Last updated: 2023-09-25

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Knit directory: Cardiotoxicity/

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Unstaged changes:
    Deleted:    analysis/Cardiotoxicity.Rproj
    Modified:   analysis/Figure5.Rmd
    Modified:   analysis/GOI_plots.Rmd
    Modified:   analysis/Knowles2019.Rmd
    Modified:   analysis/Supplementary_figures.Rmd
    Modified:   analysis/variance_scrip.Rmd
    Deleted:    corMotifcustom.R
    Modified:   output/TNNI_LDH_RNAnormlist.txt
    Modified:   output/daplot.RDS
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    Modified:   output/plan2plot.png
    Modified:   output/sequencing_info.txt
    Modified:   output/toplistall.csv
    Modified:   output/trplot.RDS
    Modified:   output/veplot.RDS

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	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)

Version	Author	Date
d0f459b	reneeisnowhere	2023-04-18

plotMotif(cormotif_initial)

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)

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))

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"))

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"))

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"))

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"))

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"))

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"))

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"))

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"))

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)

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"))

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"))

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"))

Version	Author	Date
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"))

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"))

Version	Author	Date
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"))

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"))

Version	Author	Date
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"))

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"))

Version	Author	Date
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"))

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_1.9.2    
[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_3.4.3       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_1.0.4     callr_3.7.3           vctrs_0.6.3          
[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_2.24       
[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           withr_2.5.0          
[46] evaluate_0.21         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_4.10.2        
[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_1.0.4           viridisLite_0.4.2    
[73] svglite_2.1.1         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       lifecycle_1.0.3      
[82] httr_1.4.7            mime_0.12