Cormotif_analysis
Renee Matthews
2025-05-06
Last updated: 2025-05-15
Checks: 7 0
Knit directory: ATAC_learning/
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Unstaged changes:
Modified: ATAC_learning.Rproj
Modified: analysis/AF_HF_SNPs.Rmd
Modified: analysis/Cardiotox_SNPs.Rmd
Modified: analysis/Enhancer_enrichment.Rmd
Modified: analysis/H3K27ac_cormotif.Rmd
Modified: analysis/Jaspar_motif.Rmd
Modified: analysis/Jaspar_motif_ff.Rmd
Modified: analysis/RNA_seq_integration.Rmd
Modified: analysis/TSS_and_CUG.Rmd
Modified: analysis/final_four_analysis.Rmd
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These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/Cormotif_analysis.Rmd) and
HTML (docs/Cormotif_analysis.html) files. If you’ve
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click on the hyperlinks in the table below to view the files as they
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d49fd7c | reneeisnowhere | 2025-05-15 | rastr updates and spelling |
| html | 5e6e462 | reneeisnowhere | 2025-05-07 | Build site. |
| Rmd | d969893 | reneeisnowhere | 2025-05-07 | updating new pages |
| Rmd | 2db35c7 | reneeisnowhere | 2025-05-07 | updates to analysis |
| html | 8af2b68 | reneeisnowhere | 2025-05-06 | Build site. |
| Rmd | 02654a0 | reneeisnowhere | 2025-05-06 | updates to analysis |
| html | ac1a2a6 | reneeisnowhere | 2025-05-06 | Build site. |
| Rmd | 43a493f | reneeisnowhere | 2025-05-06 | updates to analysis |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(Cormotif)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(eulerr)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)## 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$motif.p*Ng))
}
plotMotifnew<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$motif.q)[2]
v<-1:dim(fitted_cormotif$motif.q)[1]
image(u,v,t(fitted_cormotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Experiment",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$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$p.post)
genecount=floor(fitted_cormotif$motif.p*Ng)
NK=nrow(fitted_cormotif$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 regions",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$motif.p*Ng))
}Loading counts matrix and making filtered matrix
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
raw_counts <- read_delim("data/Final_four_data/re_analysis/Raw_unfiltered_counts.tsv",delim="\t") %>%
column_to_rownames("Peakid") %>%
as.matrix()
lcpm <- cpm(raw_counts, log= TRUE)
### for determining the basic cutoffs
filt_raw_counts <- raw_counts[rowMeans(lcpm)> 0,]
filt_raw_counts_noY <- filt_raw_counts[!grepl("chrY",rownames(filt_raw_counts)),]
dim(filt_raw_counts_noY)[1] 155557 48Number of filtered regions without the y chromosome = 155557 regions
making the metadata form
annotation_mat <- data.frame(timeset=colnames(filt_raw_counts_noY)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(time = factor(time, levels = c("3h", "24h"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=factor(indv, levels = c("A","B","C","D"))) %>%
mutate(trt_time=paste0(trt,"_",time))Preparing dge object
group <- c( rep(c(1,2,3,4,5,6,7,8,9,10,11,12),4))
group <- factor(group, levels =c("1","2","3","4","5","6","7","8","9","10","11","12"))
dge <- DGEList.data.frame(counts = filt_raw_counts_noY, group = group, genes = row.names(filt_raw_counts_noY))
dge <- calcNormFactors(dge)Adding group and comparison matrices
group_fac <- group
groupid <- as.numeric(group_fac)
compid <- data.frame(c1= c(2,4,6,8,10,1,3,5,7,9), c2 = c( 12,12,12,12,12,11,11,11,11,11))
compid c1 c2
1 2 12
2 4 12
3 6 12
4 8 12
5 10 12
6 1 11
7 3 11
8 5 11
9 7 11
10 9 11y_TMM_cpm <- cpm(dge, log = TRUE)
colnames(y_TMM_cpm) [1] "D_DNR_24h" "D_DNR_3h" "D_DOX_24h" "D_DOX_3h" "D_EPI_24h" "D_EPI_3h"
[7] "D_MTX_24h" "D_MTX_3h" "D_TRZ_24h" "D_TRZ_3h" "D_VEH_24h" "D_VEH_3h"
[13] "A_DNR_24h" "A_DNR_3h" "A_DOX_24h" "A_DOX_3h" "A_EPI_24h" "A_EPI_3h"
[19] "A_MTX_24h" "A_MTX_3h" "A_TRZ_24h" "A_TRZ_3h" "A_VEH_24h" "A_VEH_3h"
[25] "B_DNR_24h" "B_DNR_3h" "B_DOX_24h" "B_DOX_3h" "B_EPI_24h" "B_EPI_3h"
[31] "B_MTX_24h" "B_MTX_3h" "B_TRZ_24h" "B_TRZ_3h" "B_VEH_24h" "B_VEH_3h"
[37] "C_DNR_24h" "C_DNR_3h" "C_DOX_24h" "C_DOX_3h" "C_EPI_24h" "C_EPI_3h"
[43] "C_MTX_24h" "C_MTX_3h" "C_TRZ_24h" "C_TRZ_3h" "C_VEH_24h" "C_VEH_3h" Now that factors are grouped, We will call the cormotiffit function
to look at K=1:8.
