final_four_analysis
ERM
2024-09-23
Last updated: 2024-09-23
Checks: 7 0
Knit directory: ATAC_learning/
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Unstaged changes:
Modified: analysis/CorMotif_data_n45.Rmd
Modified: analysis/Enhancer_files_ff.Rmd
Modified: analysis/Enrichment_motif.Rmd
Modified: analysis/Peak_calling.Rmd
Modified: analysis/Raodah.Rmd
Modified: analysis/Raodah_mycount.Rmd
Modified: analysis/Smaller_set_DAR.Rmd
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Modified: analysis/index.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/final_four_analysis.Rmd)
and HTML (docs/final_four_analysis.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1777f5a | reneeisnowhere | 2024-09-23 | updated group of eight based on 4 individuals |
| Rmd | 82f2027 | reneeisnowhere | 2024-09-16 | updates to plots |
| Rmd | 7778182 | reneeisnowhere | 2024-09-10 | updates |
| Rmd | 437a792 | reneeisnowhere | 2024-09-05 | new updates |
| Rmd | a540a01 | reneeisnowhere | 2024-09-04 | adding in high conf peak set based on final files |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(Cormotif)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(eulerr)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)## 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$motif.p*Ng))
}drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
function(df,
col_var = NULL,
shape_var = NULL,
title = "") {
ggplot(df) + geom_point(aes_string(
x = "PC1",
y = "PC2",
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
pca_var_plot <- function(pca) {
# x: class == prcomp
pca.var <- pca$sdev ^ 2
pca.prop <- pca.var / sum(pca.var)
var.plot <-
qplot(PC, prop, data = data.frame(PC = 1:length(pca.prop),
prop = pca.prop)) +
labs(title = 'Variance contributed by each PC',
x = 'PC', y = 'Proportion of variance')
plot(var.plot)
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.2f", pval))
}
}
x_axis_labels = function(labels, every_nth = 1, ...) {
axis(side = 1,
at = seq_along(labels),
labels = F)
text(
x = (seq_along(labels))[seq_len(every_nth) == 1],
y = par("usr")[3] - 0.075 * (par("usr")[4] - par("usr")[3]),
labels = labels[seq_len(every_nth) == 1],
xpd = TRUE,
...
)
}Counts summary
Adding analysis of first peak counts into one large file
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
Ind1_summary <- read.csv("data/Ind1_summary.txt", row.names = 1) %>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('75DX','DOX',trt),
trt= gsub('75E','EPI', trt),
trt=gsub('75DA','DNR',trt),
trt=gsub('75M','MTX',trt),
trt=gsub('75T','TRZ',trt),
trt=gsub('75V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind2_summary <- read.csv("data/Ind2_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('87DX','DOX',trt),
trt= gsub('87E','EPI', trt),
trt=gsub('87DA','DNR',trt),
trt=gsub('87M','MTX',trt),
trt=gsub('87T','TRZ',trt),
trt=gsub('87V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind3_summary <- read.csv("data/Ind3_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('77DX','DOX',trt),
trt= gsub('77E','EPI', trt),
trt=gsub('77DA','DNR',trt),
trt=gsub('77M','MTX',trt),
trt=gsub('77T','TRZ',trt),
trt=gsub('77V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind6_summary <- read.csv("data/Ind6_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('71DX','DOX',trt),
trt= gsub('71E','EPI', trt),
trt=gsub('71DA','DNR',trt),
trt=gsub('71M','MTX',trt),
trt=gsub('71T','TRZ',trt),
trt=gsub('71V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Final_four_counts <- Ind1_summary %>% rbind(Ind2_summary) %>% rbind(Ind3_summary) %>% rbind(Ind6_summary)
Final_four_counts %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
pivot_longer(cols = reads:mapped_reads, names_to="count_type", values_to = "read_num") %>%
mutate(read_type=case_when(read_type=="fin"~"unique",.default = read_type)) %>%
tidyr::unite("Total_reads",read_type:count_type,sep = "_", remove = TRUE) %>%
tidyr::unite("Total_reads_time",Total_reads:time,sep = "_", remove = FALSE) %>%
mutate(Total_reads=factor(Total_reads, levels = c("total_reads", "total_mapped_reads","nuclear_reads","nuclear_mapped_reads","unique_reads", "unique_mapped_reads", "nodup_reads", "nodup_mapped_reads"))) %>%
dplyr::filter(Total_reads != "nuclear_reads") %>%
dplyr::filter(Total_reads != "nodup_reads") %>%
dplyr::filter(Total_reads != "unique_reads") %>%
