Corrmotif Conc Analysis
Last updated: 2025-03-05
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Knit directory: CX5461_Project/
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📌 0.1 Micromolar
📌 Fit Limma Model Functions
## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits
limmafit.default <- function(exprs,groupid,compid) {
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(0,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for(i in 1:compnum) {
selid1 <- which(groupid == compid[i,1])
selid2 <- which(groupid == compid[i,2])
eset <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
fit <- lmFit(eset,designmat)
fit <- eBayes(fit)
limmat[,i] <- fit$t[,2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
limmafits[[i]] <- fit
# log odds
# w<-sqrt(1+fit$var.prior[2]/(1/g1num+1/g2num))
# log(0.99)+dt(fit$t[1,2],g1num+g2num-2+fit$df.prior,log=TRUE)-log(0.01)-dt(fit$t[1,2]/w, g1num+g2num-2+fit$df.prior, log=TRUE)+log(w)
}
names(limmafits) <- rownames(compid)
limmacompnum<-nrow(compid)
result<-list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.counts <-
function (exprs, groupid, compid, norm.factor.method = "TMM", voom.normalize.method = "none")
{
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for (i in 1:compnum) {
message(paste("Running limma for comparision",i,"/",compnum))
selid1 <- which(groupid == compid[i, 1])
selid2 <- which(groupid == compid[i, 2])
# make a new count data frame
counts <- cbind(exprs[, selid1], exprs[, selid2])
# remove NAs
not.nas <- which(apply(counts, 1, function(x) !any(is.na(x))) == TRUE)
# runn voom/limma
d <- DGEList(counts[not.nas,])
d <- calcNormFactors(d, method = norm.factor.method)
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base = rep(1, (g1num + g2num)), delta = c(rep(0,
g1num), rep(1, g2num)))
y <- voom(d, designmat, normalize.method = voom.normalize.method)
fit <- lmFit(y, designmat)
fit <- eBayes(fit)
limmafits[[i]] <- fit
limmat[not.nas, i] <- fit$t[, 2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
}
limmacompnum <- nrow(compid)
names(limmafits) <- rownames(compid)
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.list <-
function (fitlist, cmp.idx=2)
{
compnum <- length(fitlist)
genes <- c()
for (i in 1:compnum) genes <- unique(c(genes, rownames(fitlist[[i]])))
genenum <- length(genes)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- genes
colnames(limmat) <- names(fitlist)
names(limmas2) <- names(fitlist)
names(limmadf) <- names(fitlist)
names(limmav0) <- names(fitlist)
names(limmag1num) <- names(fitlist)
names(limmag2num) <- names(fitlist)
for (i in 1:compnum) {
this.t <- fitlist[[i]]$t[,cmp.idx]
limmat[names(this.t),i] <- this.t
limmas2[i] <- fitlist[[i]]$s2.prior
limmadf[i] <- fitlist[[i]]$df.prior
limmav0[i] <- fitlist[[i]]$var.prior[cmp.idx]
limmag1num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==0)
limmag2num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==1)
}
limmacompnum <- compnum
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
## Rank genes based on statistics
generank<-function(x) {
xcol<-ncol(x)
xrow<-nrow(x)
result<-matrix(0,xrow,xcol)
z<-(1:1:xrow)
for(i in 1:xcol) {
y<-sort(x[,i],decreasing=TRUE,na.last=TRUE)
result[,i]<-match(x[,i],y)
result[,i]<-order(result[,i])
}
result
}
## Log-likelihood for moderated t under H0
modt.f0.loglike<-function(x,df) {
a<-dt(x, df, log=TRUE)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Log-likelihood for moderated t under H1
## param=c(df,g1num,g2num,v0)
modt.f1.loglike<-function(x,param) {
df<-param[1]
g1num<-param[2]
g2num<-param[3]
v0<-param[4]
w<-sqrt(1+v0/(1/g1num+1/g2num))
dt(x/w, df, log=TRUE)-log(w)
a<-dt(x/w, df, log=TRUE)-log(w)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Correlation Motif Fit
cmfit.X<-function(x, type, K=1, tol=1e-3, max.iter=100) {
## initialize
xrow <- nrow(x)
xcol <- ncol(x)
loglike0 <- list()
loglike1 <- list()
p <- rep(1, K)/K
q <- matrix(runif(K * xcol), K, xcol)
q[1, ] <- rep(0.01, xcol)
for (i in 1:xcol) {
f0 <- type[[i]][[1]]
f0param <- type[[i]][[2]]
f1 <- type[[i]][[3]]
f1param <- type[[i]][[4]]
loglike0[[i]] <- f0(x[, i], f0param)
loglike1[[i]] <- f1(x[, i], f1param)
}
condlike <- list()
for (i in 1:xcol) {
condlike[[i]] <- matrix(0, xrow, K)
}
loglike.old <- -1e+10
for (i.iter in 1:max.iter) {