This will be saved as an RDS for future use and lack of reruning
set.seed(31415)
cormotif_initial_norm <- cormotiffit(exprs = y_TMM_cpm, groupid = groupid, compid = compid, K=1:8, max.iter = 500, runtype = "logCPM")
saveRDS(cormotif_initial_norm,"data/Final_four_data/re_analysis/Cormotif_norm_initial.RDS")cormotif_initial_norm <- readRDS("data/Final_four_data/re_analysis/Cormotif_norm_initial.RDS")
plotMotif(cormotif_initial_norm)
| Version | Author | Date |
|---|---|---|
| ac1a2a6 | reneeisnowhere | 2025-05-06 |
plotIC(cormotif_initial_norm)
| Version | Author | Date |
|---|---|---|
| ac1a2a6 | reneeisnowhere | 2025-05-06 |
myColors <- rev(c("#FFFFFF", "#E6E6E6" ,"#CCCCCC", "#B3B3B3", "#999999", "#808080", "#666666","#4C4C4C", "#333333", "#191919","#000000"))
plot.new()
legend('center',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)
motif_prob <- cormotif_initial_norm$bestmotif$clustlike
rownames(motif_prob) <- rownames(y_TMM_cpm)
# saveRDS(motif_prob,"data/Final_four_data/re_analysis/motif_prob_norm.RDS")
| Version | Author | Date |
|---|---|---|
| ac1a2a6 | reneeisnowhere | 2025-05-06 |
Four motifs were found, now to generate the lists of regions that belong to each motif.
# motif_prob <- readRDS("data/Final_four_data/re_analysis/motif_prob_norm.RDS")
background_peaks <- motif_prob %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
NR_ff <- motif_prob %>%
as.data.frame() %>%
dplyr::filter(V1>.5 & V2<.5 & V3 <.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
EAR_ff <- motif_prob %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2>.5 & V3 <.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
LR_ff <- motif_prob %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2<.5 & V3 >.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
ESR_ff <- motif_prob %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2<.5 & V3 <.5& V4>0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)-EAR group has 7974 peaks.
-ESR group has 15128 peaks.
-LR group has 44227 peaks.
-NR group has 85154 peaks.
