Bayesian analysis

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Last updated: 2023-11-06

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Knit directory: Cardiotoxicity/

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    Modified:   output/plan2plot.png
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Cormotifcluster_analysis.Rmd) and HTML (docs/Cormotifcluster_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	c84a699	reneeisnowhere	2023-09-28	title update
html	3511b8e	reneeisnowhere	2023-09-25	Build site.
Rmd	7c03236	reneeisnowhere	2023-09-25	updates to plots and code
html	45073b8	reneeisnowhere	2023-07-28	Build site.
Rmd	023b9cb	reneeisnowhere	2023-07-28	new figures update
html	c209a8c	reneeisnowhere	2023-06-30	Build site.
Rmd	661e5dc	reneeisnowhere	2023-06-30	update figures with final counts
Rmd	8a8998f	reneeisnowhere	2023-06-30	update changes to genelist (if any)
Rmd	f4bd5e1	reneeisnowhere	2023-06-27	checking code changes overtime
html	d5076c9	reneeisnowhere	2023-06-21	Build site.
Rmd	a8c7dfb	reneeisnowhere	2023-06-21	adding legend
html	89bc24d	reneeisnowhere	2023-06-02	Build site.
Rmd	ba8f440	reneeisnowhere	2023-06-02	update plot
html	bc9a824	reneeisnowhere	2023-06-02	Build site.
Rmd	2aca0d8	reneeisnowhere	2023-06-02	adding in LFC plot to show magnitude of effect
Rmd	2be1117	reneeisnowhere	2023-05-31	updating pie charts and lfc to remove significance test
html	4393a05	reneeisnowhere	2023-05-26	Build site.
Rmd	ac69e6e	reneeisnowhere	2023-05-26	adding abs LFC of motifs
Rmd	f3871a5	reneeisnowhere	2023-05-26	updates
html	71f4c17	reneeisnowhere	2023-05-25	Build site.
Rmd	43b7b74	reneeisnowhere	2023-05-25	update pie chart with ggplot
Rmd	e8c82ec	reneeisnowhere	2023-05-18	adding other_analysis and genes of interest log2cpm
html	0835f9c	reneeisnowhere	2023-05-17	Build site.
Rmd	4edac57	reneeisnowhere	2023-05-17	adding piechart and barchart
html	f0bbc78	reneeisnowhere	2023-04-19	Build site.
Rmd	5ce3414	reneeisnowhere	2023-04-19	updated Cormotif
html	f4a33f1	reneeisnowhere	2023-04-19	Build site.
Rmd	b117da5	reneeisnowhere	2023-04-19	updated Cormotif
html	22246d9	reneeisnowhere	2023-04-18	Build site.
Rmd	6f6aaf4	reneeisnowhere	2023-04-18	new Cormotif analysis with codes
html	d0f459b	reneeisnowhere	2023-04-18	Build site.
Rmd	67b2a09	reneeisnowhere	2023-04-18	new Cormotif analysis with codes
Rmd	a64bbe1	reneeisnowhere	2023-04-18	updated with new cormotif method
html	9e37491	reneeisnowhere	2023-04-16	Build site.
Rmd	6d925a2	reneeisnowhere	2023-04-16	updating cormotif with updated RNAseq counts
Rmd	4e52216	reneeisnowhere	2023-03-31	End of week updates
Rmd	c365253	reneeisnowhere	2023-03-22	updated code and plots
Rmd	3a26d52	reneeisnowhere	2023-03-22	Wed poster analysis changes
Rmd	a150323	reneeisnowhere	2023-03-20	addingcormotif analysis and go on DEGs

---

library(tidyverse)
library(gprofiler2)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(kableExtra)
library(scales)
library(ggVennDiagram)
library(Cormotif)
library(RColorBrewer)
library(ggpubr)

Creation of the data set:

## 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))
}

library(edgeR)
library(Cormotif)
library(RColorBrewer)

## read in count file##
design <- read.csv("data/data_outline.txt", row.names = 1)
mymatrix <- readRDS("data/filtermatrix_x.RDS")#should be 14084
x_counts <- mymatrix$counts
label_list <- readRDS("data/label_list.RDS")

list2env(label_list,envir = .GlobalEnv)
label <- (interaction(drug, indv, time))
colnames(x_counts) <- label
group_fac <- group1
groupid <- as.numeric(group_fac)
# saveRDS(x_counts,"output/x_counts.RDS")
compid <- data.frame(c1= c(1,2,3,4,5,7,8,9,10,11), c2 = c( 6,6,6,6,6,12,12,12,12,12))

y_TMM_cpm <- cpm(x_counts, log = TRUE)

colnames(y_TMM_cpm) <- label
y_TMM_cpm
set.seed(12345)
cormotif_initial <- cormotiffit(exprs = y_TMM_cpm,
                             groupid = groupid,
                             compid = compid,
                               K=1:8, max.iter = 500, runtype="logCPM")
gene_prob_tran <- cormotif_initial$bestmotif$p.post
rownames(gene_prob_tran) <- rownames(y_TMM_cpm)
motif_prob <- cormotif_initial$bestmotif$clustlike
rownames(motif_prob) <- rownames(y_TMM_cpm)
write.csv(motif_prob,"output/cormotif_probability_genelist.csv")

cormotif_initial was created after calling corMotif, then running the corMotifcustom.R script. The extra R script enabled me to generate a table containing the likelihood of each gene that belongs to the specific cluster.

After generating the Motifs from 1 to 8, the number of motifs that best fit the data was 4 using the BIC and AIC results below.

cormotif_initial <- readRDS("data/cormotif_initialall.RDS")
myColors <-  rev(c("#FFFFFF", "#E6E6E6" ,"#CCCCCC", "#B3B3B3", "#999999", "#808080", "#666666","#4C4C4C", "#333333", "#191919","#000000"))


plotIC(cormotif_initial)

Past versions of "plot motifs-1.png"

Version	Author	Date
d0f459b	reneeisnowhere	2023-04-18

plotMotif(cormotif_initial)

Past versions of "plot motifs-2.png"

Version	Author	Date
d0f459b	reneeisnowhere	2023-04-18

plot.new()
legend('bottomleft',fill=myColors, legend =rev(c("0", "0.1", "0.2", "0.3", "0.4",  "0.5", "0.6", "0.7", "0.8","0.9", "1")), box.col="white",title = "Probability\nlegend", horiz=FALSE,title.cex=.8)

Past versions of "plot motifs-3.png"

Version	Author	Date
d5076c9	reneeisnowhere	2023-06-21

Viewing the motifs, the following groups were named:

• Motif 1: No Response (n = 7409)

• Motif 2: Top2 inhibitor response, Time-independent

  • (Time-independent response, n = 589)

• Motif 3: Top2 inhibitor response, Early

  • (Early response, n = 487)

• Motif 4: Top2 inhibitor response, Late

  • (Late response, n = 5596)

motif_prob <- cormotif_initial$bestmotif$clustlike
clust1 <- motif_prob %>%
  as.data.frame() %>%
  filter(V1>0.5) %>% 
  rownames
clust2 <- motif_prob %>%
  as.data.frame() %>%
  filter(V2>0.5) %>% 
  rownames
clust3 <- motif_prob %>%
  as.data.frame() %>%
  filter(V3>0.5) %>% 
  rownames
clust4 <- motif_prob %>%
  as.data.frame() %>%
  filter(V4>0.5) %>% 
  rownames

backGL <- read.csv("data/backGL.txt")  ##14084
length(setdiff(backGL$ENTREZID,(union(clust1,union(clust2,union(clust3,clust4))))))

[1] 10571

##63 genes not used overall  same as (14084-7504-528-444-5545)

Pie Chart of overall numbers

##label computation
pie_chartdata <- pie_chartdata %>% 
   mutate(prop = gene_num / sum(pie_chartdata$gene_num) *100) %>%
  mutate(ypos = (prop)+ 0.5*prop )


pie_chartdata %>% 
  ggplot(.,aes(x="",y=gene_num, fill=Set))+
  geom_col(width =1) +
  coord_polar("y", pi/2)+
  theme_void()+
  ggtitle("Distribution of genes for each set")+
  geom_text(aes(label = paste0(Set," (",gene_num,")")),
                position = position_stack(vjust =.45)) +
  theme(legend.position="none") +
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5))

Past versions of "pie using ggplot-1.png"

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25

The genes belonging to each set were identified by the following:

motif 1- No Response set: 7504 (gene list made by filtering likelihood of gene belonging to cluster 1 >0.5)

motif 2- Time-independent Top2i response cluster: 528 (gene list made by filtering likelihood of gene belonging to cluster 2 >0.5)

motif 3- Early Top2i response cluster: 444 (gene list made by filtering likelihood of gene belonging to cluster 3 >0.5)

motif 4- Late Top2i response cluster: 5545 (gene list made by filtering likelihood of gene belonging to cluster 4 >0.5)

There was overlap between the previous sets and the new sets, so I moved on expecting similar responses in the GO analysis. I did subset out the genes not used overall from the background gene list (rowmeans>0 from log(cpm(count matrix))) ## GO and KEGG of each set

DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif, envir=.GlobalEnv)

<environment: R_GlobalEnv>

label_list <- readRDS("data/label_list.RDS")
list2env(label_list, envir=.GlobalEnv)

<environment: R_GlobalEnv>

label <- (interaction(drug, indv, time))
 
backGL <- read.csv("data/backGL.txt")
#NRresp <- read_csv("data/cormotif_NRset.txt")

No response motif genes

GO:BP

# gostrescoNR <- gost(query = motif_NR ,
#                      organism = "hsapiens",
#                        ordered_query = FALSE,
#                        domain_scope = "custom",
#                        measure_underrepresentation = FALSE,
#                        evcodes = FALSE,
#                        user_threshold = 0.05,
#                        correction_method = c("fdr"),
#                        custom_bg = backGL$ENTREZID,
#                        sources=c("GO:BP", "KEGG"))

# saveRDS(gostrescoNR, "data/gostrescoNR.")
gostrescoNR <- readRDS("data/gostrescoNR")
cormotifNRcluster <- gostplot(gostrescoNR, capped = FALSE, interactive = TRUE)
cormotifNRcluster

