Last updated: 2026-01-28

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knitr::opts_chunk$set(echo = TRUE,warning=FALSE,message=FALSE, dev=c("png","pdf"))
####Library Loading####
library("edgeR")
library("ggplot2")
library("tibble")
library("dplyr")
library("ggrepel")
library("readr")
library("org.Hs.eg.db")
library("AnnotationDbi")
library("pheatmap")
library("tidyverse")
library("workflowr")
library("RUVSeq")
library("SummarizedExperiment")
library("readxl")
library("ggfortify")
library("ComplexHeatmap")

# BiocManager::install("SummarizedExperiment")

RNA_fc_df <- readRDS("data/Raw_Data/RNA_fc_df.RDS")
RNA_Metadata <- readRDS("data/Raw_Data/RNA_Metadata.RDS")
ann_colors <- readRDS("data/QC/ann_colors.RDS")

RNA_Metadata_No4 <- readRDS("data/QC/RNA_Metatdata_No4.RDS")
Filt_RMG0_RNA_fc_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_fc_NoD4.RDS")
Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_log2cpm_NoD4.RDS")
ann_colors_No4 <- readRDS("data/QC/ann_colors_no4.RDS")

Filt_RMG0_RNA_fc_NoD4_NoRep <- readRDS("data/Cormotif/Filt_RMG0_RNA_fc_NoD4_NoRep.RDS")
Filt_RMG0_RNA_log2cpm_NoD4_NoRep <- readRDS("data/Cormotif/Filt_RMG0_RNA_log2cpm_NoD4_NoRep.RDS")
## 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") {DOX_24T_shared_DEGs
    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))
}
check_overlap <- function(list_of_lists) {
  # create empty list to store results
  overlap_results <- list()
  
  # loop over each cluster
  for (i in seq_along(list_of_lists)) {
    cluster_name <- names(list_of_lists)[i]
    genes_i <- list_of_lists[[i]]
    
    # compare to all other clusters
    overlaps <- sapply(seq_along(list_of_lists), function(j) {
      if (i == j) return(NA)  # skip self
      genes_j <- list_of_lists[[j]]
      length(intersect(genes_i, genes_j))  # count of overlapping genes
    })
    names(overlaps) <- names(list_of_lists)
    
    overlap_results[[cluster_name]] <- overlaps
  }
  
  return(overlap_results)
}
# Seed0 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_DayBefore_seed0.rds")
# Seed1 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed1.rds")
# Seed2 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed2.rds")
# Seed3 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed3.rds")
# Seed4 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed4.rds")
# Seed5 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed5.rds")
# Seed6 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed6.rds")
# Seed7 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed7.rds")
# Seed8 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed8.rds")
# Seed9 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed9.rds")
# Seed10 <- readRDS("~/diff_timeline_tes/RNA/Run1_Run2_Concat/cormotif/CorMotif_k1_k20_seed10.rds")
# 
# CoMortiffits_TACC <- list(
#   Seed0,
#   Seed1,
#   Seed2,
#   Seed3,
#   Seed4,
#   Seed5,
#   Seed6,
#   Seed7,
#   Seed8,
#   Seed9,
#   Seed10
# )
# 
# #saveRDS(CoMortiffits_TACC,"data/Cormotif/DayBefore_CoMortiffits_TACC.RDS")
# Seed8$bic
# extract_bic <- function(fit, seed_id) {
#   # bic is a 20x2 matrix: column "bic" contains the actual BIC
#   bic_values <- fit$bic[, "bic"]
#   
#   tibble(
#     seed = seed_id,
#     motifs = seq_along(bic_values),
#     BIC = as.numeric(bic_values)
#   )
# }
# 
# # Define %||% if not using rlang
# `%||%` <- function(x, y) if(!is.null(x)) x else y
# 
# 
# names(DayBefore_CoMortiffits_TACC) <- paste0("seed_", seq_along(DayBefore_CoMortiffits_TACC))
# 
# bic_table <- map2_dfr(
#   DayBefore_CoMortiffits_TACC,
#   names(DayBefore_CoMortiffits_TACC),
#   extract_bic
# )
# 
# head(bic_table)
# table(bic_table$seed)
# 
# bic_summary <- bic_table %>%
#   group_by(motifs) %>%
#   summarise(
#     mean_BIC = mean(BIC),
#     sd_BIC   = sd(BIC),
#     n_seeds  = n(),
#     .groups = "drop"
#   )
# 
# bic_summary
# 
# plotIC(Seed8)
Filt_RMG0_RNA_log2cpm_NoD4_NoRep <- Filt_RMG0_RNA_log2cpm_NoD4 %>%
  as.data.frame() %>%             # convert to data frame
  dplyr::select(-contains("R")) %>%  # remove columns containing "R"
  as.matrix()                     # convert back to matrix