–Total number of peaks is 155557 –Total number of peaks in clusters is
152483
-percent of peaks classified into clusters: 98.0238755
-number of peaks not classified: 3074
Now to break into opening and closing motifs by group
median_24_lfc <- read_csv("data/Final_four_data/re_analysis/median_24_lfc_norm.csv")
median_3_lfc <- read_csv("data/Final_four_data/re_analysis/median_3_lfc_norm.csv")
open_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc > 0)
close_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc < 0)
open_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc > 0)
close_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc < 0)
medA <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0 & med_24h_lfc>0)
medB <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc < 0)
medC <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0& med_24h_lfc <0)
medD <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak"))%>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc > 0)
EAR_open <- EAR_ff %>%
dplyr::filter(Peakid %in% open_3med$peak)
EAR_open_gr <- EAR_open %>% GRanges()
EAR_close <- EAR_ff %>%
dplyr::filter(Peakid %in% close_3med$peak)
EAR_close_gr <- EAR_close %>% GRanges()
LR_open <- LR_ff %>%
dplyr::filter(Peakid %in% open_24med$peak)
LR_open_gr <- LR_open %>% GRanges()
LR_close <- LR_ff %>%
dplyr::filter(Peakid %in% close_24med$peak)
LR_close_gr <- LR_close %>% GRanges()
NR_gr <- NR_ff %>%
GRanges()
ESR_open <- ESR_ff %>%
dplyr::filter(Peakid %in% medA$peak)
ESR_open_gr <- ESR_open %>% GRanges()
ESR_close <- ESR_ff %>%
dplyr::filter(Peakid %in% medB$peak)
ESR_close_gr <- ESR_close %>% GRanges()
ESR_opcl <- ESR_ff %>%
dplyr::filter(Peakid %in% medC$peak)
ESR_opcl_gr <- ESR_opcl %>% GRanges()
ESR_clop <- ESR_ff %>%
dplyr::filter(Peakid %in% medD$peak)
ESR_clop_gr <- ESR_clop %>% GRanges()
NR <- NR_ff
NR_gr <- NR %>% GRanges()
background_peaks_gr <- background_peaks %>% GRanges
Motif_list <- list("EAR_open"=EAR_open,
"EAR_close"= EAR_close,
"ESR_open"=ESR_open,
"ESR_close"=ESR_close,
"ESR_opcl"=ESR_opcl,
"ESR_clop"=ESR_clop,
"LR_open"=LR_open,
"LR_close"=LR_close,
"NR"=NR_ff,
"all_regions"=background_peaks)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGeneExamining the Genomic features around each response motif region using Chipseeker
##make the Granges list
Motif_list_gr <- list("EAR_open"=EAR_open_gr,
"EAR_close"= EAR_close_gr,
"ESR_open"=ESR_open_gr,
"ESR_close"=ESR_close_gr,
"ESR_opcl"=ESR_opcl_gr,
"ESR_clop"=ESR_clop_gr,
"LR_open"=LR_open_gr,
"LR_close"=LR_close_gr,
"NR"=NR_gr,
"all_regions"=background_peaks_gr)
peakAnnoList<- lapply(Motif_list_gr, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)>> preparing features information... 2025-05-15 11:33:35 AM
>> identifying nearest features... 2025-05-15 11:33:36 AM
>> calculating distance from peak to TSS... 2025-05-15 11:33:37 AM
>> assigning genomic annotation... 2025-05-15 11:33:37 AM
>> assigning chromosome lengths 2025-05-15 11:33:56 AM
>> done... 2025-05-15 11:33:56 AM
>> preparing features information... 2025-05-15 11:33:56 AM
>> identifying nearest features... 2025-05-15 11:33:56 AM
>> calculating distance from peak to TSS... 2025-05-15 11:33:57 AM
>> assigning genomic annotation... 2025-05-15 11:33:57 AM
>> assigning chromosome lengths 2025-05-15 11:34:00 AM
>> done... 2025-05-15 11:34:00 AM
>> preparing features information... 2025-05-15 11:34:00 AM