# (gostres$result$p_value)
tableNR <- gostrescoNR$result %>%
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value)) 
tableNR%>% 
  mutate_at(.cols = 6, .funs= scales::label_scientific(digits=4)) %>% 
  kable(.,) %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

source	term_id	term_name	intersection_size	term_size	p_value
GO:BP	GO:0002181	cytoplasmic translation	117	156	1.507e-05
GO:BP	GO:0042773	ATP synthesis coupled electron transport	68	82	1.507e-05
GO:BP	GO:0042775	mitochondrial ATP synthesis coupled electron transport	68	82	1.507e-05
GO:BP	GO:0019646	aerobic electron transport chain	63	74	1.507e-05
GO:BP	GO:0009060	aerobic respiration	125	168	1.507e-05
GO:BP	GO:0006119	oxidative phosphorylation	96	122	1.507e-05
GO:BP	GO:0022904	respiratory electron transport chain	83	104	1.507e-05
GO:BP	GO:0045333	cellular respiration	150	211	4.426e-05
GO:BP	GO:1901566	organonitrogen compound biosynthetic process	872	1484	4.539e-04
GO:BP	GO:0009100	glycoprotein metabolic process	209	316	7.784e-04
GO:BP	GO:0006518	peptide metabolic process	469	771	1.771e-03
GO:BP	GO:0043603	amide metabolic process	593	993	2.042e-03
GO:BP	GO:0022900	electron transport chain	102	142	2.042e-03
GO:BP	GO:0006412	translation	396	646	2.783e-03
GO:BP	GO:0006486	protein glycosylation	129	187	2.783e-03
GO:BP	GO:0043413	macromolecule glycosylation	129	187	2.783e-03
GO:BP	GO:0070085	glycosylation	138	202	2.783e-03
GO:BP	GO:0033108	mitochondrial respiratory chain complex assembly	71	94	2.799e-03
GO:BP	GO:0015980	energy derivation by oxidation of organic compounds	189	288	3.008e-03
GO:BP	GO:1901564	organonitrogen compound metabolic process	2733	4955	3.325e-03
GO:BP	GO:0022613	ribonucleoprotein complex biogenesis	283	453	6.889e-03
GO:BP	GO:0043043	peptide biosynthetic process	403	666	9.904e-03
GO:BP	GO:0043604	amide biosynthetic process	464	775	1.033e-02
GO:BP	GO:0019538	protein metabolic process	2342	4241	1.532e-02
GO:BP	GO:0015986	proton motive force-driven ATP synthesis	51	66	1.576e-02
GO:BP	GO:0009101	glycoprotein biosynthetic process	164	252	1.831e-02
GO:BP	GO:0006487	protein N-linked glycosylation	50	65	2.164e-02
GO:BP	GO:0006754	ATP biosynthetic process	64	87	2.244e-02
GO:BP	GO:0010257	NADH dehydrogenase complex assembly	43	55	3.368e-02
GO:BP	GO:0032981	mitochondrial respiratory chain complex I assembly	43	55	3.368e-02
GO:BP	GO:0006091	generation of precursor metabolites and energy	230	369	3.697e-02
GO:BP	GO:0042776	proton motive force-driven mitochondrial ATP synthesis	44	57	4.441e-02
GO:BP	GO:0046034	ATP metabolic process	81	116	4.441e-02
GO:BP	GO:0042254	ribosome biogenesis	190	301	4.978e-02
KEGG	KEGG:05171	Coronavirus disease - COVID-19	122	162	7.225e-07
KEGG	KEGG:03010	Ribosome	98	127	1.551e-06
KEGG	KEGG:05208	Chemical carcinogenesis - reactive oxygen species	133	186	1.136e-05
KEGG	KEGG:04510	Focal adhesion	127	177	1.198e-05
KEGG	KEGG:00190	Oxidative phosphorylation	81	106	2.532e-05
KEGG	KEGG:05012	Parkinson disease	153	226	1.368e-04
KEGG	KEGG:04512	ECM-receptor interaction	55	69	1.368e-04
KEGG	KEGG:04714	Thermogenesis	134	195	1.395e-04
KEGG	KEGG:05020	Prion disease	148	220	2.532e-04
KEGG	KEGG:05415	Diabetic cardiomyopathy	117	169	2.653e-04
KEGG	KEGG:04141	Protein processing in endoplasmic reticulum	110	161	1.020e-03
KEGG	KEGG:00531	Glycosaminoglycan degradation	16	16	1.020e-03
KEGG	KEGG:05016	Huntington disease	165	256	2.083e-03
KEGG	KEGG:05010	Alzheimer disease	197	315	5.425e-03
KEGG	KEGG:00513	Various types of N-glycan biosynthesis	29	36	1.088e-02
KEGG	KEGG:04932	Non-alcoholic fatty liver disease	88	131	1.088e-02
KEGG	KEGG:04142	Lysosome	80	118	1.175e-02
KEGG	KEGG:00510	N-Glycan biosynthesis	36	48	2.410e-02
KEGG	KEGG:04810	Regulation of actin cytoskeleton	114	179	3.226e-02
KEGG	KEGG:01200	Carbon metabolism	66	98	3.806e-02
KEGG	KEGG:03040	Spliceosome	86	132	3.945e-02
KEGG	KEGG:05022	Pathways of neurodegeneration - multiple diseases	230	385	4.301e-02

  write.csv(tableNR,"output/tableNR.csv")
##GO:BP  
  tableNR %>% dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value),col=intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    ggtitle('No response enriched GO:BP terms') +
   xlab(expression("-log"[10]~"(p-value)"))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ylab("GO: BP term")+
    scale_y_discrete(labels = scales::label_wrap(30))+
      theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "noresponse data motif1-2.png"

Version	Author	Date
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

##kegg
  
  tableNR %>% 
    dplyr::filter(source!="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=15 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, 
                 y =reorder(term_name,p_value),
                 col=intersection_size)) +
    geom_point(aes(size = intersection_size, col="red")) +
    ggtitle('No response enriched KEGG terms') +
    scale_y_discrete(labels = scales::label_wrap(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    xlab(expression("-log"[10]~"(p-value)"))+
    ylab("KEGG term")+
      theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "noresponse data motif1-3.png"

Version	Author	Date
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

Late response Top2\(\beta\) inhibitor motif genes

GO:BP/KEGG

# gostresTop2bi_LR <- gost(query = c(motif_LR),
#                       organism = "hsapiens",
#                       ordered_query = FALSE,
#                       domain_scope = "custom",
#                       measure_underrepresentation = FALSE,
#                       evcodes = FALSE,
#                       user_threshold = 0.05,
#                       correction_method = c("fdr"),
#                       custom_bg = backGL$ENTREZID,
#                       sources=c("GO:BP", "KEGG"))
# saveRDS(gostresTop2bi_LR,"data/gostresTop2bi_LR.RDS")
gostresTop2bi_LR <- readRDS("data/gostresTop2bi_LR.RDS")
cormotifrespTop2bi_LR <- gostplot(gostresTop2bi_LR, capped = FALSE, interactive = TRUE)
cormotifrespTop2bi_LR

tabletop2Bi_LR <- gostresTop2bi_LR$result %>%
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value)) 
  
tabletop2Bi_LR%>% 
  mutate_at(.cols = 6, .funs= scales::label_scientific(digits=4)) %>% 
  kable(.,) %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

source	term_id	term_name	intersection_size	term_size	p_value
GO:BP	GO:0007059	chromosome segregation	200	369	2.598e-05
GO:BP	GO:0000280	nuclear division	185	344	1.136e-04
GO:BP	GO:0051301	cell division	286	570	1.287e-04
GO:BP	GO:0007049	cell cycle	694	1529	2.325e-04
GO:BP	GO:0006261	DNA-templated DNA replication	89	148	4.174e-04
GO:BP	GO:0022402	cell cycle process	502	1086	6.439e-04
GO:BP	GO:0098813	nuclear chromosome segregation	147	273	6.439e-04
GO:BP	GO:0061982	meiosis I cell cycle process	54	81	6.439e-04
GO:BP	GO:0007051	spindle organization	107	188	6.439e-04
GO:BP	GO:0140014	mitotic nuclear division	137	251	6.439e-04
GO:BP	GO:0048285	organelle fission	198	388	9.885e-04
GO:BP	GO:0000070	mitotic sister chromatid segregation	102	179	9.885e-04
GO:BP	GO:0007127	meiosis I	51	77	1.225e-03
GO:BP	GO:0000278	mitotic cell cycle	387	827	2.041e-03
GO:BP	GO:1903047	mitotic cell cycle process	329	694	2.774e-03
GO:BP	GO:0006260	DNA replication	135	255	3.497e-03
GO:BP	GO:0045132	meiotic chromosome segregation	43	64	3.592e-03
GO:BP	GO:0140013	meiotic nuclear division	71	120	4.267e-03
GO:BP	GO:0000226	microtubule cytoskeleton organization	266	552	4.267e-03
GO:BP	GO:1903046	meiotic cell cycle process	78	135	5.053e-03
GO:BP	GO:0007017	microtubule-based process	353	760	8.645e-03
GO:BP	GO:2001251	negative regulation of chromosome organization	55	90	1.077e-02
GO:BP	GO:0000819	sister chromatid segregation	115	217	1.132e-02
GO:BP	GO:0045930	negative regulation of mitotic cell cycle	112	211	1.259e-02
GO:BP	GO:0051716	cellular response to stimulus	2050	4953	1.303e-02
GO:BP	GO:0032465	regulation of cytokinesis	49	79	1.380e-02
GO:BP	GO:0070925	organelle assembly	375	817	1.380e-02
GO:BP	GO:0051276	chromosome organization	258	543	1.380e-02
GO:BP	GO:0008654	phospholipid biosynthetic process	122	234	1.380e-02
GO:BP	GO:0045839	negative regulation of mitotic nuclear division	36	54	1.546e-02
GO:BP	GO:0046474	glycerophospholipid biosynthetic process	101	189	1.675e-02
GO:BP	GO:1902850	microtubule cytoskeleton organization involved in mitosis	86	157	1.761e-02
GO:BP	GO:0050896	response to stimulus	2369	5770	1.761e-02
GO:BP	GO:0045017	glycerolipid biosynthetic process	114	218	1.777e-02
GO:BP	GO:0033046	negative regulation of sister chromatid segregation	32	47	1.794e-02
GO:BP	GO:0033048	negative regulation of mitotic sister chromatid segregation	32	47	1.794e-02
GO:BP	GO:2000816	negative regulation of mitotic sister chromatid separation	32	47	1.794e-02
GO:BP	GO:0019692	deoxyribose phosphate metabolic process	26	36	1.798e-02
GO:BP	GO:0009262	deoxyribonucleotide metabolic process	26	36	1.798e-02
GO:BP	GO:0007093	mitotic cell cycle checkpoint signaling	77	139	2.123e-02
GO:BP	GO:0051784	negative regulation of nuclear division	37	57	2.123e-02
GO:BP	GO:0071173	spindle assembly checkpoint signaling	30	44	2.444e-02
GO:BP	GO:0071174	mitotic spindle checkpoint signaling	30	44	2.444e-02
GO:BP	GO:0007094	mitotic spindle assembly checkpoint signaling	30	44	2.444e-02
GO:BP	GO:0000075	cell cycle checkpoint signaling	97	183	2.444e-02
GO:BP	GO:0045841	negative regulation of mitotic metaphase/anaphase transition	31	46	2.546e-02
GO:BP	GO:1905819	negative regulation of chromosome separation	32	48	2.570e-02
GO:BP	GO:0051985	negative regulation of chromosome segregation	32	48	2.570e-02
GO:BP	GO:0045786	negative regulation of cell cycle	166	338	2.570e-02
GO:BP	GO:0006270	DNA replication initiation	25	35	2.570e-02
GO:BP	GO:0009394	2’-deoxyribonucleotide metabolic process	25	35	2.570e-02
GO:BP	GO:0033047	regulation of mitotic sister chromatid segregation	34	52	2.637e-02
GO:BP	GO:0051304	chromosome separation	46	76	2.918e-02
GO:BP	GO:0006996	organelle organization	1261	3000	3.028e-02
GO:BP	GO:0071417	cellular response to organonitrogen compound	229	485	3.122e-02
GO:BP	GO:0031577	spindle checkpoint signaling	30	45	3.655e-02
GO:BP	GO:0021537	telencephalon development	104	201	3.660e-02
GO:BP	GO:1902100	negative regulation of metaphase/anaphase transition of cell cycle	31	47	3.660e-02
GO:BP	GO:0007052	mitotic spindle organization	71	129	3.660e-02
GO:BP	GO:0045143	homologous chromosome segregation	24	34	4.009e-02
GO:BP	GO:0090407	organophosphate biosynthetic process	237	506	4.009e-02
GO:BP	GO:1905818	regulation of chromosome separation	43	71	4.021e-02
GO:BP	GO:1901653	cellular response to peptide	142	287	4.048e-02
GO:BP	GO:0051256	mitotic spindle midzone assembly	9	9	4.080e-02
GO:BP	GO:0010889	regulation of sequestering of triglyceride	11	12	4.632e-02
GO:BP	GO:0051255	spindle midzone assembly	11	12	4.632e-02
GO:BP	GO:1901699	cellular response to nitrogen compound	241	517	4.632e-02
GO:BP	GO:0010564	regulation of cell cycle process	287	626	4.989e-02
KEGG	KEGG:03030	DNA replication	25	35	2.693e-02
KEGG	KEGG:00230	Purine metabolism	56	97	2.693e-02

 write.csv(tabletop2Bi_LR,"output/tabletop2Bi_LR.csv") 
  
  tabletop2Bi_LR %>% dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,log_val), col= intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::label_wrap(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Late response enriched GO:BP terms') +
    xlab(expression("-log"[10]~"(p-value)"))+
    ylab("GO: BP term")+
      theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "Top2iLR motif set-2.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

 tabletop2Bi_LR %>% 
    dplyr::filter(source!="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=20 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value),col=intersection_size)) +
    geom_point(aes(size = intersection_size, col="red")) +
   scale_y_discrete(labels = scales::label_wrap(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Late response enriched KEGG terms') +
   xlab(expression("-log"[10]~"(p-value)"))+
    ylab("KEGG term")+
      theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "Top2iLR motif set-3.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

