Figure_1
Renee Matthews
2025-02-20
Last updated: 2025-04-30
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Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2275802 | reneeisnowhere | 2025-04-30 | update site yml file |
| html | 3c8d60b | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | 67b47a9 | E. Renee Matthews | 2025-02-21 | wflow_publish("analysis/Figure_1.Rmd") |
| html | 5a24a87 | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | 80c0b6b | E. Renee Matthews | 2025-02-21 | adding external image |
| html | d82300d | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | 19f89cc | E. Renee Matthews | 2025-02-21 | adding external image |
| html | 22b314f | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | 0964e7c | E. Renee Matthews | 2025-02-21 | adding external image |
| html | 0872582 | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | d0ac226 | E. Renee Matthews | 2025-02-21 | adding external image |
| html | 7db849f | E. Renee Matthews | 2025-02-21 | Build site. |
| Rmd | 25af13e | E. Renee Matthews | 2025-02-21 | Adding first figure |
package loading
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
function(df,
col_var = NULL,
shape_var = NULL,
title = "") {
ggplot(df) + geom_point(aes_string(
x = "PC1",
y = "PC2",
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
##counts table for part B:
filt_counts_raw <- readRDS("data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS")Figure 1.A & E: Schematic of experiment and genome browser of an ESR loci.
knitr::include_graphics("assets/Figure\ 1.png", error=FALSE)
| Version | Author | Date |
|---|---|---|
| d64bc29 | E. Renee Matthews | 2025-02-21 |
Figure 1.B: PCA Plot of log2cpm open chromatin regions
n= 155,557
filt4_matrix_lcpm <- filt_counts_raw %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "D_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "A_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "B_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "C_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
as.matrix() %>%
cpm(., log = TRUE)
annotation_mat <- data.frame(timeset=colnames(filt_counts_raw)) %>%
mutate(sample = timeset) %>%
mutate(timeset=gsub("Ind1_75","D_",timeset)) %>%
mutate(timeset=gsub("Ind2_87","A_",timeset)) %>%
mutate(timeset=gsub("Ind3_77","B_",timeset)) %>%
mutate(timeset=gsub("Ind6_71","C_",timeset)) %>%
mutate(timeset = gsub("24h","_24h",timeset),
timeset = gsub("3h","_3h",timeset)) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= case_match(trt, 'DX' ~'DOX', 'E'~'EPI', 'DA'~'DNR', 'M'~'MTX', 'T'~'TRZ', 'V'~'VEH',.default = trt)) %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")))
pca_final_four <- calc_pca(t(filt4_matrix_lcpm))
pca_final_four_anno <- data.frame(annotation_mat, pca_final_four$x)
pca_final_four_anno %>%
ggplot(.,aes(x = PC1, y = PC2, col=trt, shape=time, group=indv))+
geom_point(size= 5)+
scale_color_manual(values=drug_pal)+
ggrepel::geom_text_repel(aes(label = indv))+
ggtitle(expression("PCA of log"[2]*"(cpm) of open chromatin regions"))+
theme_bw()+
guides(col="none", size =4)+
labs(y = "PC 2 (13.5%)", x ="PC 1 (22.3%)")+
theme(plot.title=element_text(size= 14,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"))
| Version | Author | Date |
|---|---|---|
| 7db849f | E. Renee Matthews | 2025-02-21 |
Figure 1.C: Cormotif plot without the decorations
## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits
limmafit.default <- function(exprs,groupid,compid) {
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(0,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for(i in 1:compnum) {
selid1 <- which(groupid == compid[i,1])
selid2 <- which(groupid == compid[i,2])
eset <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
fit <- lmFit(eset,designmat)