>> identifying nearest features... 2025-05-15 11:34:00 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:00 AM
>> assigning genomic annotation... 2025-05-15 11:34:00 AM
>> assigning chromosome lengths 2025-05-15 11:34:03 AM
>> done... 2025-05-15 11:34:03 AM
>> preparing features information... 2025-05-15 11:34:03 AM
>> identifying nearest features... 2025-05-15 11:34:03 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:04 AM
>> assigning genomic annotation... 2025-05-15 11:34:04 AM
>> assigning chromosome lengths 2025-05-15 11:34:07 AM
>> done... 2025-05-15 11:34:07 AM
>> preparing features information... 2025-05-15 11:34:07 AM
>> identifying nearest features... 2025-05-15 11:34:07 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:07 AM
>> assigning genomic annotation... 2025-05-15 11:34:07 AM
>> assigning chromosome lengths 2025-05-15 11:34:10 AM
>> done... 2025-05-15 11:34:10 AM
>> preparing features information... 2025-05-15 11:34:10 AM
>> identifying nearest features... 2025-05-15 11:34:10 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:10 AM
>> assigning genomic annotation... 2025-05-15 11:34:10 AM
>> assigning chromosome lengths 2025-05-15 11:34:14 AM
>> done... 2025-05-15 11:34:14 AM
>> preparing features information... 2025-05-15 11:34:14 AM
>> identifying nearest features... 2025-05-15 11:34:14 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:14 AM
>> assigning genomic annotation... 2025-05-15 11:34:14 AM
>> assigning chromosome lengths 2025-05-15 11:34:17 AM
>> done... 2025-05-15 11:34:17 AM
>> preparing features information... 2025-05-15 11:34:18 AM
>> identifying nearest features... 2025-05-15 11:34:18 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:18 AM
>> assigning genomic annotation... 2025-05-15 11:34:18 AM
>> assigning chromosome lengths 2025-05-15 11:34:21 AM
>> done... 2025-05-15 11:34:21 AM
>> preparing features information... 2025-05-15 11:34:21 AM
>> identifying nearest features... 2025-05-15 11:34:21 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:22 AM
>> assigning genomic annotation... 2025-05-15 11:34:22 AM
>> assigning chromosome lengths 2025-05-15 11:34:26 AM
>> done... 2025-05-15 11:34:26 AM
>> preparing features information... 2025-05-15 11:34:26 AM
>> identifying nearest features... 2025-05-15 11:34:26 AM
>> calculating distance from peak to TSS... 2025-05-15 11:34:27 AM
>> assigning genomic annotation... 2025-05-15 11:34:27 AM
>> assigning chromosome lengths 2025-05-15 11:34:31 AM
>> done... 2025-05-15 11:34:31 AM plotAnnoBar(peakAnnoList[c(1,3,7,5,2,4,8,6,9)])+
ggtitle ("Genomic Feature Distribution, All groups")
| Version | Author | Date |
|---|---|---|
| ac1a2a6 | reneeisnowhere | 2025-05-06 |
# saveRDS(Motif_list_gr, "data/Final_four_data/re_analysis/Motif_list_granges.RDS")Looking at the average logFC across clusters:
save_list <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- bind_rows(save_list, .id = "group")
mean_lfc <-
all_results %>%
separate(., group, into=c("trt","time")) %>%
dplyr::rename("Peakid"=genes) %>%
dplyr::select(Peakid,trt,time,logFC) %>%
mutate(EAR_open = if_else(Peakid %in% EAR_open$Peakid, "y", "no")) %>%
mutate(EAR_close = if_else(Peakid %in% EAR_close$Peakid, "y", "no")) %>%
mutate(ESR_open = if_else(Peakid %in% ESR_open$Peakid, "y", "no")) %>%
mutate(ESR_close= if_else(Peakid %in% ESR_close$Peakid, "y", "no")) %>%