if ((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations for K=",
K, sep = ""))
}
err <- tol + 1
clustlike <- matrix(0, xrow, K)
#templike <- matrix(0, xrow, 2)
templike1 <- rep(0, xrow)
templike2 <- rep(0, xrow)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax +
log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
#p.new <- (apply(clustlike, 2, sum) + 1)/(xrow + K)
p.new <- (Rfast::colsums(clustlike) + 1)/(xrow + K)
q.new <- matrix(0, K, xcol)
for (j in 1:K) {
clustpsum <- sum(clustlike[, j])
for (i in 1:xcol) {
q.new[j, i] <- (sum(clustlike[, j] * condlike[[i]][,
j]) + 1)/(clustpsum + 2)
}
}
err.p <- max(abs(p.new - p)/p)
err.q <- max(abs(q.new - q)/q)
err <- max(err.p, err.q)
loglike.new <- (sum(tempmax + log(tempsum)) + sum(log(p.new)) +
sum(log(q.new) + log(1 - q.new)))/xrow
p <- p.new
q <- q.new
loglike.old <- loglike.new
if (err < tol) {
break
}
}
clustlike <- matrix(0, xrow, K)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax + log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
p.post <- matrix(0, xrow, xcol)
for (j in 1:K) {
for (i in 1:xcol) {
p.post[, i] <- p.post[, i] + clustlike[, j] * condlike[[i]][,
j]
}
}
loglike.old <- loglike.old - (sum(log(p)) + sum(log(q) +
log(1 - q)))/xrow
loglike.old <- loglike.old * xrow
result <- list(p.post = p.post, motif.prior = p, motif.q = q,
loglike = loglike.old, clustlike=clustlike, condlike=condlike)
}
## Fit using (0,0,...,0) and (1,1,...,1)
cmfitall<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01
## compute loglikelihood
L0<-matrix(0,xrow,1)
L1<-matrix(0,xrow,1)
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
L0<-L0+loglike0[[i]]
L1<-L1+loglike1[[i]]
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2])+1)/(xrow+2)
## evaluate convergence
err<-abs(p.new-p)/p
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+log(p.new)+log(1-p.new))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(i in 1:xcol) {
p.post[,i]<-clustlike[,2]
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-(log(p)+log(1-p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
## Fit each dataset separately
cmfitsep<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01*rep(1,xcol)
loglike.final<-rep(0,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
p.post<-matrix(0,xrow,xcol)
## EM algorithm to get MLE of p
for(coli in 1:xcol) {
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## evaluate whether the log.likelihood increases
loglike.new<-sum(tempmax+log(tempsum))/xrow
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2]))/(xrow)
## evaluate convergence
err<-abs(p.new-p[coli])/p[coli]
loglike.old<-loglike.new
p[coli]<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post[,coli]<-clustlike[,2]
loglike.final[coli]<-loglike.old
}
## return
loglike.final<-loglike.final*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.final)
}
## Fit the full model
cmfitfull<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
K<-2^xcol
p<-rep(1,K)/K
pattern<-rep(0,xcol)
patid<-matrix(0,K,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
L<-matrix(0,xrow,K)
for(i in 1:K)
{
patid[i,]<-pattern
for(j in 1:xcol) {
if(pattern[j] < 0.5) {
L[,i]<-L[,i]+loglike0[[j]]
} else {
L[,i]<-L[,i]+loglike1[[j]]
}
}
if(i < K) {
pattern[xcol]<-pattern[xcol]+1
j<-xcol
while(pattern[j] > 1) {
pattern[j]<-0
j<-j-1
pattern[j]<-pattern[j]+1
}
}
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(apply(clustlike,2,sum)+1)/(xrow+K)
## evaluate convergence
err<-max(abs(p.new-p)/p)
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+sum(log(p.new)))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(j in 1:K) {
for(i in 1:xcol) {
if(patid[j,i] > 0.5) {
p.post[,i]<-p.post[,i]+clustlike[,j]
}
}
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-sum(log(p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
generatetype<-function(limfitted)
{
jtype<-list()
df<-limfitted$g1num+limfitted$g2num-2+limfitted$df0
for(j in 1:limfitted$compnum)
{
jtype[[j]]<-list(f0=modt.f0.loglike, f0.param=df[j], f1=modt.f1.loglike, f1.param=c(df[j],limfitted$g1num[j],limfitted$g2num[j],limfitted$v0[j]))
}
jtype
}
cormotiffit <- function(exprs, groupid=NULL, compid=NULL, K=1, tol=1e-3,
max.iter=100, BIC=TRUE, norm.factor.method="TMM",
voom.normalize.method = "none", runtype=c("logCPM","counts","limmafits"), each=3)