Early Response Top2 inhibitor motif genes

GO:BP/KEGG

# gostresTop2bi_ER <- gost(query = c(motif_ER),
#                       organism = "hsapiens",
#                       ordered_query = FALSE,
#                       domain_scope = "custom",
#                       measure_underrepresentation = FALSE,
#                       evcodes = FALSE,
#                       user_threshold = 0.05,
#                       correction_method = c("fdr"),
#                       custom_bg = backGL$ENTREZID,
#                       sources=c("GO:BP", "KEGG"))
# saveRDS(gostresTop2bi_ER, "data/gostresTop2bi_ER.RDS")

gostresTop2bi_ER <- readRDS("data/gostresTop2bi_ER.RDS")
cormotifrespTop2bi_ER <- gostplot(gostresTop2bi_ER, capped = FALSE, interactive = TRUE)

gostresTop2bi_ER$result %>% 
  mutate_at(.cols = 6, .funs= scales::label_scientific(digits=4)) %>% 
  kable(.,) %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

query	significant	p_value	term_size	query_size	intersection_size	precision	recall	term_id	source	term_name	effective_domain_size	source_order	parents
query_1	TRUE	0.0000000	2684	428	2.16e+02	0.5046729	0.0804769	GO:0097659	GO:BP	nucleic acid-templated transcription	13586	19768	GO:00104….
query_1	TRUE	0.0000000	2683	428	2.16e+02	0.5046729	0.0805069	GO:0006351	GO:BP	DNA-templated transcription	13586	2176	GO:0097659
query_1	TRUE	0.0000000	2714	428	2.16e+02	0.5046729	0.0795873	GO:0032774	GO:BP	RNA biosynthetic process	13586	8240	GO:00090….
query_1	TRUE	0.0000000	1995	428	1.83e+02	0.4275701	0.0917293	GO:0006366	GO:BP	transcription by RNA polymerase II	13586	2189	GO:0006351
query_1	TRUE	0.0000000	2576	428	2.09e+02	0.4883178	0.0811335	GO:1903506	GO:BP	regulation of nucleic acid-templated transcription	13586	24157	GO:00976….
query_1	TRUE	0.0000000	2574	428	2.09e+02	0.4883178	0.0811966	GO:0006355	GO:BP	regulation of DNA-templated transcription	13586	2180	GO:00063….
query_1	TRUE	0.0000000	1914	428	1.78e+02	0.4158879	0.0929990	GO:0006357	GO:BP	regulation of transcription by RNA polymerase II	13586	2182	GO:00063….
query_1	TRUE	0.0000000	3080	428	2.30e+02	0.5373832	0.0746753	GO:0019219	GO:BP	regulation of nucleobase-containing compound metabolic process	13586	5897	GO:00061….
query_1	TRUE	0.0000000	2593	428	2.09e+02	0.4883178	0.0806016	GO:2001141	GO:BP	regulation of RNA biosynthetic process	13586	27647	GO:00105….
query_1	TRUE	0.0000000	2845	428	2.19e+02	0.5116822	0.0769772	GO:0051252	GO:BP	regulation of RNA metabolic process	13586	14368	GO:00160….
query_1	TRUE	0.0000000	4048	428	2.59e+02	0.6051402	0.0639822	GO:0090304	GO:BP	nucleic acid metabolic process	13586	19208	GO:00061….
query_1	TRUE	0.0000000	3012	428	2.19e+02	0.5116822	0.0727092	GO:0010556	GO:BP	regulation of macromolecule biosynthetic process	13586	4304	GO:00090….
query_1	TRUE	0.0000000	4174	428	2.63e+02	0.6144860	0.0630091	GO:0031323	GO:BP	regulation of cellular metabolic process	13586	7510	GO:00192….
query_1	TRUE	0.0000000	3600	428	2.41e+02	0.5630841	0.0669444	GO:0016070	GO:BP	RNA metabolic process	13586	5221	GO:0090304
query_1	TRUE	0.0000000	3070	428	2.19e+02	0.5116822	0.0713355	GO:0034654	GO:BP	nucleobase-containing compound biosynthetic process	13586	9171	GO:00061….
query_1	TRUE	0.0000000	3080	428	2.19e+02	0.5116822	0.0711039	GO:0031326	GO:BP	regulation of cellular biosynthetic process	13586	7513	GO:00098….
query_1	TRUE	0.0000000	3133	428	2.20e+02	0.5140187	0.0702202	GO:0019438	GO:BP	aromatic compound biosynthetic process	13586	6097	GO:00067….
query_1	TRUE	0.0000000	3132	428	2.20e+02	0.5140187	0.0702427	GO:0018130	GO:BP	heterocycle biosynthetic process	13586	5504	GO:00442….
query_1	TRUE	0.0000000	3169	428	2.20e+02	0.5140187	0.0694225	GO:0009889	GO:BP	regulation of biosynthetic process	13586	3827	GO:00090….
query_1	TRUE	0.0000000	3237	428	2.21e+02	0.5163551	0.0682731	GO:1901362	GO:BP	organic cyclic compound biosynthetic process	13586	22388	GO:19013….
query_1	TRUE	0.0000000	4292	428	2.60e+02	0.6074766	0.0605778	GO:0051171	GO:BP	regulation of nitrogen compound metabolic process	13586	14316	GO:00068….
query_1	TRUE	0.0000000	4417	428	2.64e+02	0.6168224	0.0597691	GO:0080090	GO:BP	regulation of primary metabolic process	13586	18790	GO:00192….
query_1	TRUE	0.0000000	3580	428	2.33e+02	0.5443925	0.0650838	GO:0010468	GO:BP	regulation of gene expression	13586	4251	GO:00104….
query_1	TRUE	0.0000000	3730	428	2.36e+02	0.5514019	0.0632708	GO:0009059	GO:BP	macromolecule biosynthetic process	13586	3311	GO:00431….
query_1	TRUE	0.0000000	4455	428	2.62e+02	0.6121495	0.0588103	GO:0006139	GO:BP	nucleobase-containing compound metabolic process	13586	2028	GO:00067….
query_1	TRUE	0.0000000	4617	428	2.66e+02	0.6214953	0.0576132	GO:0060255	GO:BP	regulation of macromolecule metabolic process	13586	15305	GO:00192….
query_1	TRUE	0.0000000	4570	428	2.63e+02	0.6144860	0.0575492	GO:0046483	GO:BP	heterocycle metabolic process	13586	12897	GO:0044237
query_1	TRUE	0.0000000	4596	428	2.63e+02	0.6144860	0.0572237	GO:0006725	GO:BP	cellular aromatic compound metabolic process	13586	2484	GO:0044237
query_1	TRUE	0.0000000	3750	428	2.31e+02	0.5397196	0.0616000	GO:0044271	GO:BP	cellular nitrogen compound biosynthetic process	13586	11584	GO:00346….
query_1	TRUE	0.0000000	4747	428	2.65e+02	0.6191589	0.0558247	GO:1901360	GO:BP	organic cyclic compound metabolic process	13586	22386	GO:0071704
query_1	TRUE	0.0000000	5017	428	2.73e+02	0.6378505	0.0544150	GO:0019222	GO:BP	regulation of metabolic process	13586	5900	GO:00081….
query_1	TRUE	0.0000000	4587	428	2.58e+02	0.6028037	0.0562459	GO:0010467	GO:BP	gene expression	13586	4250	GO:0043170
query_1	TRUE	0.0000000	4245	428	2.45e+02	0.5724299	0.0577150	GO:0044249	GO:BP	cellular biosynthetic process	13586	11577	GO:00090….
query_1	TRUE	0.0000000	4946	428	2.67e+02	0.6238318	0.0539830	GO:0034641	GO:BP	cellular nitrogen compound metabolic process	13586	9164	GO:00068….
query_1	TRUE	0.0000000	4539	428	2.49e+02	0.5817757	0.0548579	GO:1901576	GO:BP	organic substance biosynthetic process	13586	22571	GO:00090….
query_1	TRUE	0.0000000	4590	428	2.49e+02	0.5817757	0.0542484	GO:0009058	GO:BP	biosynthetic process	13586	3310	GO:0008152
query_1	TRUE	0.0000000	7069	428	3.22e+02	0.7523364	0.0455510	GO:0043170	GO:BP	macromolecule metabolic process	13586	11154	GO:0071704
query_1	TRUE	0.0000000	6996	428	3.14e+02	0.7336449	0.0448828	GO:0044237	GO:BP	cellular metabolic process	13586	11571	GO:00081….
query_1	TRUE	0.0000000	7552	428	3.29e+02	0.7686916	0.0435646	GO:0050794	GO:BP	regulation of cellular process	13586	14008	GO:00099….
query_1	TRUE	0.0000000	1315	428	1.05e+02	0.2453271	0.0798479	GO:1903508	GO:BP	positive regulation of nucleic acid-templated transcription	13586	24159	GO:00976….
query_1	TRUE	0.0000000	1315	428	1.05e+02	0.2453271	0.0798479	GO:0045893	GO:BP	positive regulation of DNA-templated transcription	13586	12393	GO:00063….
query_1	TRUE	0.0000000	1441	428	1.11e+02	0.2593458	0.0770298	GO:0051254	GO:BP	positive regulation of RNA metabolic process	13586	14370	GO:00106….
query_1	TRUE	0.0000000	1322	428	1.05e+02	0.2453271	0.0794251	GO:1902680	GO:BP	positive regulation of RNA biosynthetic process	13586	23482	GO:00105….
query_1	TRUE	0.0000000	1602	428	1.16e+02	0.2710280	0.0724095	GO:0045935	GO:BP	positive regulation of nucleobase-containing compound metabolic process	13586	12431	GO:00061….
query_1	TRUE	0.0000000	7441	428	3.21e+02	0.7500000	0.0431394	GO:0006807	GO:BP	nitrogen compound metabolic process	13586	2543	GO:0008152
query_1	TRUE	0.0000000	1015	428	8.60e+01	0.2009346	0.0847291	GO:0045892	GO:BP	negative regulation of DNA-templated transcription	13586	12392	GO:00063….
query_1	TRUE	0.0000000	7806	428	3.29e+02	0.7686916	0.0421471	GO:0044238	GO:BP	primary metabolic process	13586	11572	GO:0008152
query_1	TRUE	0.0000000	1017	428	8.60e+01	0.2009346	0.0845624	GO:1903507	GO:BP	negative regulation of nucleic acid-templated transcription	13586	24158	GO:00976….
query_1	TRUE	0.0000000	1026	428	8.60e+01	0.2009346	0.0838207	GO:1902679	GO:BP	negative regulation of RNA biosynthetic process	13586	23481	GO:00105….
query_1	TRUE	0.0000000	1507	428	1.09e+02	0.2546729	0.0723291	GO:0010557	GO:BP	positive regulation of macromolecule biosynthetic process	13586	4305	GO:00090….
query_1	TRUE	0.0000000	8061	428	3.35e+02	0.7827103	0.0415581	GO:0050789	GO:BP	regulation of biological process	13586	14004	GO:00081….
query_1	TRUE	0.0000000	1119	428	9.00e+01	0.2102804	0.0804290	GO:0051253	GO:BP	negative regulation of RNA metabolic process	13586	14369	GO:00106….
query_1	TRUE	0.0000000	1209	428	9.40e+01	0.2196262	0.0777502	GO:0045934	GO:BP	negative regulation of nucleobase-containing compound metabolic process	13586	12430	GO:00061….