mutate(ESR_opcl= if_else(Peakid %in% ESR_opcl$Peakid, "y", "no")) %>%
mutate(ESR_clop= if_else(Peakid %in% ESR_clop$Peakid, "y", "no")) %>%
mutate(LR_open= if_else(Peakid %in% LR_open$Peakid, "y", "no")) %>%
mutate(LR_close= if_else(Peakid %in% LR_close$Peakid, "y", "no")) %>%
mutate(NR = if_else(Peakid %in% NR_ff$Peakid, "y", "no"))%>%
mutate(trt = factor(trt,levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
group_by(trt, time) %>%
mutate(absFC = (logFC)) %>% ##I took away abs(logFC) here but did not change code below
dplyr::select(trt, time, absFC, EAR_open:NR) %>%
dplyr::summarize(
EAR_op = mean(absFC[EAR_open == "y"]),
EAR_cl = mean(absFC[EAR_close == "y"]),
ESR_op = mean(absFC[ESR_open == "y"]),
ESR_cl = mean(absFC[ESR_close == "y"]),
ESR_opcl = mean(absFC[ESR_opcl == "y"]),
ESR_clop = mean(absFC[ESR_clop == "y"]),
LR_op = mean(absFC[LR_open == "y"]),
LR_cl = mean(absFC[LR_close == "y"]),
NR = mean(absFC[NR == "y"])
) %>%
as.data.frame()
mean_lfc %>%
ungroup() %>%
pivot_longer(!c(trt, time), names_to = "Motif",
values_to = "meanLFC") %>%
mutate(time=factor(time, levels = c("3","24"))) %>%
ggplot(., aes(x = time,y = meanLFC,col = trt,
group = trt
)) +
geom_point(size = 2) +
geom_line(linewidth = 2) +
ggpubr::fill_palette(drug_pal) +
# guides(fill=guide_legend(title = "Treatment"))+
facet_wrap( ~ Motif, nrow = 2) +
theme_bw() +
xlab("Time (hours)") +
scale_color_manual(values = drug_pal) +
ylab(" Avg. Log Fold Change") +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.0), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.0),
strip.background = element_rect(fill = "transparent"),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 11,
color = "black",
face = "bold"
)
)
# Folder with input BED files
output_dir <- "data/Final_four_data/re_analysis/motif_beds_centered"
# Create output folder if needed
dir.create(output_dir, showWarnings = FALSE)
# Loop through each BED file
for (name in names(Motif_list_gr)) {
gr <- Motif_list_gr[[name]]
# Recenter each region to 200 bp around its midpoint
gr_centered <- resize(gr, width = 200, fix = "center")
# Export to BED (auto converts to 0-based)
export(gr_centered, con = file.path(output_dir, paste0(name, "_centered.bed")), format = "BED")
}
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
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 stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] smplot2_0.2.5
[2] cowplot_1.1.3
[3] ComplexHeatmap_2.22.0
[4] ggrepel_0.9.6
[5] plyranges_1.26.0
[6] ggsignif_0.6.4
[7] genomation_1.38.0
[8] eulerr_7.0.2
[9] devtools_2.4.5
[10] usethis_3.1.0
[11] ggpubr_0.6.0
[12] BiocParallel_1.40.0
[13] Cormotif_1.52.0
[14] affy_1.84.0
[15] scales_1.3.0
[16] VennDiagram_1.7.3
[17] futile.logger_1.4.3
[18] gridExtra_2.3
[19] ggfortify_0.4.17
[20] edgeR_4.4.2
[21] limma_3.62.2
[22] rtracklayer_1.66.0
[23] org.Hs.eg.db_3.20.0
[24] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[25] GenomicFeatures_1.58.0
[26] AnnotationDbi_1.68.0
[27] Biobase_2.66.0
[28] GenomicRanges_1.58.0
[29] GenomeInfoDb_1.42.3
[30] IRanges_2.40.1
[31] S4Vectors_0.44.0
[32] BiocGenerics_0.52.0
[33] ChIPseeker_1.42.1
[34] RColorBrewer_1.1-3
[35] broom_1.0.7
[36] kableExtra_1.4.0
[37] lubridate_1.9.4
[38] forcats_1.0.0
[39] stringr_1.5.1
[40] dplyr_1.1.4
[41] purrr_1.0.4
[42] readr_2.1.5
[43] tidyr_1.3.1
[44] tibble_3.2.1
[45] ggplot2_3.5.1