{
# first I want to do some typechecking. Input can be either a normalized
# matrix, a count matrix, or a list of limma fits. Dispatch the correct
# limmafit accordingly.
# todo: add some typechecking here
limfitted <- list()
if (runtype=="counts") {
limfitted <- limmafit.counts(exprs,groupid,compid, norm.factor.method, voom.normalize.method)
} else if (runtype=="logCPM") {
limfitted <- limmafit.default(exprs,groupid,compid)
} else if (runtype=="limmafits") {
limfitted <- limmafit.list(exprs)
} else {
stop("runtype must be one of 'logCPM', 'counts', or 'limmafits'")
}
jtype<-generatetype(limfitted)
fitresult<-list()
ks <- rep(K, each = each)
fitresult <- bplapply(1:length(ks), function(i, x, type, ks, tol, max.iter) {
cmfit.X(x, type, K = ks[i], tol = tol, max.iter = max.iter)
}, x=limfitted$t, type=jtype, ks=ks, tol=tol, max.iter=max.iter)
best.fitresults <- list()
for (i in 1:length(K)) {
w.k <- which(ks==K[i])
this.bic <- c()
for (j in w.k) this.bic[j] <- -2 * fitresult[[j]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
w.min <- which(this.bic == min(this.bic, na.rm = TRUE))[1]
best.fitresults[[i]] <- fitresult[[w.min]]
}
fitresult <- best.fitresults
bic <- rep(0, length(K))
aic <- rep(0, length(K))
loglike <- rep(0, length(K))
for (i in 1:length(K)) loglike[i] <- fitresult[[i]]$loglike
for (i in 1:length(K)) bic[i] <- -2 * fitresult[[i]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
for (i in 1:length(K)) aic[i] <- -2 * fitresult[[i]]$loglike + 2 * (K[i] - 1 + K[i] * limfitted$compnum)
if(BIC==TRUE) {
bestflag=which(bic==min(bic))
}
else {
bestflag=which(aic==min(aic))
}
result<-list(bestmotif=fitresult[[bestflag]],bic=cbind(K,bic),
aic=cbind(K,aic),loglike=cbind(K,loglike), allmotifs=fitresult)
}
cormotiffitall<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitall(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitsep<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitsep(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitfull<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitfull(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
plotIC<-function(fitted_cormotif)
{
oldpar<-par(mfrow=c(1,2))
plot(fitted_cormotif$bic[,1], fitted_cormotif$bic[,2], type="b",xlab="Motif Number", ylab="BIC", main="BIC")
plot(fitted_cormotif$aic[,1], fitted_cormotif$aic[,2], type="b",xlab="Motif Number", ylab="AIC", main="AIC")
}
plotMotif<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$bestmotif$motif.q)[2]
v<-1:dim(fitted_cormotif$bestmotif$motif.q)[1]
image(u,v,t(fitted_cormotif$bestmotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Study",yaxt = "n",
ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
axis(2,at=1:length(v))
for(i in 1:(length(u)+1))
{
abline(v=(i-0.5))
}
for(i in 1:(length(v)+1))
{
abline(h=(i-0.5))
}
Ng=10000
if(is.null(fitted_cormotif$bestmotif$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$bestmotif$p.post)
genecount=floor(fitted_cormotif$bestmotif$motif.p*Ng)
NK=nrow(fitted_cormotif$bestmotif$motif.q)
plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
frame.plot=FALSE,axes=FALSE,xlab="No. of genes",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$bestmotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$bestmotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$bestmotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$bestmotif$motif.p*Ng))
}📌 Load Required Libraries
library(Cormotif)
library(Rfast)
library(dplyr)
library(BiocParallel)
library(gprofiler2)
library(ggplot2)📌 Corrmotif Model 0.1 Micromolar
📌 Load Corrmotif Data
# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene
# Filter for 0.1 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.1", colnames(Corrmotif_df))])
# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.1", colnames(groupid))])
compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]📌 Corrmotif Model 0.1 Micromolar (K=1:8)
📌 Fit Corrmotif Model (K=1:8) (0.1 Micromolar)
set.seed(11111)
# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.1 <- cormotiffit(
exprs = exprs.corrmotif,
groupid = groupid_df,
compid = compid_df,
K = 1:8,
max.iter = 1000,
BIC = TRUE,
runtype = "logCPM"
)
gene_prob_0.1 <- motif.fitted_0.1$bestmotif$p.post
rownames(gene_prob_0.1) <- rownames(Corrmotif_df)
motif_prob_0.1 <- motif.fitted_0.1$bestmotif$clustlike
rownames(motif_prob_0.1) <- rownames(gene_prob_0.1)
write.csv(motif_prob_0.1,"data/cormotif_probability_genelist_0.1.csv")📌 Plot motif (0.1 Micromolar)
cormotif_0.1 <- readRDS("data/Corrmotif/cormotif_0.1.RDS")
cormotif_0.1$bic K bic
[1,] 1 291696.5
[2,] 2 284585.3
[3,] 3 283482.9
[4,] 4 283551.8
[5,] 5 283620.7
[6,] 6 283689.6
[7,] 7 283758.5
[8,] 8 283827.4plotIC(cormotif_0.1)
plotMotif(cormotif_0.1)
📌 Extract Gene Probabilities (0.1 Micromolar)