query_1	TRUE	0.0000000	8320	428	3.40e+02	0.7943925	0.0408654	GO:0065007	GO:BP	biological regulation	13586	16811	GO:0008150
query_1	TRUE	0.0000000	1564	428	1.09e+02	0.2546729	0.0696931	GO:0031328	GO:BP	positive regulation of cellular biosynthetic process	13586	7515	GO:00098….
query_1	TRUE	0.0000000	937	428	7.90e+01	0.1845794	0.0843116	GO:0045944	GO:BP	positive regulation of transcription by RNA polymerase II	13586	12439	GO:00063….
query_1	TRUE	0.0000000	1735	428	1.16e+02	0.2710280	0.0668588	GO:0031324	GO:BP	negative regulation of cellular metabolic process	13586	7511	GO:00098….
query_1	TRUE	0.0000000	8169	428	3.34e+02	0.7803738	0.0408863	GO:0071704	GO:BP	organic substance metabolic process	13586	17822	GO:0008152
query_1	TRUE	0.0000000	1604	428	1.09e+02	0.2546729	0.0679551	GO:0009891	GO:BP	positive regulation of biosynthetic process	13586	3829	GO:00090….
query_1	TRUE	0.0000000	1214	428	9.10e+01	0.2126168	0.0749588	GO:0010558	GO:BP	negative regulation of macromolecule biosynthetic process	13586	4306	GO:00090….
query_1	TRUE	0.0000000	1239	428	9.10e+01	0.2126168	0.0734463	GO:0031327	GO:BP	negative regulation of cellular biosynthetic process	13586	7514	GO:00098….
query_1	TRUE	0.0000000	2412	428	1.40e+02	0.3271028	0.0580431	GO:0051173	GO:BP	positive regulation of nitrogen compound metabolic process	13586	14318	GO:00068….
query_1	TRUE	0.0000000	740	428	6.50e+01	0.1518692	0.0878378	GO:0000122	GO:BP	negative regulation of transcription by RNA polymerase II	13586	51	GO:00063….
query_1	TRUE	0.0000000	1280	428	9.10e+01	0.2126168	0.0710938	GO:0009890	GO:BP	negative regulation of biosynthetic process	13586	3828	GO:00090….
query_1	TRUE	0.0000000	2646	428	1.47e+02	0.3434579	0.0555556	GO:0010604	GO:BP	positive regulation of macromolecule metabolic process	13586	4347	GO:00098….
query_1	TRUE	0.0000000	2270	428	1.31e+02	0.3060748	0.0577093	GO:0031325	GO:BP	positive regulation of cellular metabolic process	13586	7512	GO:00098….
query_1	TRUE	0.0000000	8518	428	3.36e+02	0.7850467	0.0394459	GO:0008152	GO:BP	metabolic process	13586	3197	GO:0008150
query_1	TRUE	0.0000000	2293	428	1.29e+02	0.3014019	0.0562582	GO:0009892	GO:BP	negative regulation of metabolic process	13586	3830	GO:00081….
query_1	TRUE	0.0000000	1849	428	1.11e+02	0.2593458	0.0600324	GO:0051172	GO:BP	negative regulation of nitrogen compound metabolic process	13586	14317	GO:00068….
query_1	TRUE	0.0000000	2135	428	1.22e+02	0.2850467	0.0571429	GO:0010605	GO:BP	negative regulation of macromolecule metabolic process	13586	4348	GO:00098….
query_1	TRUE	0.0000000	2880	428	1.49e+02	0.3481308	0.0517361	GO:0009893	GO:BP	positive regulation of metabolic process	13586	3831	GO:00081….
query_1	TRUE	0.0000000	3625	428	1.75e+02	0.4088785	0.0482759	GO:0048523	GO:BP	negative regulation of cellular process	13586	13544	GO:00099….
query_1	TRUE	0.0000000	4045	428	1.86e+02	0.4345794	0.0459827	GO:0048519	GO:BP	negative regulation of biological process	13586	13540	GO:00081….
query_1	TRUE	0.0000001	551	428	4.60e+01	0.1074766	0.0834846	GO:0006325	GO:BP	chromatin organization	13586	2169	GO:0016043
query_1	TRUE	0.0000002	378	428	3.60e+01	0.0841121	0.0952381	GO:0006338	GO:BP	chromatin remodeling	13586	2173	GO:0006325
query_1	TRUE	0.0000397	4099	428	1.76e+02	0.4112150	0.0429373	GO:0048522	GO:BP	positive regulation of cellular process	13586	13543	GO:00099….
query_1	TRUE	0.0001518	433	428	3.30e+01	0.0771028	0.0762125	GO:0016570	GO:BP	histone modification	13586	5382	GO:0036211
query_1	TRUE	0.0001867	4579	428	1.89e+02	0.4415888	0.0412754	GO:0048518	GO:BP	positive regulation of biological process	13586	13539	GO:00081….
query_1	TRUE	0.0015558	1835	428	8.80e+01	0.2056075	0.0479564	GO:0050793	GO:BP	regulation of developmental process	13586	14007	GO:00325….
query_1	TRUE	0.0044985	117	428	1.30e+01	0.0303738	0.1111111	GO:0016571	GO:BP	histone methylation	13586	5383	GO:00064….
query_1	TRUE	0.0057725	104	428	1.20e+01	0.0280374	0.1153846	GO:0018022	GO:BP	peptidyl-lysine methylation	13586	5461	GO:00064….
query_1	TRUE	0.0059120	89	428	1.10e+01	0.0257009	0.1235955	GO:0034968	GO:BP	histone lysine methylation	13586	9215	GO:00165….
query_1	TRUE	0.0066377	4	428	3.00e+00	0.0070093	0.7500000	GO:0097676	GO:BP	histone H3-K36 dimethylation	13586	19769	GO:00104….
query_1	TRUE	0.0077228	785	428	4.40e+01	0.1028037	0.0560510	GO:0006974	GO:BP	DNA damage response	13586	2659	GO:0033554
query_1	TRUE	0.0088927	336	428	2.40e+01	0.0560748	0.0714286	GO:0018205	GO:BP	peptidyl-lysine modification	13586	5568	GO:0018193
query_1	TRUE	0.0097655	18	428	5.00e+00	0.0116822	0.2777778	GO:0006607	GO:BP	NLS-bearing protein import into nucleus	13586	2378	GO:0006606
query_1	TRUE	0.0117205	850	428	4.60e+01	0.1074766	0.0541176	GO:0060429	GO:BP	epithelium development	13586	15465	GO:0009888
query_1	TRUE	0.0145341	665	428	3.80e+01	0.0887850	0.0571429	GO:0016071	GO:BP	mRNA metabolic process	13586	5222	GO:0016070
query_1	TRUE	0.0146171	884	428	4.70e+01	0.1098131	0.0531674	GO:0072359	GO:BP	circulatory system development	13586	18343	GO:0048731
query_1	TRUE	0.0149450	5	428	3.00e+00	0.0070093	0.6000000	GO:0010452	GO:BP	histone H3-K36 methylation	13586	4237	GO:0034968
query_1	TRUE	0.0154985	227	428	1.80e+01	0.0420561	0.0792952	GO:0030522	GO:BP	intracellular receptor signaling pathway	13586	7201	GO:0007165
query_1	TRUE	0.0168245	153	428	1.40e+01	0.0327103	0.0915033	GO:0040029	GO:BP	epigenetic regulation of gene expression	13586	10481	GO:00063….
query_1	TRUE	0.0178521	11336	428	3.82e+02	0.8925234	0.0336980	GO:0009987	GO:BP	cellular process	13586	3889	GO:0008150
query_1	TRUE	0.0245270	178	428	1.50e+01	0.0350467	0.0842697	GO:0090596	GO:BP	sensory organ morphogenesis	13586	19390	GO:00074….
query_1	TRUE	0.0245270	1425	428	6.70e+01	0.1565421	0.0470175	GO:0009888	GO:BP	tissue development	13586	3826	GO:0048856
query_1	TRUE	0.0251445	160	428	1.40e+01	0.0327103	0.0875000	GO:0006479	GO:BP	protein methylation	13586	2267	GO:00082….
query_1	TRUE	0.0251445	160	428	1.40e+01	0.0327103	0.0875000	GO:0008213	GO:BP	protein alkylation	13586	3212	GO:0036211
query_1	TRUE	0.0253959	279	428	2.00e+01	0.0467290	0.0716846	GO:1903706	GO:BP	regulation of hemopoiesis	13586	24346	GO:00026….
query_1	TRUE	0.0260534	199	428	1.60e+01	0.0373832	0.0804020	GO:1902105	GO:BP	regulation of leukocyte differentiation	13586	23039	GO:00025….
query_1	TRUE	0.0262717	6	428	3.00e+00	0.0070093	0.5000000	GO:0086023	GO:BP	adenylate cyclase-activating adrenergic receptor signaling pathway involved in heart process	13586	18890	GO:00718….
query_1	TRUE	0.0287928	504	428	3.00e+01	0.0700935	0.0595238	GO:0048729	GO:BP	tissue morphogenesis	13586	13726	GO:00096….
query_1	TRUE	0.0304015	506	428	3.00e+01	0.0700935	0.0592885	GO:0007507	GO:BP	heart development	13586	3064	GO:00485….
query_1	TRUE	0.0321858	165	428	1.40e+01	0.0327103	0.0848485	GO:0007623	GO:BP	circadian rhythm	13586	3152	GO:0048511
query_1	TRUE	0.0345089	534	428	3.10e+01	0.0724299	0.0580524	GO:0043009	GO:BP	chordate embryonic development	13586	11060	GO:0009792
query_1	TRUE	0.0347034	49	428	7.00e+00	0.0163551	0.1428571	GO:1902275	GO:BP	regulation of chromatin organization	13586	23191	GO:00063….
query_1	TRUE	0.0369747	149	428	1.30e+01	0.0303738	0.0872483	GO:0019827	GO:BP	stem cell population maintenance	13586	6381	GO:00325….
query_1	TRUE	0.0408860	7	428	3.00e+00	0.0070093	0.4285714	GO:1900246	GO:BP	positive regulation of RIG-I signaling pathway	13586	21445	GO:00395….
query_1	TRUE	0.0408860	151	428	1.30e+01	0.0303738	0.0860927	GO:0098727	GO:BP	maintenance of cell number	13586	19907	GO:0032502
query_1	TRUE	0.0408860	2	428	2.00e+00	0.0046729	1.0000000	GO:0032242	GO:BP	regulation of nucleoside transport	13586	7848	GO:00158….
query_1	TRUE	0.0408860	2	428	2.00e+00	0.0046729	1.0000000	GO:1901898	GO:BP	negative regulation of relaxation of cardiac muscle	13586	22853	GO:00551….
query_1	TRUE	0.0411487	15	428	4.00e+00	0.0093458	0.2666667	GO:0032239	GO:BP	regulation of nucleobase-containing compound transport	13586	7845	GO:00159….
query_1	TRUE	0.0449089	38	428	6.00e+00	0.0140187	0.1578947	GO:2001222	GO:BP	regulation of neuron migration	13586	27702	GO:00017….
query_1	TRUE	0.0490935	155	428	1.30e+01	0.0303738	0.0838710	GO:0045165	GO:BP	cell fate commitment	13586	11997	GO:00301….
query_1	TRUE	0.0490935	118	428	1.10e+01	0.0257009	0.0932203	GO:1903708	GO:BP	positive regulation of hemopoiesis	13586	24348	GO:00026….
query_1	TRUE	0.0490935	118	428	1.10e+01	0.0257009	0.0932203	GO:1902107	GO:BP	positive regulation of leukocyte differentiation	13586	23041	GO:00025….
query_1	TRUE	0.0000000	415	428	4.50e+01	0.1051402	0.1084337	KEGG:05168	KEGG	Herpes simplex virus 1 infection	13586	442	KEGG:00000