fit <- eBayes(fit)
limmat[,i] <- fit$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))
}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"))
group_fac_ff <- group
groupid_ff <- as.numeric(group_fac_ff)
compid_ff <- 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))
y_TMM_cpm_ff <- cpm(filt_counts_raw, log = TRUE)
colnames(y_TMM_cpm_ff) <- colnames(filt_counts_raw)
set.seed(31415)
cormotif_initial_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=1:8, max.iter = 500, runtype = "logCPM")
# saveRDS(cormotif_initial_ff,"data/Final_four_data/cormotif_ff_4_run.RDS")cormotif_initial_ff <- readRDS("data/Final_four_data/cormotif_ff_4_run.RDS")
motif_prob_ff <- cormotif_initial_ff$bestmotif$clustlike
rownames(motif_prob_ff) <- rownames(filt4_matrix_lcpm)
plotMotif(cormotif_initial_ff)
| Version | Author | Date |
|---|---|---|
| 7db849f | E. Renee Matthews | 2025-02-21 |
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)
| Version | Author | Date |
|---|---|---|
| 7db849f | E. Renee Matthews | 2025-02-21 |
Code for making response clusters
motif_prob_ff <- readRDS("data/Final_four_data/motif_prob_ff.RDS")
NR_ff <- motif_prob_ff %>%
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_ff %>%
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_ff %>%
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_ff %>%
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)Code for making median dataframes and the division of Response clusters
### making median lfc (toplist_ff is a dataframe that contains all the DAR columns from the edgeR-limma-voom pipeline. There are 1,866,720 rows which are all 155,560 open chromatin regions and log2FC with respect to the Vehicle for each time and treatment(12 time-treatment combinations))
median_df <- toplist_ff %>%
pivot_wider(., id_cols=c(time,peak), names_from = trt, values_from = logFC) %>%
rowwise() %>%
mutate(median_lfc= median(c_across(DNR:TRZ)))
median_3_lfc <- median_df %>%
dplyr::filter(time == "3 hours") %>%
ungroup() %>%
dplyr::select(time, peak,median_lfc) %>%
dplyr::rename("med_3h_lfc"=median_lfc)
median_24_lfc <- median_df %>%
dplyr::filter(time == "24 hours") %>%
ungroup() %>%
dplyr::select(time, peak,median_lfc) %>%
dplyr::rename("med_24h_lfc"=median_lfc)median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")
median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.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)
####four different clusters for ESR
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$peak)
EAR_close <- EAR_ff %>%
dplyr::filter(Peakid %in% close_3med$peak)
LR_open <- LR_ff %>%
dplyr::filter(Peakid %in% open_24med$peak)
LR_close <- LR_ff %>%
dplyr::filter(Peakid %in% close_24med$peak)
NR_gr <- NR_ff %>%
GRanges()
ESR_open <- ESR_ff %>%
dplyr::filter(Peakid %in% medA$peak)
ESR_close <- ESR_ff %>%
dplyr::filter(Peakid %in% medB$peak)
ESR_opcl <- ESR_ff %>%
dplyr::filter(Peakid %in% medC$peak)
ESR_clop <- ESR_ff %>%
dplyr::filter(Peakid %in% medD$peak)
Nine_group_list <- list("EAR_open"=EAR_open,"EAR_close"=EAR_close, "LR_open"=LR_open, "LR_close"=LR_close,"ESR_open"=ESR_open,"ESR_close"=ESR_close,"ESR_opcl"=ESR_opcl,"ESR_clop"=ESR_clop, "NR"=NR_ff)
# saveRDS(Nine_group_list,"data/Final_four_data/Nine_group_list.RDS")Figure 1.D: mean Log FC across response clusters
toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
### loading
mean_lfc <- toplist_ff %>%
mutate(EAR_open = if_else(peak %in% EAR_open$Peakid, "y", "no")) %>%
mutate(EAR_close = if_else(peak %in% EAR_close$Peakid, "y", "no")) %>%
mutate(ESR_open = if_else(peak %in% ESR_open$Peakid, "y", "no")) %>%
mutate(ESR_close= if_else(peak %in% ESR_close$Peakid, "y", "no")) %>%
mutate(ESR_opcl= if_else(peak %in% ESR_opcl$Peakid, "y", "no")) %>%