# saveRDS(Filt_RMG0_RNA_log2cpm_NoD4_NoRep,"data/Cormotif/Filt_RMG0_RNA_log2cpm_NoD4_NoRep.RDS")
groupid <- c(rep(1:5,7),rep(6:10,7))
compid_Cond1 <- c(1,2,3,4,6,7,8,9)
compid_Cond2 <- c(2,3,4,5,7,8,9,10)
compid <- cbind(compid_Cond1,compid_Cond2)

#Model Fitting

# set.seed(8)
# motif.fitted<-cormotiffit(exprs = Filt_RMG0_RNA_log2cpm_NoD4_NoRep,
#                           groupid = groupid,
#                           compid = compid,
#                           K=11,
#                           max.iter = 3000,
#                           runtype = "logCPM")
# 
# 
# saveRDS(motif.fitted,"data/Cormotif/cormotif_RNA__seed8_k8_DayBefore_emmaCode.RDS")
motif.fitted <- readRDS("data/Cormotif/cormotif_RNA__seed8_k8_DayBefore_emmaCode.RDS")
plotIC(motif.fitted)

Version Author Date
f388c90 John D. Hurley 2026-01-28 
motif.fitted$bic
      K      bic
[1,] 11 708372.2
plotMotif(motif.fitted)

Version Author Date
f388c90 John D. Hurley 2026-01-28 
#  Studies Human Day0 - 1. HDay2, 2. HDay5, 3. HDay15, 4. HDay30
#          Chimp Day0 - 5. CDay2, 6. CDay5, 7. CDay15, 8. CDay30
# Set up Variables
groupid <- c(rep(1:5,7),rep(6:10,7))
compid_Cond1 <- c(1,2,3,4,6,7,8,9)
compid_Cond2 <- c(2,3,4,5,7,8,9,10)

# Extract motif matrix
motif_matrix <- as.matrix(motif.fitted$bestmotif$motif.q)

# Label rows and columns
rownames(motif_matrix) <- paste0("Motif_", seq_len(nrow(motif_matrix)))
colnames(motif_matrix) <- paste0("C", compid[,1], "_vs_C", compid[,2])

# Convert to tidy format
motif_df <- as.data.frame(motif_matrix) %>%
  mutate(Motif = rownames(.)) %>%
  pivot_longer(-Motif, names_to = "Comparison", values_to = "Probability")

# Reorder motifs 1–11
motif_df$Motif <- factor(motif_df$Motif, levels = paste0("Motif_", 1:11))

motif_df$Comparison <- factor(motif_df$Comparison, levels = c("C1_vs_C2","C2_vs_C3","C3_vs_C4","C4_vs_C5","C6_vs_C7","C7_vs_C8","C8_vs_C9","C9_vs_C10"))

# Plot
ggplot(motif_df, aes(x = Comparison, y = Motif, fill = Probability)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "white", high = "black") +
  labs(
    title = "CorMotif Motif Activity Across Comparisons \n  (no day4) (no replicates) (RowMeans>0) DayBefore" ,
    x = "Comparison (Condition 1 vs Condition 2)",
    y = "Motif (1–11)",
    fill = "Posterior Probability"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid = element_blank()
  )

Version Author Date
f388c90 John D. Hurley 2026-01-28 
#  Comparison C1 = Human Day0
#  Comparison C2 = Human Day2
#  Comparison C3 = Human Day5
#  Comparison C4 = Human Day15
#  Comparison C5 = Human Day30
#  Comparison C6 = Chimp Day0
#  Comparison C7 = Chimp Day2
#  Comparison C8 = Chimp Day5
#  Comparison C9 = Chimp Day15
#  Comparison C10 = Chimp Day30
motif.fit <- motif.fitted
log2cpm <- Filt_RMG0_RNA_log2cpm_NoD4