tabletop2Bi_ER <- gostresTop2bi_ER$result %>%
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value)) 


tabletop2Bi_ER %>% 
  mutate_at(.cols = 6, .funs= scales::label_scientific(digits=4)) %>% 
  kable(.,) %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

source	term_id	term_name	intersection_size	term_size	p_value
GO:BP	GO:0097659	nucleic acid-templated transcription	216	2684	4.831e-44
GO:BP	GO:0006351	DNA-templated transcription	216	2683	4.831e-44
GO:BP	GO:0032774	RNA biosynthetic process	216	2714	2.189e-43
GO:BP	GO:0006366	transcription by RNA polymerase II	183	1995	2.249e-43
GO:BP	GO:1903506	regulation of nucleic acid-templated transcription	209	2576	4.789e-43
GO:BP	GO:0006355	regulation of DNA-templated transcription	209	2574	4.789e-43
GO:BP	GO:0006357	regulation of transcription by RNA polymerase II	178	1914	5.740e-43
GO:BP	GO:0019219	regulation of nucleobase-containing compound metabolic process	230	3080	5.740e-43
GO:BP	GO:2001141	regulation of RNA biosynthetic process	209	2593	9.534e-43
GO:BP	GO:0051252	regulation of RNA metabolic process	219	2845	2.793e-42
GO:BP	GO:0090304	nucleic acid metabolic process	259	4048	3.333e-38
GO:BP	GO:0010556	regulation of macromolecule biosynthetic process	219	3012	3.797e-38
GO:BP	GO:0031323	regulation of cellular metabolic process	263	4174	5.752e-38
GO:BP	GO:0016070	RNA metabolic process	241	3600	1.603e-37
GO:BP	GO:0034654	nucleobase-containing compound biosynthetic process	219	3070	7.321e-37
GO:BP	GO:0031326	regulation of cellular biosynthetic process	219	3080	1.177e-36
GO:BP	GO:0019438	aromatic compound biosynthetic process	220	3133	4.702e-36
GO:BP	GO:0018130	heterocycle biosynthetic process	220	3132	4.702e-36
GO:BP	GO:0009889	regulation of biosynthetic process	220	3169	2.937e-35
GO:BP	GO:1901362	organic cyclic compound biosynthetic process	221	3237	2.510e-34
GO:BP	GO:0051171	regulation of nitrogen compound metabolic process	260	4292	3.891e-34
GO:BP	GO:0080090	regulation of primary metabolic process	264	4417	6.045e-34
GO:BP	GO:0010468	regulation of gene expression	233	3580	1.125e-33
GO:BP	GO:0009059	macromolecule biosynthetic process	236	3730	2.901e-32
GO:BP	GO:0006139	nucleobase-containing compound metabolic process	262	4455	3.237e-32
GO:BP	GO:0060255	regulation of macromolecule metabolic process	266	4617	2.141e-31
GO:BP	GO:0046483	heterocycle metabolic process	263	4570	1.083e-30
GO:BP	GO:0006725	cellular aromatic compound metabolic process	263	4596	3.013e-30
GO:BP	GO:0044271	cellular nitrogen compound biosynthetic process	231	3750	2.354e-29
GO:BP	GO:1901360	organic cyclic compound metabolic process	265	4747	1.106e-28
GO:BP	GO:0019222	regulation of metabolic process	273	5017	3.556e-28
GO:BP	GO:0010467	gene expression	258	4587	6.001e-28
GO:BP	GO:0044249	cellular biosynthetic process	245	4245	1.931e-27
GO:BP	GO:0034641	cellular nitrogen compound metabolic process	267	4946	1.932e-26
GO:BP	GO:1901576	organic substance biosynthetic process	249	4539	1.734e-24
GO:BP	GO:0009058	biosynthetic process	249	4590	1.070e-23
GO:BP	GO:0043170	macromolecule metabolic process	322	7069	1.384e-21
GO:BP	GO:0044237	cellular metabolic process	314	6996	5.668e-19
GO:BP	GO:0050794	regulation of cellular process	329	7552	1.274e-18
GO:BP	GO:1903508	positive regulation of nucleic acid-templated transcription	105	1315	7.760e-18
GO:BP	GO:0045893	positive regulation of DNA-templated transcription	105	1315	7.760e-18
GO:BP	GO:0051254	positive regulation of RNA metabolic process	111	1441	7.808e-18
GO:BP	GO:1902680	positive regulation of RNA biosynthetic process	105	1322	1.100e-17
GO:BP	GO:0045935	positive regulation of nucleobase-containing compound metabolic process	116	1602	1.150e-16
GO:BP	GO:0006807	nitrogen compound metabolic process	321	7441	1.263e-16
GO:BP	GO:0045892	negative regulation of DNA-templated transcription	86	1015	9.336e-16
GO:BP	GO:0044238	primary metabolic process	329	7806	1.011e-15
GO:BP	GO:1903507	negative regulation of nucleic acid-templated transcription	86	1017	1.011e-15
GO:BP	GO:1902679	negative regulation of RNA biosynthetic process	86	1026	1.712e-15
GO:BP	GO:0010557	positive regulation of macromolecule biosynthetic process	109	1507	1.770e-15
GO:BP	GO:0050789	regulation of biological process	335	8061	2.536e-15
GO:BP	GO:0051253	negative regulation of RNA metabolic process	90	1119	3.409e-15
GO:BP	GO:0045934	negative regulation of nucleobase-containing compound metabolic process	94	1209	5.154e-15
GO:BP	GO:0065007	biological regulation	340	8320	1.640e-14
GO:BP	GO:0031328	positive regulation of cellular biosynthetic process	109	1564	2.373e-14
GO:BP	GO:0045944	positive regulation of transcription by RNA polymerase II	79	937	2.887e-14
GO:BP	GO:0031324	negative regulation of cellular metabolic process	116	1735	4.001e-14
GO:BP	GO:0071704	organic substance metabolic process	334	8169	8.304e-14
GO:BP	GO:0009891	positive regulation of biosynthetic process	109	1604	1.326e-13
GO:BP	GO:0010558	negative regulation of macromolecule biosynthetic process	91	1214	1.594e-13
GO:BP	GO:0031327	negative regulation of cellular biosynthetic process	91	1239	5.461e-13
GO:BP	GO:0051173	positive regulation of nitrogen compound metabolic process	140	2412	1.626e-12
GO:BP	GO:0000122	negative regulation of transcription by RNA polymerase II	65	740	2.417e-12
GO:BP	GO:0009890	negative regulation of biosynthetic process	91	1280	3.689e-12
GO:BP	GO:0010604	positive regulation of macromolecule metabolic process	147	2646	9.659e-12
GO:BP	GO:0031325	positive regulation of cellular metabolic process	131	2270	2.536e-11
GO:BP	GO:0008152	metabolic process	336	8518	4.520e-11
GO:BP	GO:0009892	negative regulation of metabolic process	129	2293	2.638e-10
GO:BP	GO:0051172	negative regulation of nitrogen compound metabolic process	111	1849	2.688e-10
GO:BP	GO:0010605	negative regulation of macromolecule metabolic process	122	2135	4.514e-10
GO:BP	GO:0009893	positive regulation of metabolic process	149	2880	1.795e-09
GO:BP	GO:0048523	negative regulation of cellular process	175	3625	4.157e-09
GO:BP	GO:0048519	negative regulation of biological process	186	4045	4.792e-08
GO:BP	GO:0006325	chromatin organization	46	551	8.684e-08
GO:BP	GO:0006338	chromatin remodeling	36	378	1.915e-07
GO:BP	GO:0048522	positive regulation of cellular process	176	4099	3.968e-05
GO:BP	GO:0016570	histone modification	33	433	1.518e-04
GO:BP	GO:0048518	positive regulation of biological process	189	4579	1.867e-04
GO:BP	GO:0050793	regulation of developmental process	88	1835	1.556e-03
GO:BP	GO:0016571	histone methylation	13	117	4.499e-03
GO:BP	GO:0018022	peptidyl-lysine methylation	12	104	5.773e-03
GO:BP	GO:0034968	histone lysine methylation	11	89	5.912e-03
GO:BP	GO:0097676	histone H3-K36 dimethylation	3	4	6.638e-03
GO:BP	GO:0006974	DNA damage response	44	785	7.723e-03
GO:BP	GO:0018205	peptidyl-lysine modification	24	336	8.893e-03
GO:BP	GO:0006607	NLS-bearing protein import into nucleus	5	18	9.766e-03
GO:BP	GO:0060429	epithelium development	46	850	1.172e-02
GO:BP	GO:0016071	mRNA metabolic process	38	665	1.453e-02
GO:BP	GO:0072359	circulatory system development	47	884	1.462e-02
GO:BP	GO:0010452	histone H3-K36 methylation	3	5	1.495e-02
GO:BP	GO:0030522	intracellular receptor signaling pathway	18	227	1.550e-02
GO:BP	GO:0040029	epigenetic regulation of gene expression	14	153	1.682e-02
GO:BP	GO:0009987	cellular process	382	11336	1.785e-02
GO:BP	GO:0090596	sensory organ morphogenesis	15	178	2.453e-02
GO:BP	GO:0009888	tissue development	67	1425	2.453e-02
GO:BP	GO:0006479	protein methylation	14	160	2.514e-02
GO:BP	GO:0008213	protein alkylation	14	160	2.514e-02
GO:BP	GO:1903706	regulation of hemopoiesis	20	279	2.540e-02
GO:BP	GO:1902105	regulation of leukocyte differentiation	16	199	2.605e-02
GO:BP	GO:0086023	adenylate cyclase-activating adrenergic receptor signaling pathway involved in heart process	3	6	2.627e-02
GO:BP	GO:0048729	tissue morphogenesis	30	504	2.879e-02
GO:BP	GO:0007507	heart development	30	506	3.040e-02
GO:BP	GO:0007623	circadian rhythm	14	165	3.219e-02
GO:BP	GO:0043009	chordate embryonic development	31	534	3.451e-02
GO:BP	GO:1902275	regulation of chromatin organization	7	49	3.470e-02
GO:BP	GO:0019827	stem cell population maintenance	13	149	3.697e-02
GO:BP	GO:1900246	positive regulation of RIG-I signaling pathway	3	7	4.089e-02
GO:BP	GO:0098727	maintenance of cell number	13	151	4.089e-02
GO:BP	GO:0032242	regulation of nucleoside transport	2	2	4.089e-02
GO:BP	GO:1901898	negative regulation of relaxation of cardiac muscle	2	2	4.089e-02
GO:BP	GO:0032239	regulation of nucleobase-containing compound transport	4	15	4.115e-02
GO:BP	GO:2001222	regulation of neuron migration	6	38	4.491e-02
GO:BP	GO:0045165	cell fate commitment	13	155	4.909e-02
GO:BP	GO:1903708	positive regulation of hemopoiesis	11	118	4.909e-02
GO:BP	GO:1902107	positive regulation of leukocyte differentiation	11	118	4.909e-02
KEGG	KEGG:05168	Herpes simplex virus 1 infection	45	415	6.666e-11

  tabletop2Bi_ER %>% 
    dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=13 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value),col=intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::label_wrap(35))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Early acute response enriched GO:BP terms') +
    xlab(expression("-log"[10]~"(p-value)"))+
    ylab("GO: BP term")+
      theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 8, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "Top2iER set motif-1.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

tabletop2Bi_ER %>% 
    dplyr::filter(source!="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=15 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   ggplot(., aes(x = log_val,y = reorder(term_name,p_value),
                 col=intersection_size)) +
  geom_point(aes(size = intersection_size, col="red")) +
  scale_y_discrete(labels = scales::label_wrap(35))+
  guides(col="none", 
         size=guide_legend(title ="# of intersected \n terms"))+
  ggtitle('Early acute response enriched KEGG terms') +
 xlab(expression("-log"[10]~"(p-value)"))+
  ylab("KEGG term")+
  theme_bw()+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 9, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "Top2iER set motif-2.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