# Extract posterior probabilities for genes
gene_prob_tran_0.1 <- cormotif_0.1$bestmotif$p.post
rownames(gene_prob_tran_0.1) <- rownames(Corrmotif_df)
# Define gene probability groups
prob_1_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] < 0.5 & gene_prob_tran_0.1[,6]<0.5),])
length(prob_1_0.1)[1] 12308prob_2_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] >0.5 & gene_prob_tran_0.1[,3] >0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_2_0.1)[1] 415prob_3_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_3_0.1)[1] 1551📌 Distribution of Gene Clusters Identified by Corrmotif (0.1 micromolar)
# Load necessary library
library(ggplot2)
# Data
data <- data.frame(
Category = c("Non response (0.1 µM)", "CX-DOX mid-late response (0.1 µM)", "DOX only mid-late (0.1 µM)"),
Value = c(12308, 415, 1551)
)
# Define custom colors
custom_colors <- c("Non response (0.1 µM)" = "#FF9999",
"DOX only mid-late (0.1 µM)" = "#99FF99",
"CX-DOX mid-late response (0.1 µM)" = "#66B2FF")
# Create pie chart
ggplot(data, aes(x = "", y = Value, fill = Category)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
geom_text(aes(label = Value),
position = position_stack(vjust = 0.5),
size = 4, color = "black") +
labs(title = "Pie Chart (0.1 micromolar Corrmotif)", x = NULL, y = NULL) +
theme_void() +
scale_fill_manual(values = custom_colors)
| Version | Author | Date |
|---|---|---|
| 2de67e6 | sayanpaul01 | 2025-02-27 |
📌 Save Corr-motif datasets for Gene Ontology analysis (0.1 Micromolar)
write.csv(data.frame(Entrez_ID = prob_1_0.1), "data/prob_1_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.1), "data/prob_2_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.1), "data/prob_3_0.1.csv", row.names = FALSE)📌 0.1 Micromolar abs logFC
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Absolute logFC
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(absFC = abs(logFC))
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Drug = factor(Drug, levels = c("CX.5461", "DOX")),
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group,
levels = c(
"CX-DOX mid-late response\n(0.1 micromolar)",
"DOX only mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.1, aes(x = Drug, y = absFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) + # Custom color palette
facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) + # Ensure Proper Wrapping
theme_bw() +
labs(
x = "Drugs",
y = "|Log Fold Change|",
title = "|Log Fold| for 0.1 micromolar"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "gray"), # Gray background for facet labels
strip.text = element_text(size = 12, color = "black", face = "bold"), # Bold styling for facet labels
axis.text.x = element_text(size = 10, color = "black", angle = 15)
)
📌 0.1 Micromolar mean (Abs logFC) across timepoints
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "3"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "3"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
mutate(abs_logFC = abs(logFC)) %>% # Take absolute log fold change
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.1$Response_Group),
Drug = unique(all_toptables_0.1$Drug),
Timepoint = c("3", "24", "48")
)
# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.1 micromolar)",
"DOX only mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(0, 2.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean |Log Fold Change|",
title = "Mean |Log Fold Change| Across Response Groups (0.1 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 0.1 Micromolar logFC
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.1 micromolar)",
"DOX only mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot**
ggplot(all_toptables_0.1, aes(x = Drug, y = logFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(Response_Group ~ Timepoint) +
theme_bw() +
labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.1 Micromolar") +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
strip.text = element_text(size = 12, face = "bold")
)
📌 0.1 Micromolar mean logFC across timepoints
# Compute Mean logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.1 micromolar)",
"DOX only mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Line Plot**
ggplot(data_summary, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
ylim(-2, 1.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean Log Fold Change",
title = "Mean Log Fold Change Across Response Groups (0.1 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 Corrmotif Model 0.5 Micromolar
📌 Load Corrmotif Data
# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene
# Filter for 0.5 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.5", colnames(Corrmotif_df))])
# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.5", colnames(groupid))])
compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]📌 Corrmotif Model 0.5 Micromolar (K=1:8)