[46] tidyverse_2.0.0
[47] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] doParallel_1.0.17
[6] httr_1.4.7
[7] profvis_0.4.0
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] GetoptLong_1.0.5
[13] urlchecker_1.0.1
[14] withr_3.0.2
[15] preprocessCore_1.68.0
[16] cli_3.6.4
[17] formatR_1.14
[18] labeling_0.4.3
[19] sass_0.4.9
[20] Rsamtools_2.22.0
[21] systemfonts_1.2.1
[22] yulab.utils_0.2.0
[23] foreign_0.8-88
[24] DOSE_4.0.0
[25] svglite_2.1.3
[26] R.utils_2.13.0
[27] sessioninfo_1.2.3
[28] plotrix_3.8-4
[29] BSgenome_1.74.0
[30] pwr_1.3-0
[31] impute_1.80.0
[32] rstudioapi_0.17.1
[33] RSQLite_2.3.9
[34] shape_1.4.6.1
[35] generics_0.1.3
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] BiocIO_1.16.0
[39] vroom_1.6.5
[40] gtools_3.9.5
[41] car_3.1-3
[42] GO.db_3.20.0
[43] Matrix_1.7-3
[44] abind_1.4-8
[45] R.methodsS3_1.8.2
[46] lifecycle_1.0.4
[47] whisker_0.4.1
[48] yaml_2.3.10
[49] carData_3.0-5
[50] SummarizedExperiment_1.36.0
[51] gplots_3.2.0
[52] qvalue_2.38.0
[53] SparseArray_1.6.2
[54] blob_1.2.4
[55] promises_1.3.2
[56] crayon_1.5.3
[57] miniUI_0.1.1.1
[58] ggtangle_0.0.6
[59] lattice_0.22-6
[60] KEGGREST_1.46.0
[61] pillar_1.10.1
[62] knitr_1.49
[63] fgsea_1.32.2
[64] rjson_0.2.23
[65] boot_1.3-31
[66] codetools_0.2-20
[67] fastmatch_1.1-6
[68] glue_1.8.0
[69] getPass_0.2-4
[70] ggfun_0.1.8
[71] data.table_1.17.0
[72] remotes_2.5.0
[73] vctrs_0.6.5
[74] png_0.1-8
[75] treeio_1.30.0
[76] gtable_0.3.6
[77] cachem_1.1.0
[78] xfun_0.51
[79] S4Arrays_1.6.0
[80] mime_0.12
[81] iterators_1.0.14
[82] statmod_1.5.0
[83] ellipsis_0.3.2
[84] nlme_3.1-167
[85] ggtree_3.14.0
[86] bit64_4.6.0-1
[87] rprojroot_2.0.4
[88] bslib_0.9.0
[89] affyio_1.76.0
[90] rpart_4.1.24
[91] KernSmooth_2.23-26
[92] Hmisc_5.2-2
[93] colorspace_2.1-1
[94] DBI_1.2.3
[95] nnet_7.3-20
[96] seqPattern_1.38.0
[97] tidyselect_1.2.1
[98] processx_3.8.6
[99] bit_4.6.0
[100] compiler_4.4.2
[101] curl_6.2.1
[102] git2r_0.35.0
[103] htmlTable_2.4.3
[104] xml2_1.3.7
[105] DelayedArray_0.32.0
[106] checkmate_2.3.2
[107] caTools_1.18.3
[108] callr_3.7.6
[109] digest_0.6.37
[110] rmarkdown_2.29
[111] XVector_0.46.0
[112] base64enc_0.1-3
[113] htmltools_0.5.8.1
[114] pkgconfig_2.0.3
[115] MatrixGenerics_1.18.1
[116] fastmap_1.2.0
[117] GlobalOptions_0.1.2
[118] rlang_1.1.5
[119] htmlwidgets_1.6.4
[120] UCSC.utils_1.2.0
[121] shiny_1.10.0
[122] farver_2.1.2
[123] jquerylib_0.1.4
[124] zoo_1.8-13
[125] jsonlite_1.9.1
[126] GOSemSim_2.32.0
[127] R.oo_1.27.0
[128] RCurl_1.98-1.16
[129] magrittr_2.0.3
[130] Formula_1.2-5
[131] GenomeInfoDbData_1.2.13
[132] ggplotify_0.1.2
[133] patchwork_1.3.0
[134] munsell_0.5.1
[135] Rcpp_1.0.14
[136] ape_5.8-1
[137] stringi_1.8.4
[138] zlibbioc_1.52.0
[139] plyr_1.8.9
[140] pkgbuild_1.4.6
[141] parallel_4.4.2
[142] Biostrings_2.74.1
[143] splines_4.4.2
[144] circlize_0.4.16
[145] hms_1.1.3
[146] locfit_1.5-9.12
[147] ps_1.9.0
[148] igraph_2.1.4
[149] reshape2_1.4.4
[150] pkgload_1.4.0
[151] futile.options_1.0.1
[152] XML_3.99-0.18
[153] evaluate_1.0.3
[154] lambda.r_1.2.4
[155] BiocManager_1.30.25
[156] foreach_1.5.2
[157] tzdb_0.4.0
[158] httpuv_1.6.15
[159] clue_0.3-66
[160] gridBase_0.4-7
[161] xtable_1.8-4
[162] restfulr_0.0.15
[163] tidytree_0.4.6
[164] rstatix_0.7.2
[165] later_1.4.1
[166] viridisLite_0.4.2
[167] aplot_0.2.5
[168] memoise_2.0.1
[169] GenomicAlignments_1.42.0
[170] cluster_2.1.8.1
[171] timechange_0.3.0