Time-independent Top2inhibitor motif genes

GO:BP/KEGG

# gostresTop2bi_TI <- gost(query = c(motif_TI),
#                       organism = "hsapiens",
#                       ordered_query = FALSE,
#                       domain_scope = "custom",
#                       measure_underrepresentation = FALSE,
#                       evcodes = FALSE,
#                       user_threshold = 0.05,
#                       correction_method = c("fdr"),
#                       custom_bg = backGL$ENTREZID,
#                       sources=c("GO:BP", "KEGG"))

 # saveRDS(gostresTop2bi_TI, "data/gostresTop2bi_TI.RDS")

gostresTop2bi_TI <- readRDS("data/gostresTop2bi_TI.RDS")
cormotifrespTop2bi_TI <- gostplot(gostresTop2bi_TI, capped = FALSE, interactive = TRUE)
cormotifrespTop2bi_TI

tabletop2Bi_TI <- gostresTop2bi_TI$result %>%
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value)) 
# write.csv(tabletop2Bi_TI,"output/tabletop2Bi_TI.csv")

tabletop2Bi_TI %>% 
  mutate_at(.cols = 6, .funs= scales::label_scientific(digits=4)) %>% 
  kable(.,) %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

source	term_id	term_name	intersection_size	term_size	p_value
GO:BP	GO:0006357	regulation of transcription by RNA polymerase II	179	1914	1.167e-29
GO:BP	GO:0006366	transcription by RNA polymerase II	181	1995	1.181e-28
GO:BP	GO:0006355	regulation of DNA-templated transcription	204	2574	2.150e-25
GO:BP	GO:1903506	regulation of nucleic acid-templated transcription	204	2576	2.150e-25
GO:BP	GO:0051252	regulation of RNA metabolic process	217	2845	2.150e-25
GO:BP	GO:2001141	regulation of RNA biosynthetic process	204	2593	3.638e-25
GO:BP	GO:0097659	nucleic acid-templated transcription	206	2684	3.793e-24
GO:BP	GO:0006351	DNA-templated transcription	206	2683	3.793e-24
GO:BP	GO:0032774	RNA biosynthetic process	206	2714	1.542e-23
GO:BP	GO:0019219	regulation of nucleobase-containing compound metabolic process	222	3080	6.127e-23
GO:BP	GO:0010556	regulation of macromolecule biosynthetic process	213	3012	1.591e-20
GO:BP	GO:0090304	nucleic acid metabolic process	258	4048	5.317e-20
GO:BP	GO:0031326	regulation of cellular biosynthetic process	214	3080	1.030e-19
GO:BP	GO:0009889	regulation of biosynthetic process	218	3169	1.059e-19
GO:BP	GO:0034654	nucleobase-containing compound biosynthetic process	213	3070	1.467e-19
GO:BP	GO:0019438	aromatic compound biosynthetic process	215	3133	2.990e-19
GO:BP	GO:0018130	heterocycle biosynthetic process	215	3132	2.990e-19
GO:BP	GO:0016070	RNA metabolic process	236	3600	3.111e-19
GO:BP	GO:0010468	regulation of gene expression	235	3580	3.256e-19
GO:BP	GO:1901362	organic cyclic compound biosynthetic process	217	3237	3.041e-18
GO:BP	GO:0051171	regulation of nitrogen compound metabolic process	260	4292	4.120e-17
GO:BP	GO:0080090	regulation of primary metabolic process	264	4417	1.085e-16
GO:BP	GO:0060255	regulation of macromolecule metabolic process	272	4617	1.317e-16
GO:BP	GO:0031323	regulation of cellular metabolic process	253	4174	1.876e-16
GO:BP	GO:0006139	nucleobase-containing compound metabolic process	262	4455	1.666e-15
GO:BP	GO:0046483	heterocycle metabolic process	265	4570	6.283e-15
GO:BP	GO:0006725	cellular aromatic compound metabolic process	265	4596	1.371e-14
GO:BP	GO:0019222	regulation of metabolic process	282	5017	1.456e-14
GO:BP	GO:1901360	organic cyclic compound metabolic process	268	4747	1.373e-13
GO:BP	GO:0009059	macromolecule biosynthetic process	224	3730	3.408e-13
GO:BP	GO:0051253	negative regulation of RNA metabolic process	98	1119	3.408e-13
GO:BP	GO:0044271	cellular nitrogen compound biosynthetic process	224	3750	6.248e-13
GO:BP	GO:0000122	negative regulation of transcription by RNA polymerase II	74	740	1.914e-12
GO:BP	GO:0045892	negative regulation of DNA-templated transcription	90	1015	2.605e-12
GO:BP	GO:1903507	negative regulation of nucleic acid-templated transcription	90	1017	2.846e-12
GO:BP	GO:1902679	negative regulation of RNA biosynthetic process	90	1026	4.674e-12
GO:BP	GO:0010467	gene expression	256	4587	5.204e-12
GO:BP	GO:0045934	negative regulation of nucleobase-containing compound metabolic process	100	1209	5.404e-12
GO:BP	GO:0034641	cellular nitrogen compound metabolic process	270	4946	7.415e-12
GO:BP	GO:0045944	positive regulation of transcription by RNA polymerase II	80	937	5.876e-10
GO:BP	GO:0031327	negative regulation of cellular biosynthetic process	96	1239	8.602e-10
GO:BP	GO:0044249	cellular biosynthetic process	234	4245	8.602e-10
GO:BP	GO:0010558	negative regulation of macromolecule biosynthetic process	94	1214	1.521e-09
GO:BP	GO:0009890	negative regulation of biosynthetic process	97	1280	2.241e-09
GO:BP	GO:0043170	macromolecule metabolic process	341	7069	5.242e-09
GO:BP	GO:0050789	regulation of biological process	375	8061	1.411e-08
GO:BP	GO:1901576	organic substance biosynthetic process	241	4539	1.699e-08
GO:BP	GO:0009058	biosynthetic process	243	4590	1.743e-08
GO:BP	GO:1903508	positive regulation of nucleic acid-templated transcription	96	1315	2.052e-08
GO:BP	GO:0045893	positive regulation of DNA-templated transcription	96	1315	2.052e-08
GO:BP	GO:1902680	positive regulation of RNA biosynthetic process	96	1322	2.687e-08
GO:BP	GO:0050794	regulation of cellular process	354	7552	6.948e-08
GO:BP	GO:0031324	negative regulation of cellular metabolic process	115	1735	8.154e-08
GO:BP	GO:0051254	positive regulation of RNA metabolic process	100	1441	1.216e-07
GO:BP	GO:0065007	biological regulation	379	8320	2.560e-07
GO:BP	GO:0010557	positive regulation of macromolecule biosynthetic process	101	1507	6.184e-07
GO:BP	GO:0051173	positive regulation of nitrogen compound metabolic process	143	2412	8.103e-07
GO:BP	GO:0031328	positive regulation of cellular biosynthetic process	103	1564	1.034e-06
GO:BP	GO:0051172	negative regulation of nitrogen compound metabolic process	116	1849	1.530e-06
GO:BP	GO:0045935	positive regulation of nucleobase-containing compound metabolic process	104	1602	1.778e-06
GO:BP	GO:0010604	positive regulation of macromolecule metabolic process	152	2646	1.826e-06
GO:BP	GO:0009891	positive regulation of biosynthetic process	104	1604	1.837e-06
GO:BP	GO:0010605	negative regulation of macromolecule metabolic process	128	2135	3.184e-06
GO:BP	GO:0009893	positive regulation of metabolic process	160	2880	6.139e-06
GO:BP	GO:0009892	negative regulation of metabolic process	134	2293	6.139e-06
GO:BP	GO:0031325	positive regulation of cellular metabolic process	133	2270	6.139e-06
GO:BP	GO:0006807	nitrogen compound metabolic process	340	7441	1.121e-05
GO:BP	GO:0044238	primary metabolic process	350	7806	5.451e-05
GO:BP	GO:0071704	organic substance metabolic process	362	8169	8.965e-05
GO:BP	GO:0044237	cellular metabolic process	319	6996	8.965e-05
GO:BP	GO:0003007	heart morphogenesis	24	216	1.824e-04
GO:BP	GO:0009952	anterior/posterior pattern specification	17	127	4.487e-04
GO:BP	GO:0045595	regulation of cell differentiation	72	1137	7.662e-04
GO:BP	GO:0048523	negative regulation of cellular process	181	3625	9.101e-04
GO:BP	GO:0048645	animal organ formation	10	51	1.259e-03
GO:BP	GO:0045596	negative regulation of cell differentiation	37	482	2.670e-03
GO:BP	GO:0140467	integrated stress response signaling	8	35	2.670e-03
GO:BP	GO:0060411	cardiac septum morphogenesis	11	67	2.686e-03
GO:BP	GO:0007389	pattern specification process	27	306	2.869e-03
GO:BP	GO:0008152	metabolic process	366	8518	3.170e-03
GO:BP	GO:0045597	positive regulation of cell differentiation	44	620	3.172e-03
GO:BP	GO:0060537	muscle tissue development	29	343	3.172e-03
GO:BP	GO:0014706	striated muscle tissue development	21	210	3.236e-03
GO:BP	GO:0048738	cardiac muscle tissue development	20	198	4.167e-03
GO:BP	GO:0060914	heart formation	7	29	5.075e-03
GO:BP	GO:0048518	positive regulation of biological process	214	4579	7.246e-03
GO:BP	GO:0003002	regionalization	24	273	7.258e-03
GO:BP	GO:2000026	regulation of multicellular organismal development	62	1014	7.566e-03
GO:BP	GO:0035914	skeletal muscle cell differentiation	9	52	7.737e-03
GO:BP	GO:0051094	positive regulation of developmental process	59	957	8.730e-03
GO:BP	GO:0048519	negative regulation of biological process	191	4045	1.183e-02
GO:BP	GO:0035880	embryonic nail plate morphogenesis	3	4	1.183e-02
GO:BP	GO:0021546	rhombomere development	3	4	1.183e-02
GO:BP	GO:0003151	outflow tract morphogenesis	10	68	1.281e-02
GO:BP	GO:0051093	negative regulation of developmental process	44	665	1.334e-02
GO:BP	GO:0007049	cell cycle	84	1529	1.667e-02
GO:BP	GO:0043009	chordate embryonic development	37	534	1.700e-02
GO:BP	GO:0051239	regulation of multicellular organismal process	106	2035	1.822e-02
GO:BP	GO:0050793	regulation of developmental process	97	1835	2.051e-02
GO:BP	GO:0001756	somitogenesis	8	48	2.121e-02
GO:BP	GO:0030336	negative regulation of cell migration	20	227	2.191e-02
GO:BP	GO:0048522	positive regulation of cellular process	191	4099	2.296e-02
GO:BP	GO:0051726	regulation of cell cycle	57	956	2.316e-02
GO:BP	GO:0060284	regulation of cell development	40	605	2.365e-02
GO:BP	GO:0051241	negative regulation of multicellular organismal process	47	748	2.370e-02
GO:BP	GO:0045598	regulation of fat cell differentiation	12	102	2.370e-02
GO:BP	GO:0035282	segmentation	10	75	2.530e-02
GO:BP	GO:0048483	autonomic nervous system development	6	28	2.601e-02
GO:BP	GO:0009792	embryo development ending in birth or egg hatching	37	551	2.744e-02
GO:BP	GO:0002294	CD4-positive, alpha-beta T cell differentiation involved in immune response	7	40	3.146e-02
GO:BP	GO:0060412	ventricular septum morphogenesis	7	40	3.146e-02
GO:BP	GO:0002293	alpha-beta T cell differentiation involved in immune response	7	40	3.146e-02
GO:BP	GO:0002287	alpha-beta T cell activation involved in immune response	7	40	3.146e-02
GO:BP	GO:0042093	T-helper cell differentiation	7	40	3.146e-02
GO:BP	GO:2000146	negative regulation of cell motility	20	236	3.158e-02
GO:BP	GO:0055017	cardiac muscle tissue growth	9	65	3.329e-02
GO:BP	GO:0045444	fat cell differentiation	17	187	3.493e-02
GO:BP	GO:0048486	parasympathetic nervous system development	4	12	3.493e-02
GO:BP	GO:0060429	epithelium development	51	850	3.493e-02
GO:BP	GO:0048729	tissue morphogenesis	34	504	3.788e-02
GO:BP	GO:0003206	cardiac chamber morphogenesis	12	109	3.788e-02
GO:BP	GO:0007517	muscle organ development	22	277	3.929e-02
GO:BP	GO:0002292	T cell differentiation involved in immune response	7	42	3.951e-02
GO:BP	GO:0007507	heart development	34	506	3.952e-02
GO:BP	GO:0060562	epithelial tube morphogenesis	22	278	4.020e-02
GO:BP	GO:0048568	embryonic organ development	24	316	4.247e-02
GO:BP	GO:1900744	regulation of p38MAPK cascade	6	32	4.660e-02
GO:BP	GO:0060840	artery development	10	83	4.660e-02
GO:BP	GO:0042127	regulation of cell population proliferation	64	1147	4.660e-02
GO:BP	GO:0003281	ventricular septum development	9	69	4.660e-02
GO:BP	GO:1902893	regulation of miRNA transcription	8	56	4.670e-02
GO:BP	GO:0040013	negative regulation of locomotion	21	265	4.839e-02
GO:BP	GO:0060038	cardiac muscle cell proliferation	7	44	4.861e-02
GO:BP	GO:0010628	positive regulation of gene expression	47	783	4.922e-02
GO:BP	GO:0030278	regulation of ossification	10	84	4.925e-02
GO:BP	GO:0014855	striated muscle cell proliferation	8	57	4.953e-02
GO:BP	GO:0045667	regulation of osteoblast differentiation	11	99	4.953e-02
GO:BP	GO:0061614	miRNA transcription	8	57	4.953e-02
GO:BP	GO:0040007	growth	45	742	4.953e-02
KEGG	KEGG:05168	Herpes simplex virus 1 infection	47	415	3.765e-09
KEGG	KEGG:04115	p53 signaling pathway	11	65	3.175e-03
KEGG	KEGG:05217	Basal cell carcinoma	9	49	5.764e-03
KEGG	KEGG:05224	Breast cancer	14	117	5.917e-03
KEGG	KEGG:05225	Hepatocellular carcinoma	16	145	5.917e-03
KEGG	KEGG:05226	Gastric cancer	13	117	1.757e-02
KEGG	KEGG:05202	Transcriptional misregulation in cancer	13	128	3.526e-02