📌 Fit Corrmotif Model (K=1:8) (0.5 Micromolar)
# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.5 <- cormotiffit(
exprs = exprs.corrmotif,
groupid = groupid_df,
compid = compid_df,
K = 1:8,
max.iter = 1000,
BIC = TRUE,
runtype = "logCPM"
)
gene_prob_0.5 <- motif.fitted_0.5$bestmotif$p.post
rownames(gene_prob_0.5) <- rownames(Corrmotif_df)
motif_prob_0.5 <- motif.fitted_0.5$bestmotif$clustlike
rownames(motif_prob_0.5) <- rownames(gene_prob_0.5)
write.csv(motif_prob_0.5,"data/cormotif_probability_genelist_0.5.csv")📌 Plot motif (0.5 Micromolar)
cormotif_0.5 <- readRDS("data/Corrmotif/cormotif_0.5.RDS")
cormotif_0.5$bic K bic
[1,] 1 352140.7
[2,] 2 346785.8
[3,] 3 344812.9
[4,] 4 344860.1
[5,] 5 344751.9
[6,] 6 344820.8
[7,] 7 344889.7
[8,] 8 344966.6plotIC(cormotif_0.5)
| Version | Author | Date |
|---|---|---|
| 41cd1be | sayanpaul01 | 2025-02-27 |
plotMotif(cormotif_0.5)
| Version | Author | Date |
|---|---|---|
| 41cd1be | sayanpaul01 | 2025-02-27 |
📌 Extract Gene Probabilities (0.5 Micromolar)
# Extract posterior probabilities for genes
gene_prob_tran_0.5 <- cormotif_0.5$bestmotif$p.post
rownames(gene_prob_tran_0.5) <- rownames(Corrmotif_df)
# Define gene probability groups
prob_1_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6]<0.5),])
length(prob_1_0.5)[1] 7134prob_2_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] >0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>=0.02),])
length(prob_2_0.5)[1] 179prob_3_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_3_0.5)[1] 6450prob_4_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] >= 0.1 & gene_prob_tran_0.5[,2] > 0.5 & gene_prob_tran_0.5[,3] > 0.5 & gene_prob_tran_0.5[,4] >= 0.02 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6] < 0.5),])
length(prob_4_0.5)[1] 142prob_5_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] >0.5 & gene_prob_tran_0.5[,3] >0.5 & gene_prob_tran_0.5[,4] >=0.02 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_5_0.5)[1] 221📌 Distribution of Gene Clusters Identified by Corrmotif (0.5 micromolar)
# Load necessary library
library(ggplot2)
# Data
data <- data.frame(
Category = c("Non response (0.5)", "DOX-only early-mid response (0.5 µM)", "DOX only mid-late response (0.5 µM)", "CX-only mid-late response (0.5 µM)", "CX-DOX mid-late response (0.5 µM)"),
Value = c(7134,179,6450,142,221)
)
# Add values to category names (to be displayed in the legend)
data$Category <- paste0(data$Category, " (", data$Value, ")")
# Define custom colors with updated category names
custom_colors <- setNames(
c("#FF9999", "#FF66CC", "#66B2FF", "#99FF99", "#FFD700"),
data$Category # Ensures color names match updated categories
)
# Create pie chart without number labels inside
ggplot(data, aes(x = "", y = Value, fill = Category)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
labs(title = "Pie Chart (0.5 micromolar Corrmotif)", x = NULL, y = NULL) +
theme_void() +
scale_fill_manual(values = custom_colors)
📌 Save Corr-motif datasets for Gene Ontology analysis (0.5 Micromolar)
write.csv(data.frame(Entrez_ID = prob_1_0.5), "data/prob_1_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.5), "data/prob_2_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.5), "data/prob_3_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_4_0.5), "data/prob_4_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_5_0.5), "data/prob_5_0.5.csv", row.names = FALSE)📌 0.5 Micromolar abs logFC
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Convert datasets to DataFrames
Toptable_CX_0.5_3_df <- data.frame(CX_0.5_3)
Toptable_CX_0.5_24_df <- data.frame(CX_0.5_24)
Toptable_CX_0.5_48_df <- data.frame(CX_0.5_48)
Toptable_DOX_0.5_3_df <- data.frame(DOX_0.5_3)
Toptable_DOX_0.5_24_df <- data.frame(DOX_0.5_24)
Toptable_DOX_0.5_48_df <- data.frame(DOX_0.5_48)
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
Toptable_CX_0.5_3_df %>% mutate(Drug = "CX.5461", Timepoint = "3"),
Toptable_CX_0.5_24_df %>% mutate(Drug = "CX.5461", Timepoint = "24"),
Toptable_CX_0.5_48_df %>% mutate(Drug = "CX.5461", Timepoint = "48"),
Toptable_DOX_0.5_3_df %>% mutate(Drug = "DOX", Timepoint = "3"),
Toptable_DOX_0.5_24_df %>% mutate(Drug = "DOX", Timepoint = "24"),
Toptable_DOX_0.5_48_df %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-only early-mid response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "CX-DOX mid-late response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
)
# Remove NA Values (Genes Not in Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>% filter(!is.na(Response_Group))
# Compute Absolute logFC
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(absFC = abs(logFC))
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Drug = factor(Drug, levels = c("CX.5461", "DOX")),
Timepoint = factor(Timepoint, levels = c("3", "24", "48"),
