  group_by(Response_Group) %>%
  summarise(
    p_value = {
      group_counts <- Count[P53_Target %in% c("Yes", "No")]
      if (!"Yes" %in% P53_Target) group_counts <- c(group_counts, 0)
      if (!"No" %in% P53_Target) group_counts <- c(0, group_counts)

      contingency_table <- matrix(c(
        group_counts[1], group_counts[2],  # Response group counts
        non_response_counts["Yes"], non_response_counts["No"]  # Non-response counts
      ), nrow = 2, byrow = TRUE)

      # Perform chi-square test if all values are valid
      if (all(contingency_table >= 0 & is.finite(contingency_table))) {
        chisq.test(contingency_table)$p.value
      } else {
        NA
      }
    },
    .groups = "drop"
  ) %>%
  mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))

Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 3: `Response_Group = CX DOX (early) response (0.5)`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrect

# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
  left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")

# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
  Response_Group = c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)", 
                     "CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)"),
  y_pos = 105,  # Fixed at 105% of Y-axis
  Significance = chi_results$Significance
)

# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = P53_Target)) +
  geom_bar(stat = "identity", position = "stack") +  # Stacked bars
  geom_text(
    data = star_positions,
    aes(x = Response_Group, y = y_pos, label = Significance),  # Place stars at fixed 105%
    inherit.aes = FALSE,
    size = 6, color = "black", fontface = "bold", vjust = 0  # Keeps stars aligned
  ) +
  scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) +  # **Y-axis now limited to 110% for visibility**
  scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +  # Yes (Red), No (Blue)
  labs(
    title = "Proportion of P53 Target Genes in\n0.5 Corrmotif Response Groups",
    x = "Response Groups (0.5 Concentration)",
    y = "Percentage",
    fill = "P53 Target Genes"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    axis.title = element_text(size = 15, color = "black"),
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
    legend.title = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
    strip.background = element_blank(),
    strip.text = element_text(size = 12, face = "bold")
  )

Past versions of "prop P53 0.5-1.png"

Version	Author	Date
96d0db1	sayanpaul01	2025-03-05

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] org.Hs.eg.db_3.18.0  AnnotationDbi_1.64.1 IRanges_2.36.0      
 [4] S4Vectors_0.40.1     tidyr_1.3.1          ggplot2_3.5.1       
 [7] gprofiler2_0.2.3     BiocParallel_1.36.0  dplyr_1.1.4         
[10] Rfast_2.1.0          RcppParallel_5.1.9   RcppZiggurat_0.1.6  
[13] Rcpp_1.0.12          Cormotif_1.48.0      limma_3.58.1        
[16] affy_1.80.0          Biobase_2.62.0       BiocGenerics_0.48.1 
[19] workflowr_1.7.1     

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        viridisLite_0.4.2       farver_2.1.2           
 [4] blob_1.2.4              bitops_1.0-7            Biostrings_2.70.1      
 [7] RCurl_1.98-1.13         fastmap_1.1.1           lazyeval_0.2.2         
[10] promises_1.3.0          digest_0.6.34           lifecycle_1.0.4        
[13] statmod_1.5.0           processx_3.8.5          KEGGREST_1.42.0        
[16] RSQLite_2.3.3           magrittr_2.0.3          compiler_4.3.0         
[19] rlang_1.1.3             sass_0.4.9              tools_4.3.0            
[22] yaml_2.3.10             data.table_1.14.10      knitr_1.49             
[25] labeling_0.4.3          htmlwidgets_1.6.4       bit_4.0.5              
[28] withr_3.0.2             purrr_1.0.2             grid_4.3.0             
[31] preprocessCore_1.64.0   git2r_0.35.0            colorspace_2.1-0       
[34] scales_1.3.0            cli_3.6.1               crayon_1.5.3           
[37] rmarkdown_2.29          generics_0.1.3          rstudioapi_0.17.1      
[40] httr_1.4.7              DBI_1.2.3               cachem_1.0.8           
[43] stringr_1.5.1           zlibbioc_1.48.0         parallel_4.3.0         
[46] XVector_0.42.0          BiocManager_1.30.25     vctrs_0.6.5            
[49] jsonlite_1.8.9          callr_3.7.6             bit64_4.0.5            
[52] plotly_4.10.4           jquerylib_0.1.4         affyio_1.72.0          
[55] glue_1.7.0              codetools_0.2-20        ps_1.8.1               
[58] stringi_1.8.3           gtable_0.3.6            GenomeInfoDb_1.38.8    
[61] later_1.3.2             munsell_0.5.1           tibble_3.2.1           
[64] pillar_1.10.1           htmltools_0.5.8.1       GenomeInfoDbData_1.2.11
[67] R6_2.5.1                rprojroot_2.0.4         evaluate_1.0.3         
[70] png_0.1-8               memoise_2.0.1           httpuv_1.6.15          
[73] bslib_0.8.0             whisker_0.4.1           xfun_0.50              
[76] fs_1.6.3                getPass_0.2-4           pkgconfig_2.0.3