  tabletop2Bi_TI %>% 
    dplyr::filter(source =="GO:BP") %>%
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=13 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value),col=intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::label_wrap(35))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Early sustained response enriched GO:BP terms') +
    xlab(expression("-log"[10]~"(p-value)"))+
    #scale_x_continuous(expand = expansion(mult = .2))+
    ylab("GO: BP term")+
     theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 8, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "time independent-2.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

  tabletop2Bi_TI %>% 
    dplyr::filter(source =="KEGG") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=15 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value),col=intersection_size)) +
    geom_point(aes(size = intersection_size, col="red")) +
  scale_y_discrete(labels = scales::label_wrap(35))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Early sustained response KEGG terms') +
   xlab(expression("-log"[10]~"(p-value)"))+
    #scale_x_continuous(expand = expansion(mult = .2))+
    ylab("KEGG term")+
      theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 9, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "time independent-3.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

long GO:BP frame

motifcol <- c("#F8766D", "#00BFC4","#7CAE00",  "#C77CFF")
NR1f <- tableNR %>% dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%mutate(order_val=rev(as.numeric(rownames(.))))

LR3f <- tabletop2Bi_LR %>% dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=9 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value))  %>%  mutate(order_val=rev(as.numeric(rownames(.))))


TI3f <- tabletop2Bi_TI %>% 
    dplyr::filter(source =="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value))  %>% mutate(order_val=rev(as.numeric(rownames(.))))

ER3f <- tabletop2Bi_ER %>% 
    dplyr::filter(source =="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value))  %>% mutate(order_val=rev(as.numeric(rownames(.))))
# motif_list_GO <- list(ER3f,TI3f,LR3f,NR1f)
# names(motif_list_GO) <- c("ER3f","TI3f","LR3f","NR1f")
# saveRDS(motif_list_GO,"output/supplementary_motif_list_GO.RDS")

GOmotiflong <-list("EAR"=ER3f,"ESR"=TI3f,"LR"=LR3f, "NR"=NR1f)
GOBPlong <- data.table::rbindlist(GOmotiflong, idcol = "motif")
p <- GOBPlong %>% mutate(motif=factor(motif,levels=c("EAR","ESR","LR","NR"))) %>% 
  
ggplot(., aes(x = log_val, y=reorder(term_name, order_val, desc=FALSE),col=motif)) +
    geom_point(aes(size = intersection_size, col=motif)) +
  scale_y_discrete(labels = scales::label_wrap(40))+
  facet_grid(motif~., scales = "free_y")+
  scale_color_discrete(type=motifcol)+
  
    guides(col=guide_legend(title="Motif group"), size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Top 10 enriched GO:BP terms for each motif') +
     xlab(expression("-log"[10]~"(p-value)"))+
    #scale_x_continuous(expand = expansion(mult = .2))+
    ylab("GO:BP term")+
      theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 8, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))


#https://stackoverflow.com/questions/41631806/change-facet-label-text-and-background-colour/60046113#60046113
g <- ggplot_gtable(ggplot_build(p))
stripr <- which(grepl('strip-r', g$layout$name))
fills <- c("#F8766D", "#00BFC4","#7CAE00",  "#C77CFF")
k <- 1
for (i in stripr) {
  j <- which(grepl('rect', g$grobs[[i]]$grobs[[1]]$childrenOrder))
  g$grobs[[i]]$grobs[[1]]$children[[j]]$gp$fill <- fills[k]
  k <- k+1
}
#print(grid::grid.draw(g))
grid.draw(g)

Past versions of "all go in one loooooong frame-1.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

#hex <- hue_pal()(4)

#hex
#> hex (red/green/blue/purple default in ggplot)
#[1] "#F8766D" "#7CAE00" "#00BFC4" "#C77CFF"
#p

long go KEGG frame

LR3fk <- tabletop2Bi_LR %>% dplyr::filter(source=="KEGG") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value))


TI3fk <- tabletop2Bi_TI %>% 
    dplyr::filter(source =="KEGG") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) 

ER3fk <- tabletop2Bi_ER %>% 
    dplyr::filter(source =="KEGG") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) 

KEGGmotiflong <-list("ER"=ER3fk,"LR"=LR3fk,"TI"=TI3fk)
KEGGlong <- data.table::rbindlist(KEGGmotiflong, idcol = "motif")
KEGGlong %>% group_by(motif) %>% 
ggplot(., aes(x = log_val, y =reorder(term_name,p_value,desc=FALSE),col=motif)) +
    geom_point(aes(size = intersection_size, col=motif)) +
  scale_y_discrete(limits=rev,labels = scales::label_wrap(30))+
    guides(col=guide_legend(title="Motif group"), size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('Top enriched KEGG terms for each motif') +
    xlab(expression("-log"[10]~"(p-value)"))+
    #scale_x_continuous(expand = expansion(mult = .2))+
    ylab("KEGG term")+
      theme_bw()+
 theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 9, color = "black", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "kegg break-1.png"

Version	Author	Date
d0f459b	reneeisnowhere	2023-04-18
9e37491	reneeisnowhere	2023-04-16

Example of genes from each cluster

Motif_NR

drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")

drug_pal <- c("#41B333","#8B006D","#DF707E","#F1B72B", "#3386DD","#707031")
#fills <- c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF")

DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)

<environment: R_GlobalEnv>

set.seed(12345)

geneexpressionsets <- c( "27102" , "92312" , "10360",  "388558")

cpmcounts <- readRDS("data/cpmcount.RDS")

cpmcounts %>% dplyr::filter(rownames(.)==geneexpressionsets[1]) %>%
  pivot_longer(everything(), names_to = "treatment",values_to = "counts") %>%
  mutate(drug=rep(c("DNR","DOX","EPI","MTX","TRZ", "VEH"),12)) %>%
    mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time = rep((rep(c("3h", "24h"), c(6,6))), 6)) %>%
  mutate(time=factor(time, levels =c("3h", "24h"))) %>%
  mutate(indv=factor(rep(c(1,2,3,4,5,6), c(12,12,12,12,12,12)))) %>%
  ggplot(., aes(x=drug, y=counts))+
    geom_boxplot(position="identity",aes(fill=drug))+
  geom_point(aes(col=indv, size=2, alpha=0.5))+
  guides(alpha= "none", size= "none",  indv="none")+
  scale_color_brewer(palette = "Dark2",guide = "none")+
  scale_fill_manual(values=drug_pal_fact)+
    facet_wrap("time",  nrow=1, ncol=2)+
  theme_bw()+
  ylab(expression(atop("No Response set",italic("GCNT1")~log[2]~"cpm ")))+
  xlab("")+
  theme(strip.background = element_rect(fill = "#C77CFF"),
        plot.title = element_text(size=18,hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.x = element_text(size = 12, color = "white", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "clust1NR gene-1.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
71f4c17	reneeisnowhere	2023-05-25
f4a33f1	reneeisnowhere	2023-04-19

  # saveRDS(motif_NRrep,"output/motif_NRrep.RDS")

Log-Fold Change of Motif_NR

lfc_nums <- readRDS("data/toplistall.RDS")
lfc_nums %>% 
  dplyr::filter(ENTREZID %in% clust1) %>% 
  mutate(absFC=abs(logFC)) %>% 
  mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time=factor(time, levels=c("3_hours","24_hours"), labels = c(" 3 hours", "24 hours"))) %>%
  ggplot(., aes(x=id,y=absFC))+
  geom_boxplot(aes(fill=id))+
  scale_fill_manual(values=drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  facet_wrap(~time)+
  theme_bw()+
  xlab(" ")+
  ylab("|Log Fold Change|")+
  theme_bw()+
  ggtitle("|Log Fold| for all genes in NR set")+
  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 = "#C77CFF"),
        axis.text.x = element_text(size = 8, color = "white", angle = 15),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Past versions of "LFC motif NR-1.png"

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
bc9a824	reneeisnowhere	2023-06-02
4393a05	reneeisnowhere	2023-05-26

Motif_TI

# 
# motif_TI_rep <-
  cpmcounts %>% dplyr::filter(rownames(.)==92312) %>%
  pivot_longer(everything(), names_to = "treatment",values_to = "counts") %>%
  mutate(drug=rep(c("DNR","DOX","EPI","MTX","TRZ", "VEH"),12)) %>%
  mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time = rep((rep(c("3h", "24h"), c(6,6))), 6)) %>% 
  mutate(time=factor(time, 
                     levels=c("3h","24h"),
                     labels=c("3 hours","24 hours"))) %>%
  mutate(indv=factor(rep(c(1,2,3,4,5,6), c(12,12,12,12,12,12)))) %>%
  ggplot(., aes(x=drug, y=counts))+
  geom_boxplot(position="identity",aes(fill=drug))+
  geom_point(aes(col=indv, size=2, alpha=0.5))+
  guides(alpha= "none", size= "none",  indv="none")+
  scale_color_brewer(palette = "Dark2",guide = "none")+
  scale_fill_manual(values=drug_pal_fact)+
  facet_wrap("time", nrow=1, ncol=2)+
  theme_bw()+
  ylab(expression(atop("Early sustained response",italic("MEX3A")~log[2]~"cpm ")))+
  xlab("")+
  theme(strip.background = element_rect(fill = "#00BFC4"),
        plot.title = element_text(size=18,hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.x = element_text(size = 12, color = "white", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of clustESR-1.png