labels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group,
levels = c("CX-DOX mid-late response\n(0.5 micromolar)",
"CX-only mid-late response\n(0.5 micromolar)",
"DOX only mid-late response\n(0.5 micromolar)",
"DOX-only early-mid response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)")) # Reversed Order
)
# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.5, aes(x = Drug, y = absFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) + # Custom color palette
facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) + # Ensure Proper Wrapping
theme_bw() +
labs(
x = "Drugs",
y = "|Log Fold Change|",
title = "|Log Fold| for 0.5 micromolar"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "gray"), # Gray background for facet labels
strip.text = element_text(size = 12, color = "black", face = "bold"), # Bold styling for facet labels
axis.text.x = element_text(size = 10, color = "black", angle = 15)
)
📌 0.5 Micromolar mean (Abs logFC) across timepoints
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "3"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "3"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-only early-mid response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "CX-DOX mid-late response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) # Remove NA values
# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.5 %>%
mutate(abs_logFC = abs(logFC)) %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.5$Response_Group),
Drug = unique(all_toptables_0.5$Drug),
Timepoint = c("3", "24", "48")
)
# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.5 micromolar)",
"CX-only mid-late response\n(0.5 micromolar)",
"DOX-only mid-late response\n(0.5 micromolar)",
"DOX-only early-mid response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Mean Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(0, 2.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean |Log Fold Change|",
title = "Mean |Log Fold Change| Across Response Groups (0.5 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 0.5 Micromolar logFC
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-only early-mid response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "CX-DOX mid-late response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.5 micromolar)",
"CX-only mid-late response\n(0.5 micromolar)",
"DOX-only mid-late response\n(0.5 micromolar)",
"DOX-only early-mid response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot**
ggplot(all_toptables_0.5, aes(x = Drug, y = logFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(Response_Group ~ Timepoint) +
theme_bw() +
labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.5 Micromolar") +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
strip.text = element_text(size = 12, face = "bold")
)
📌 0.5 Micromolar mean logFC across timepoints
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-only early-mid response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX-only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "CX-DOX mid-late response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Mean logFC for Line Plot
data_summary_0.5 <- all_toptables_0.5 %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.5$Response_Group),
Drug = unique(all_toptables_0.5$Drug),
Timepoint = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")
)
# **Merge to keep missing timepoints**
data_summary_0.5 <- full_join(timepoints_full, data_summary_0.5, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_logFC with 0 if no genes were present**
data_summary_0.5$mean_logFC[is.na(data_summary_0.5$mean_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary_0.5 <- data_summary_0.5 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"CX-DOX mid-late response\n(0.5 micromolar)",
"CX-only mid-late response\n(0.5 micromolar)",
"DOX-only mid-late response\n(0.5 micromolar)",
"DOX-only early-mid response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed Order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Mean logFC**
ggplot(data_summary_0.5, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(-2, 1.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean Log Fold Change",
title = "Mean Log Fold Change Across Response Groups (0.5 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 Proportion of DNA Damage Repair Genes (0.1 micromolar)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)Warning: package 'tidyr' was built under R version 4.3.3library(org.Hs.eg.db)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.1 Concentration**
prob_groups_0.1 <- list(
"Non Response (0.1)" = read.csv("data/prob_1_0.1.csv")$Entrez_ID,
"DOX only mid-late (0.1)" = read.csv("data/prob_2_0.1.csv")$Entrez_ID,
"CX_DOX mid-late (0.1)" = read.csv("data/prob_3_0.1.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.1 <- bind_rows(