mutate(ESR_clop= if_else(peak %in% ESR_clop$Peakid, "y", "no")) %>%
mutate(LR_open= if_else(peak %in% LR_open$Peakid, "y", "no")) %>%
mutate(LR_close= if_else(peak %in% LR_close$Peakid, "y", "no")) %>%
mutate(NR = if_else(peak %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)) %>%
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") %>%
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() +
# ggtitle(" average |Log Fold| for genes across treatment each motif")+
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"
)
)
| Version | Author | Date |
|---|---|---|
| 7db849f | E. Renee Matthews | 2025-02-21 |
Figure 1.E: genomebrowser of ESR loci (see image above)
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] readxl_1.4.5
[2] smplot2_0.2.5
[3] cowplot_1.1.3
[4] ComplexHeatmap_2.22.0
[5] ggrepel_0.9.6
[6] plyranges_1.26.0
[7] ggsignif_0.6.4
[8] genomation_1.38.0
[9] edgeR_4.4.2
[10] limma_3.62.2
[11] ggpubr_0.6.0
[12] BiocParallel_1.40.0
[13] ggVennDiagram_1.5.2
[14] scales_1.3.0
[15] VennDiagram_1.7.3
[16] futile.logger_1.4.3
[17] gridExtra_2.3
[18] ggfortify_0.4.17
[19] rtracklayer_1.66.0
[20] org.Hs.eg.db_3.20.0
[21] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[22] GenomicFeatures_1.58.0
[23] AnnotationDbi_1.68.0
[24] Biobase_2.66.0
[25] GenomicRanges_1.58.0
[26] GenomeInfoDb_1.42.3
[27] IRanges_2.40.1
[28] S4Vectors_0.44.0
[29] BiocGenerics_0.52.0
[30] RColorBrewer_1.1-3
[31] broom_1.0.7
[32] kableExtra_1.4.0
[33] lubridate_1.9.4
[34] forcats_1.0.0
[35] stringr_1.5.1
[36] dplyr_1.1.4
[37] purrr_1.0.4
[38] readr_2.1.5
[39] tidyr_1.3.1
[40] tibble_3.2.1
[41] ggplot2_3.5.1
[42] tidyverse_2.0.0
[43] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.1 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.6
[13] lattice_0.22-6 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-2
[17] sass_0.4.9 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] plotrix_3.8-4 httpuv_1.6.15
[23] DBI_1.2.3 abind_1.4-8
[25] zlibbioc_1.52.0 RCurl_1.98-1.16
[27] nnet_7.3-20 git2r_0.35.0
[29] circlize_0.4.16 GenomeInfoDbData_1.2.13
[31] svglite_2.1.3 codetools_0.2-20
[33] DelayedArray_0.32.0 xml2_1.3.7
[35] tidyselect_1.2.1 shape_1.4.6.1
[37] farver_2.1.2 UCSC.utils_1.2.0
[39] base64enc_0.1-3 matrixStats_1.5.0
[41] GenomicAlignments_1.42.0 jsonlite_1.9.1
[43] GetoptLong_1.0.5 Formula_1.2-5
[45] iterators_1.0.14 systemfonts_1.2.1
[47] foreach_1.5.2 tools_4.4.2
[49] Rcpp_1.0.14 glue_1.8.0
[51] SparseArray_1.6.2 xfun_0.51
[53] MatrixGenerics_1.18.1 withr_3.0.2
[55] formatR_1.14 fastmap_1.2.0
[57] callr_3.7.6 digest_0.6.37
[59] timechange_0.3.0 R6_2.6.1
[61] seqPattern_1.38.0 colorspace_2.1-1
[63] RSQLite_2.3.9 generics_0.1.3
[65] data.table_1.17.0 htmlwidgets_1.6.4
[67] httr_1.4.7 S4Arrays_1.6.0
[69] whisker_0.4.1 pkgconfig_2.0.3
[71] gtable_0.3.6 blob_1.2.4
[73] impute_1.80.0 XVector_0.46.0
[75] htmltools_0.5.8.1 carData_3.0-5
[77] pwr_1.3-0 clue_0.3-66
[79] png_0.1-8 knitr_1.49
[81] lambda.r_1.2.4 rstudioapi_0.17.1
[83] tzdb_0.4.0 reshape2_1.4.4
[85] rjson_0.2.23 checkmate_2.3.2
[87] curl_6.2.1 zoo_1.8-13
[89] cachem_1.1.0 GlobalOptions_0.1.2
[91] KernSmooth_2.23-26 parallel_4.4.2
[93] foreign_0.8-88 restfulr_0.0.15
[95] pillar_1.10.1 vctrs_0.6.5
[97] promises_1.3.2 car_3.1-3
[99] cluster_2.1.8.1 htmlTable_2.4.3
[101] evaluate_1.0.3 cli_3.6.4
[103] locfit_1.5-9.12 compiler_4.4.2
[105] futile.options_1.0.1 Rsamtools_2.22.0
[107] rlang_1.1.5 crayon_1.5.3
[109] labeling_0.4.3 ps_1.9.0
[111] getPass_0.2-4 plyr_1.8.9
[113] fs_1.6.5 stringi_1.8.4
[115] viridisLite_0.4.2 gridBase_0.4-7
[117] munsell_0.5.1 Biostrings_2.74.1
[119] Matrix_1.7-3 BSgenome_1.74.0
[121] patchwork_1.3.0 hms_1.1.3
[123] bit64_4.6.0-1 KEGGREST_1.46.0
[125] statmod_1.5.0 SummarizedExperiment_1.36.0
[127] memoise_2.0.1 bslib_0.9.0
[129] bit_4.6.0