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28

# saveRDS(motif_TI_rep,"output/motif_TI_rep.RDS")

Log-Fold Change of TI response

lfc_nums %>% 
  dplyr::filter(ENTREZID %in% clust2) %>% 
  mutate(absFC=abs(logFC)) %>% 
  mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time=factor(time, 
                     levels=c("3_hours","24_hours"),
                     labels=c("3 hours","24 hours"))) %>%
  ggplot(., aes(x=id,y=absFC))+
  geom_boxplot(aes(fill=id))+
  scale_fill_manual(values=drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  facet_wrap(~time)+
  theme_bw()+
  xlab("")+
  ylab("|Log Fold Change|")+
  theme_bw()+
  ggtitle("|Log Fold| for genes in ESR")+
  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 = "#00BFC4"),
        axis.text = element_text(size = 8, color = "white", angle = 10),    
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Past versions of "esr Foldchange-1.png"

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25
45073b8	reneeisnowhere	2023-07-28

Motif_LR

# motif_LRrep <-
  cpmcounts %>% dplyr::filter(rownames(.)==55588) %>%
  pivot_longer(everything(), names_to = "treatment",values_to = "counts") %>%
  mutate(drug=rep(c("DNR","DOX","EPI","MTX","TRZ", "VEH"),12)) %>%
  mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time = rep((rep(c("3h", "24h"), c(6,6))), 6)) %>%
 mutate(time=factor(time, levels =c("3h", "24h"), labels=c("3 hours","24 hours"))) %>%
  mutate(indv=factor(rep(c(1,2,3,4,5,6), c(12,12,12,12,12,12)))) %>%
  ggplot(., aes(x=drug, y=counts))+
  geom_boxplot(position="identity",aes(fill=drug))+
  geom_point(aes(col=indv, size=2, alpha=0.5))+
  guides(alpha= "none", size= "none",  indv="none")+
  scale_color_brewer(palette = "Dark2",guide = "none")+
  scale_fill_manual(values=drug_pal_fact)+
  facet_wrap("time",  nrow=1, ncol=2)+
  theme_bw()+
  ylab(expression(atop("Late response ",italic("MED29")~log[2]~"cpm ")))+
  xlab("")+
  xlab("")+
  theme(strip.background = element_rect(fill = "#7CAE00"),
        plot.title = element_text(size=18,hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.x = element_text(size = 12, color = "white", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of motif_LR-1.png

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
71f4c17	reneeisnowhere	2023-05-25
f4a33f1	reneeisnowhere	2023-04-19

# saveRDS(motif_LRrep,"output/motif_LRrep.RDS")

Log-Fold Change of Late Response

lfc_nums %>% 
  dplyr::filter(ENTREZID %in% clust4) %>% 
  mutate(absFC=abs(logFC)) %>% 
  mutate(id = as.factor(id)) %>%
  mutate(time=factor(time, levels =c("3_hours", "24_hours"), labels=c("3 hours","24 hours"))) %>%
  ggplot(., aes(x=id,y=absFC))+
  geom_boxplot(aes(fill=id))+
  scale_fill_manual(values=drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  facet_wrap(~time)+
  theme_bw()+
  xlab("")+
  ylab("|Log Fold Change|")+
  theme_bw()+
  ggtitle("|Log Fold| for genes in LR set")+
  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 = "#7CAE00"),
        axis.text = element_text(size = 8, color = "white", angle = 10),     
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Past versions of "motifLR LFC-1.png"

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
bc9a824	reneeisnowhere	2023-06-02
4393a05	reneeisnowhere	2023-05-26

Motif_ER

# motif_ERrep <-
  cpmcounts %>% dplyr::filter(rownames(.)==27245) %>%
  pivot_longer(everything(), names_to = "treatment",values_to = "counts") %>%
  mutate(drug = rep(c("DNR","DOX","EPI","MTX","TRZ", "VEH"),12)) %>%
  mutate(time = rep((rep(c("3h", "24h"), c(6,6))), 6)) %>%
  mutate(time=factor(time, levels =c("3h", "24h"), labels=c("3 hours","24 hours"))) %>%
  mutate(indv=factor(rep(c(1,2,3,4,5,6), c(12,12,12,12,12,12)))) %>%
  ggplot(., aes(x=drug, y=counts))+
  geom_boxplot(position="identity",aes(fill=drug))+
  geom_point(aes(col=indv, size=2, alpha=0.5))+
  guides(alpha= "none", size= "none",  indv="none")+
  scale_color_brewer(palette = "Dark2",guide = "none")+
  scale_fill_manual(values=drug_pal_fact)+
  facet_wrap("time",nrow=1, ncol=2)+
  theme_bw()+
  ylab(expression(atop("Early acute response",italic("AHDC1")~log[2]~"cpm ")))+
  xlab("")+
  xlab("")+
  theme(strip.background = element_rect(fill = "#F8766D"),
        plot.title = element_text(size=18,hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.x = element_text(size = 12, color = "white", angle = 0),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"))

Past versions of "motif ER-1.png"

Version	Author	Date
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
71f4c17	reneeisnowhere	2023-05-25
f4a33f1	reneeisnowhere	2023-04-19

# saveRDS(motif_ERrep,"output/motif_ERrep.RDS")

Log-Fold Change of Early Response

lfc_nums %>% 
  dplyr::filter(ENTREZID %in% clust3) %>% 
  mutate(absFC=abs(logFC)) %>% 
  # mutate(id = as.factor(id)) %>%
  mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 

  mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
  ggplot(., aes(x=id,y=absFC))+
  geom_boxplot(aes(fill=id))+
  scale_fill_manual(values=drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  facet_wrap(~time)+
  theme_bw()+
  xlab("")+
  ylab("|Log Fold Change|")+
  theme_bw()+
  ggtitle("|Log Fold| for genes in EAR set")+
  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 = "#F8766D"),
        axis.text = element_text(size = 8, color = "white", angle = 10),   
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Past versions of lfc_ER-1.png

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25
45073b8	reneeisnowhere	2023-07-28
c209a8c	reneeisnowhere	2023-06-30
d5076c9	reneeisnowhere	2023-06-21
bc9a824	reneeisnowhere	2023-06-02
4393a05	reneeisnowhere	2023-05-26

##  I want the mean lfc by cluster with sid deg and non sid deg at 3 and 24 hourslfc_nums %>% 

mean_lfc <- lfc_nums %>% 
  mutate(ER=if_else(ENTREZID %in%motif_ER,"y","no")) %>% 
  mutate(LR=if_else(ENTREZID %in%motif_LR,"y","no")) %>%
  mutate(TI=if_else(ENTREZID %in%motif_TI,"y","no")) %>%
  mutate(NR=if_else(ENTREZID %in%motif_NR,"y","no")) %>%
  mutate(id = as.factor(id)) %>%
  mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>% 
  group_by(id,time) %>% 
  mutate(absFC=abs(logFC)) %>% 
  dplyr::select(id,time,absFC,ER,TI,LR,NR) %>% 
  dplyr::summarize(EAR=mean(absFC[ER=="y"]),ESR=mean(absFC[TI=="y"]),LR=mean(absFC[LR=="y"]),NR=mean(absFC[NR=="y"])) %>% as.data.frame()


mean_lfc %>% 
    mutate(time=factor(time, 
                     levels=c("3_hours","24_hours"),
                     labels=c("3 hours","24 hours"))) %>% 
  mutate(id=factor(id, levels=c("DNR" ,"DOX", "EPI" , "MTX" ,"TRZ"))) %>% 
  pivot_longer(!c(id,time), names_to = "Motif",values_to="meanLFC") %>% 
  ggplot(., aes(x=time,y= meanLFC,col=id,group=id))+
  geom_point()+
  geom_line(size = 3)+
 scale_fill_manual(values=drug_pal_fact)+
  facet_wrap(~Motif)+
  theme_bw()+
  xlab("")+
  scale_color_manual(values=drug_pal_fact)+
  ylab("|Log Fold Change|")+
  theme_bw()+
  ggtitle(" average |Log Fold| for genes across treatment each motif")+
  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 = "transparent"),
        axis.text = element_text(size = 8, color = "black", angle = 0),   
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Past versions of "all lfc-1.png"

Version	Author	Date
3511b8e	reneeisnowhere	2023-09-25

Session information

sessionInfo()

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

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

other attached packages:
 [1] ggpubr_0.6.0        RColorBrewer_1.1-3  Cormotif_1.46.0    
 [4] limma_3.56.2        affy_1.78.2         Biobase_2.60.0     
 [7] ggVennDiagram_1.2.3 scales_1.2.1        kableExtra_1.3.4   
[10] VennDiagram_1.7.3   futile.logger_1.4.3 gridExtra_2.3      
[13] BiocGenerics_0.46.0 gprofiler2_0.2.2    lubridate_1.9.3    
[16] forcats_1.0.0       stringr_1.5.0       dplyr_1.1.3        
[19] purrr_1.0.2         readr_2.1.4         tidyr_1.3.0        
[22] tibble_3.2.1        ggplot2_3.4.4       tidyverse_2.0.0    
[25] workflowr_1.7.1    

loaded via a namespace (and not attached):
 [1] formatR_1.14          rlang_1.1.1           magrittr_2.0.3       
 [4] git2r_0.32.0          compiler_4.3.1        getPass_0.2-2        
 [7] systemfonts_1.0.5     callr_3.7.3           vctrs_0.6.4          
[10] rvest_1.0.3           pkgconfig_2.0.3       fastmap_1.1.1        
[13] ellipsis_0.3.2        backports_1.4.1       labeling_0.4.3       
[16] utf8_1.2.3            promises_1.2.1        rmarkdown_2.25       
[19] tzdb_0.4.0            ps_1.7.5              preprocessCore_1.62.1
[22] xfun_0.40             zlibbioc_1.46.0       cachem_1.0.8         
[25] jsonlite_1.8.7        RVenn_1.1.0           highr_0.10           
[28] later_1.3.1           broom_1.0.5           R6_2.5.1             
[31] bslib_0.5.1           stringi_1.7.12        car_3.1-2            
[34] jquerylib_0.1.4       Rcpp_1.0.11           knitr_1.44           
[37] httpuv_1.6.11         timechange_0.2.0      tidyselect_1.2.0     
[40] rstudioapi_0.15.0     abind_1.4-5           yaml_2.3.7           
[43] processx_3.8.2        shiny_1.7.5.1         withr_2.5.1          
[46] evaluate_0.22         lambda.r_1.2.4        xml2_1.3.5           
[49] pillar_1.9.0          affyio_1.70.0         BiocManager_1.30.22  
[52] carData_3.0-5         whisker_0.4.1         plotly_4.10.3        
[55] generics_0.1.3        rprojroot_2.0.3       hms_1.1.3            
[58] munsell_0.5.0         xtable_1.8-4          glue_1.6.2           
[61] lazyeval_0.2.2        tools_4.3.1           data.table_1.14.8    
[64] webshot_0.5.5         ggsignif_0.6.4        fs_1.6.3             
[67] crosstalk_1.2.0       colorspace_2.1-0      cli_3.6.1            
[70] futile.options_1.0.1  fansi_1.0.5           viridisLite_0.4.2    
[73] svglite_2.1.2         gtable_0.3.4          rstatix_0.7.2        
[76] sass_0.4.7            digest_0.6.33         htmlwidgets_1.6.2    
[79] farver_2.1.1          htmltools_0.5.6.1     lifecycle_1.0.3      
[82] httr_1.4.7            mime_0.12