#extract the posterior probability that these genes belong to motifs
gene_prob_ppost <- motif.fit$bestmotif$p.post
rownames(gene_prob_ppost) <- rownames(log2cpm)
# saveRDS(gene_prob_ppost,"data/Cormotif/RNA_gene_prob_ppost_Trajectory.RDS")

#assign each gene to a motif with max post prob
assigned_motifs <- apply(gene_prob_ppost, 1, which.max)
max_probs <- apply(gene_prob_ppost, 1, max)

#combine these into a dataframe - motif assigned genes (p.post)
motif_assignment_df <- gene_prob_ppost %>%
  as.data.frame() %>%
  rownames_to_column("Gene") %>%
  mutate(
    Assigned_Motif = assigned_motifs[Gene],
    Max_Probability = max_probs[Gene]
  )

#make some histograms of the unfiltered data from Cormotif p.post

gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V1))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V1")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V2))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V2")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V3))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V3")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V4))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V4")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V5))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V5")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V6))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V6")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V7))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V7")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V8))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V8")

Version Author Date
f388c90 John D. Hurley 2026-01-28 
motif.fit <- motif.fitted
log2cpm <- Filt_RMG0_RNA_log2cpm_NoD4
#extract the cluster likelihood - which DEGs are most likely to be in this cluster
motif_prob <- motif.fit$bestmotif$clustlike
rownames(motif_prob) <- rownames(gene_prob_ppost)
# saveRDS(motif_prob,"data/RNA_motif_prob_Trajectory.RDS")

# motif.fitted$bestmotif$clustlike

clust1_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 > 0.5 & V2 <  0.5 & V3 < 0.5   & V4 < 0.5 & V5 < 0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust1_RNA")
[1] "clust1_RNA"
length(clust1_RNA)
[1] 3381
clust2_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 > 0.5 & V3 < 0.5 & V4 < 0.5 & V5 < 0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust2_RNA")
[1] "clust2_RNA"
length(clust2_RNA)
[1] 815
clust3_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 > 0.5 & V4 < 0.5 & V5 < 0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust3_RNA")
[1] "clust3_RNA"
length(clust3_RNA)
[1] 623
clust4_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 > 0.5 & V5 < 0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5) %>%
  rownames()
print("clust4_RNA")
[1] "clust4_RNA"
length(clust4_RNA)
[1] 1129
clust5_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 > 0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust5_RNA")
[1] "clust5_RNA"
length(clust5_RNA)
[1] 37
clust6_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 < 0.5 & V6 > 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust6_RNA")
[1] "clust6_RNA"
length(clust6_RNA)
[1] 416
clust7_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 < 0.5 & V6 < 0.5 & V7 > 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust7_RNA")
[1] "clust7_RNA"
length(clust7_RNA)
[1] 586
clust8_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 <0.5 & V6 < 0.5 & V7 < 0.5 & V8 > 0.5 & V9 < 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust8_RNA")
[1] "clust8_RNA"
length(clust8_RNA)
[1] 628
clust9_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 <0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 > 0.5 & V10 < 0.5 & V11 < 0.5) %>%
  rownames()
print("clust9_RNA")
[1] "clust9_RNA"
length(clust9_RNA)
[1] 669
clust10_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 <0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 > 0.5 & V11 < 0.5) %>%
  rownames()
print("clust10_RNA")
[1] "clust10_RNA"
length(clust10_RNA)
[1] 1560
clust11_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 < 0.5 & V5 <0.5 & V6 < 0.5 & V7 < 0.5 & V8 < 0.5 & V9 < 0.5 & V10 < 0.5 & V11 > 0.5) %>%
  rownames()
print("clust11_RNA")
[1] "clust11_RNA"
length(clust11_RNA)
[1] 656
clust_names_list <- list(
  clust1_RNA,
  clust2_RNA,
  clust3_RNA,
  clust4_RNA,
  clust5_RNA,
  clust6_RNA,
  clust7_RNA,
  clust8_RNA,
  clust9_RNA,
  clust10_RNA,
  clust11_RNA
)

names(clust_names_list) <- paste0("clust", 1:11)

print(overlap_matirx <- check_overlap(clust_names_list))
$clust1
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
     NA       0       0       0       0       0       0       0       0       0 
clust11 
      0 