lapply(prob_groups_0.1, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.1 <- corrmotif_df_0.1 %>%
mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))
# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.1 %>%
group_by(Response_Group, DNA_Damage) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.1)", "DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for "DOX only mid-late (0.1)" and "CX_DOX mid-late (0.1)" vs "Non Response (0.1)"**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.1)") %>%
dplyr::select(DNA_Damage, Count) %>%
{setNames(.$Count, .$DNA_Damage)} # Convert to named vector
chi_results <- proportion_data %>%
filter(Response_Group %in% c("DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
non_response_counts["Yes"], non_response_counts["No"]
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # Fixed Y-axis to 100%
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of DNA Damage Repair Genes in\n0.1 Corrmotif Response Groups",
x = "Response Groups (0.1 Concentration)",
y = "Percentage",
fill = "DNA Damage Repair"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Proportion of DNA Damage Repair Genes (0.5 micromolar)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)
# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.5 Concentration**
prob_groups_0.5 <- list(
"Non Response (0.5)" = read.csv("data/prob_1_0.5.csv")$Entrez_ID,
"DOX-specific response (0.5)" = read.csv("data/prob_2_0.5.csv")$Entrez_ID,
"DOX only mid-late response (0.5)" = read.csv("data/prob_3_0.5.csv")$Entrez_ID,
"CX DOX (early) response (0.5)" = read.csv("data/prob_4_0.5.csv")$Entrez_ID,
"DOX + CX (mid-late) response (0.5)" = read.csv("data/prob_5_0.5.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.5 <- bind_rows(
lapply(prob_groups_0.5, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.5 <- corrmotif_df_0.5 %>%
mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))
# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.5 %>%
group_by(Response_Group, DNA_Damage) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.5)", "DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for Each Response Group vs Non-Response**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.5)") %>%
dplyr::select(DNA_Damage, Count) %>%
{setNames(.$Count, .$DNA_Damage)} # Convert to named vector
# **Comparing Each Group Against "Non Response (0.5)"**
chi_results <- proportion_data %>%
filter(Response_Group %in% c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2], # Response group counts
non_response_counts["Yes"], non_response_counts["No"] # Non-response counts
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # **Y-axis now limited to 110% for visibility**
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of DNA Damage Repair Genes in\n0.5 Corrmotif Response Groups",
x = "Response Groups (0.5 Concentration)",
y = "Percentage",
fill = "DNA Damage Repair Genes"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Proportion of P53 target Genes Genes (0.1 micromolar)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)
# **🔹 Read P53 Target Gene List**
P53_Target <- read.csv("data/P53_Target.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
P53_Target <- P53_Target %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = P53_Target$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
P53_Target_genes <- na.omit(P53_Target$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.1 Concentration**
prob_groups_0.1 <- list(
"Non Response (0.1)" = read.csv("data/prob_1_0.1.csv")$Entrez_ID,
"DOX only mid-late (0.1)" = read.csv("data/prob_2_0.1.csv")$Entrez_ID,
"CX_DOX mid-late (0.1)" = read.csv("data/prob_3_0.1.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.1 <- bind_rows(
lapply(prob_groups_0.1, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with P53 Target Genes**
corrmotif_df_0.1 <- corrmotif_df_0.1 %>%
mutate(P53_Target = ifelse(Entrez_ID %in% P53_Target_genes, "Yes", "No"))
# **🔹 Count P53 Target Genes in Each Response Group**
proportion_data <- corrmotif_df_0.1 %>%
group_by(Response_Group, P53_Target) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$P53_Target <- factor(proportion_data$P53_Target, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.1)", "DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for "DOX only mid-late (0.1)" and "CX_DOX mid-late (0.1)" vs "Non Response (0.1)"**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.1)") %>%
dplyr::select(P53_Target, Count) %>%
{setNames(.$Count, .$P53_Target)} # Convert to named vector
chi_results <- proportion_data %>%
filter(Response_Group %in% c("DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[P53_Target %in% c("Yes", "No")]
if (!"Yes" %in% P53_Target) group_counts <- c(group_counts, 0)
if (!"No" %in% P53_Target) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
non_response_counts["Yes"], non_response_counts["No"]
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("DOX only mid-late (0.1)", "CX_DOX mid-late (0.1)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = P53_Target)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # Fixed Y-axis to 100%
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of P53 Target Genes in\n0.1 Corrmotif Response Groups",
x = "Response Groups (0.1 Concentration)",
y = "Percentage",
fill = "P53 Target Genes"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Proportion of P53 target Genes (0.5 micromolar)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)
# **🔹 Read P53 Target Gene List**
P53_Target <- read.csv("data/P53_Target.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
P53_Target <- P53_Target %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = P53_Target$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
P53_Target_genes <- na.omit(P53_Target$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.5 Concentration**
prob_groups_0.5 <- list(
"Non Response (0.5)" = read.csv("data/prob_1_0.5.csv")$Entrez_ID,
"DOX-specific response (0.5)" = read.csv("data/prob_2_0.5.csv")$Entrez_ID,
"DOX only mid-late response (0.5)" = read.csv("data/prob_3_0.5.csv")$Entrez_ID,
"CX DOX (early) response (0.5)" = read.csv("data/prob_4_0.5.csv")$Entrez_ID,
"DOX + CX (mid-late) response (0.5)" = read.csv("data/prob_5_0.5.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.5 <- bind_rows(
lapply(prob_groups_0.5, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with P53 Target Genes**
corrmotif_df_0.5 <- corrmotif_df_0.5 %>%
mutate(P53_Target = ifelse(Entrez_ID %in% P53_Target_genes, "Yes", "No"))
# **🔹 Count P53 Target Genes in Each Response Group**
proportion_data <- corrmotif_df_0.5 %>%
group_by(Response_Group, P53_Target) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$P53_Target <- factor(proportion_data$P53_Target, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.5)", "DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for Each Response Group vs Non-Response**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.5)") %>%
dplyr::select(P53_Target, Count) %>%
{setNames(.$Count, .$P53_Target)} # Convert to named vector
# **Comparing Each Group Against "Non Response (0.5)"**
chi_results <- proportion_data %>%
filter(Response_Group %in% c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[P53_Target %in% c("Yes", "No")]
if (!"Yes" %in% P53_Target) group_counts <- c(group_counts, 0)
if (!"No" %in% P53_Target) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2], # Response group counts
non_response_counts["Yes"], non_response_counts["No"] # Non-response counts
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 3: `Response_Group = CX DOX (early) response (0.5)`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrect# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = P53_Target)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # **Y-axis now limited to 110% for visibility**
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of P53 Target Genes in\n0.5 Corrmotif Response Groups",
x = "Response Groups (0.5 Concentration)",
y = "Percentage",
fill = "P53 Target Genes"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 IRanges_2.36.0
[4] S4Vectors_0.40.1 tidyr_1.3.1 ggplot2_3.5.1
[7] gprofiler2_0.2.3 BiocParallel_1.36.0 dplyr_1.1.4
[10] Rfast_2.1.0 RcppParallel_5.1.9 RcppZiggurat_0.1.6
[13] Rcpp_1.0.12 Cormotif_1.48.0 limma_3.58.1
[16] affy_1.80.0 Biobase_2.62.0 BiocGenerics_0.48.1
[19] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2
[4] blob_1.2.4 bitops_1.0-7 Biostrings_2.70.1
[7] RCurl_1.98-1.13 fastmap_1.1.1 lazyeval_0.2.2
[10] promises_1.3.0 digest_0.6.34 lifecycle_1.0.4
[13] statmod_1.5.0 processx_3.8.5 KEGGREST_1.42.0
[16] RSQLite_2.3.3 magrittr_2.0.3 compiler_4.3.0
[19] rlang_1.1.3 sass_0.4.9 tools_4.3.0
[22] yaml_2.3.10 data.table_1.14.10 knitr_1.49
[25] labeling_0.4.3 htmlwidgets_1.6.4 bit_4.0.5
[28] withr_3.0.2 purrr_1.0.2 grid_4.3.0
[31] preprocessCore_1.64.0 git2r_0.35.0 colorspace_2.1-0
[34] scales_1.3.0 cli_3.6.1 crayon_1.5.3
[37] rmarkdown_2.29 generics_0.1.3 rstudioapi_0.17.1
[40] httr_1.4.7 DBI_1.2.3 cachem_1.0.8
[43] stringr_1.5.1 zlibbioc_1.48.0 parallel_4.3.0
[46] XVector_0.42.0 BiocManager_1.30.25 vctrs_0.6.5
[49] jsonlite_1.8.9 callr_3.7.6 bit64_4.0.5
[52] plotly_4.10.4 jquerylib_0.1.4 affyio_1.72.0
[55] glue_1.7.0 codetools_0.2-20 ps_1.8.1
[58] stringi_1.8.3 gtable_0.3.6 GenomeInfoDb_1.38.8
[61] later_1.3.2 munsell_0.5.1 tibble_3.2.1
[64] pillar_1.10.1 htmltools_0.5.8.1 GenomeInfoDbData_1.2.11
[67] R6_2.5.1 rprojroot_2.0.4 evaluate_1.0.3
[70] png_0.1-8 memoise_2.0.1 httpuv_1.6.15
[73] bslib_0.8.0 whisker_0.4.1 xfun_0.50
[76] fs_1.6.3 getPass_0.2-4 pkgconfig_2.0.3