$clust2
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0      NA       0       0       0       0       0       0       0       0 
clust11 
      0 

$clust3
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0      NA       0       0       0       0       0       0       0 
clust11 
      0 

$clust4
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0      NA       0       0       0       0       0       0 
clust11 
      0 

$clust5
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0      NA       0       0       0       0       0 
clust11 
      0 

$clust6
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0      NA       0       0       0       0 
clust11 
      0 

$clust7
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0       0      NA       0       0       0 
clust11 
      0 

$clust8
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0       0       0      NA       0       0 
clust11 
      0 

$clust9
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0       0       0       0      NA       0 
clust11 
      0 

$clust10
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0       0       0       0       0      NA 
clust11 
      0 

$clust11
 clust1  clust2  clust3  clust4  clust5  clust6  clust7  clust8  clust9 clust10 
      0       0       0       0       0       0       0       0       0       0 
clust11 
     NA 
#View(motif_prob)
# write.csv(clust1_RNA,"data/Cormotif/clust1_RNA_Trajectory.csv")
# write.csv(clust2_RNA,"data/Cormotif/clust2_RNA_Trajectory.csv")
# write.csv(clust3_RNA,"data/Cormotif/clust3_RNA_Trajectory.csv")
# write.csv(clust4_RNA,"data/Cormotif/clust4_RNA_Trajectory.csv")
# write.csv(clust5_RNA,"data/Cormotif/clust5_RNA_Trajectory.csv")
# write.csv(clust6_RNA,"data/Cormotif/clust6_RNA_Trajectory.csv")
# write.csv(clust7_RNA,"data/Cormotif/clust7_RNA_Trajectory.csv")
# write.csv(clust8_RNA,"data/Cormotif/clust8_RNA_Trajectory.csv")
# write.csv(clust9_RNA,"data/Cormotif/clust9_RNA_Trajectory.csv")
# write.csv(clust10_RNA,"data/Cormotif/clust10_RNA_Trajectory.csv")
# write.csv(clust11_RNA,"data/Cormotif/clust11_RNA_Trajectory.csv")
# saveRDS(clust1_RNA,"data/Cormotif/clust1_RNA_Trajectory.RDS")
# saveRDS(clust2_RNA,"data/Cormotif/clust2_RNA_Trajectory.RDS")
# saveRDS(clust3_RNA,"data/Cormotif/clust3_RNA_Trajectory.RDS")
# saveRDS(clust4_RNA,"data/Cormotif/clust4_RNA_Trajectory.RDS")
# saveRDS(clust5_RNA,"data/Cormotif/clust5_RNA_Trajectory.RDS")
# saveRDS(clust6_RNA,"data/Cormotif/clust6_RNA_Trajectory.RDS")
# saveRDS(clust7_RNA,"data/Cormotif/clust7_RNA_Trajectory.RDS")
# saveRDS(clust8_RNA,"data/Cormotif/clust8_RNA_Trajectory.RDS")
# saveRDS(clust9_RNA,"data/Cormotif/clust9_RNA_Trajectory.RDS")
# saveRDS(clust10_RNA,"data/Cormotif/clust10_RNA_Trajectory.RDS")
# saveRDS(clust1_RNA,"data/Cormotif/clust11_RNA_Trajectory.RDS")
# ```
# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_CorMotif_Trajectory_Ensemble.Rmd")

sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ComplexHeatmap_2.24.1       ggfortify_0.4.19           
 [3] readxl_1.4.5                RUVSeq_1.42.0              
 [5] EDASeq_2.42.0               ShortRead_1.66.0           
 [7] GenomicAlignments_1.44.0    SummarizedExperiment_1.38.1
 [9] MatrixGenerics_1.20.0       matrixStats_1.5.0          
[11] Rsamtools_2.24.0            GenomicRanges_1.60.0       
[13] Biostrings_2.76.0           GenomeInfoDb_1.44.3        
[15] XVector_0.48.0              BiocParallel_1.42.1        
[17] lubridate_1.9.4             forcats_1.0.1              
[19] stringr_1.5.2               purrr_1.1.0                
[21] tidyr_1.3.1                 tidyverse_2.0.0            
[23] pheatmap_1.0.13             org.Hs.eg.db_3.21.0        
[25] AnnotationDbi_1.70.0        IRanges_2.42.0             
[27] S4Vectors_0.46.0            Biobase_2.68.0             
[29] BiocGenerics_0.54.0         generics_0.1.4             
[31] readr_2.1.5                 ggrepel_0.9.6              
[33] dplyr_1.1.4                 tibble_3.3.0               
[35] ggplot2_4.0.0               edgeR_4.6.3                
[37] limma_3.64.3                workflowr_1.7.2            

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      shape_1.4.6.1           rstudioapi_0.18.0      
  [4] jsonlite_2.0.0          magrittr_2.0.3          GenomicFeatures_1.60.0 
  [7] farver_2.1.2            rmarkdown_2.30          GlobalOptions_0.1.3    
 [10] BiocIO_1.18.0           fs_1.6.6                vctrs_0.6.5            
 [13] memoise_2.0.1           RCurl_1.98-1.17         htmltools_0.5.8.1      
 [16] S4Arrays_1.8.1          progress_1.2.3          curl_7.0.0             
 [19] cellranger_1.1.0        SparseArray_1.8.1       sass_0.4.10            
 [22] bslib_0.9.0             httr2_1.2.2             cachem_1.1.0           
 [25] whisker_0.4.1           iterators_1.0.14        lifecycle_1.0.5        
 [28] pkgconfig_2.0.3         Matrix_1.7-3            R6_2.6.1               
 [31] fastmap_1.2.0           clue_0.3-66             GenomeInfoDbData_1.2.14
 [34] digest_0.6.37           colorspace_2.1-2        ps_1.9.1               
 [37] rprojroot_2.1.1         RSQLite_2.4.3           hwriter_1.3.2.1        
 [40] labeling_0.4.3          filelock_1.0.3          timechange_0.3.0       
 [43] httr_1.4.7              abind_1.4-8             compiler_4.5.1         
 [46] doParallel_1.0.17       bit64_4.6.0-1           withr_3.0.2            
 [49] S7_0.2.0                DBI_1.2.3               R.utils_2.13.0         
 [52] biomaRt_2.64.0          MASS_7.3-65             rappdirs_0.3.4         
 [55] DelayedArray_0.34.1     rjson_0.2.23            tools_4.5.1            
 [58] otel_0.2.0              httpuv_1.6.16           R.oo_1.27.1            
 [61] glue_1.8.0              restfulr_0.0.16         callr_3.7.6            
 [64] promises_1.3.3          getPass_0.2-4           cluster_2.1.8.1        
 [67] gtable_0.3.6            tzdb_0.5.0              R.methodsS3_1.8.2      
 [70] hms_1.1.4               xml2_1.5.1              foreach_1.5.2          
 [73] pillar_1.11.1           later_1.4.4             circlize_0.4.17        
 [76] BiocFileCache_2.16.2    lattice_0.22-7          rtracklayer_1.68.0     
 [79] aroma.light_3.38.0      bit_4.6.0               deldir_2.0-4           
 [82] tidyselect_1.2.1        locfit_1.5-9.12         knitr_1.51             
 [85] git2r_0.36.2            gridExtra_2.3           xfun_0.53              
 [88] statmod_1.5.0           stringi_1.8.7           UCSC.utils_1.4.0       
 [91] yaml_2.3.10             evaluate_1.0.5          codetools_0.2-20       
 [94] interp_1.1-6            cli_3.6.5               processx_3.8.6         
 [97] jquerylib_0.1.4         Rcpp_1.1.0              dbplyr_2.5.1           
[100] png_0.1-8               XML_3.99-0.20           parallel_4.5.1         
[103] blob_1.3.0              prettyunits_1.2.0       latticeExtra_0.6-31    
[106] jpeg_0.1-11             bitops_1.0-9            pwalign_1.4.0          
[109] scales_1.4.0            crayon_1.5.3            GetoptLong_1.1.0       
[112] rlang_1.1.6             KEGGREST_1.48.1