dplyr::mutate(trt=factor(trt, levels = c("DOX", "EPI","DNR", "MTX","TRZ","VEH"))) %>%
ggplot(., aes(x=Total_reads, y=read_num, group=interaction(indv,time)))+
geom_col(aes(fill=trt), position="dodge")+
theme_minimal()+
scale_fill_manual(values=drug_pal)+
facet_wrap(~time+trt+indv,nrow = 6, ncol = 12 )+
theme(strip.text = element_text(face = "bold", hjust = 0, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'),
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust=1))
Final_four_counts %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
pivot_longer(cols = reads:mapped_reads, names_to="count_type", values_to = "read_num") %>%
dplyr::filter(read_type=="nodup"& count_type=="mapped_reads") %>%
ggplot(., aes(x=trt,y=read_num,group=(interaction(time,trt))))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
# geom_hline(yintercept = 20)+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Reads across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))
allTSSE <- readRDS( "data/all_TSSE_scores.RDS")
allTSSE %>% as.data.frame() %>%
rownames_to_column("sample") %>%
separate(sample, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(time = factor(time, levels = c("3","24"),labels = c("3 hours","24 hours"))) %>%
dplyr::filter(indv !=4 &indv !=5) %>%
ggplot(., aes(x= time, y= V1, group = indv))+
# geom_col(position= "dodge",aes(fill=trt)) %>%
geom_jitter(aes(col = trt, size = 1.5, alpha = 0.5) , position=position_jitter(0.25))+
geom_hline(yintercept=5, linetype = 3)+
geom_hline(yintercept=7, col = "blue")+
facet_wrap(~indv)+
theme_bw()+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal)+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1))
Ind1_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
Ind2_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
Ind3_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
Ind6_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")
a1<- plotAvgProf(Ind1_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 1" )>> plotting figure... 2024-09-23 9:14:25 AM b1 <- plotAvgProf(Ind2_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 2" )>> plotting figure... 2024-09-23 9:14:37 AM c1 <- plotAvgProf(Ind3_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 3" )>> plotting figure... 2024-09-23 9:14:44 AM d1 <- plotAvgProf(Ind6_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 6" )>> plotting figure... 2024-09-23 9:14:45 AM a2 <- plotAvgProf(Ind1_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 1" )>> plotting figure... 2024-09-23 9:14:45 AM b2 <- plotAvgProf(Ind2_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 2" )>> plotting figure... 2024-09-23 9:15:02 AM c2 <- plotAvgProf(Ind3_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 3" )>> plotting figure... 2024-09-23 9:15:12 AM d2 <- plotAvgProf(Ind6_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 6" )>> plotting figure... 2024-09-23 9:15:26 AM plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)
Analysis using the four selected individuals and the peaks generated from just that set:
# Peaks_bed_file <- rtracklayer::BEDFile("~/ATAC_downloads/finalfour_bl_filt_peaks.bed")
# import(Peaks_bed_file) %>% as.data.frame()
# write_delim(all_four_filt_counts, "data/all_four_filtered_counts.txt",delim = "\t")
all_four_filt_counts <- read.delim("data/all_four_filtered_counts.txt",header=TRUE) %>%
GRanges() %>%
keepStandardChromosomes(pruning.mode = "coarse") %>%
as.data.frame()# all_four_filt_counts %>%
names(all_four_filt_counts) = gsub(pattern = "_S.*", replacement = "", x = names(all_four_filt_counts))
names(all_four_filt_counts) = gsub(pattern = "ind1.trimmed.filt_files.trimmed_|ind2.trimmed.filt_files.trimmed_|ind3.trimmed.filt_files.trimmed_|ind6.trimmed.filt_files.trimmed_", replacement = "", x = names(all_four_filt_counts))
names(all_four_filt_counts) = gsub(pattern = "ind1.trimmed.filt_files.trimmed_|ind2.trimmed.filt_files.trimmed_|ind3.trimmed.filt_files.trimmed_|ind6.trimmed.filt_files.trimmed_", replacement = "", x = names(all_four_filt_counts))summary of peaks by sample
##check commandline file for how I pulled the wc -l on all files.
# peakcount_new4 <- read_delim("~/ATAC_downloads/master_peaks/4_indonly/peakcount_new4.txt",
# + delim = "\t", escape_double = FALSE,
# # + col_names = FALSE, trim_ws = TRUE)
# peakcount_new4 %>%
# mutate(X1=gsub("75","1_", X1),
# X1=gsub("87","2_",X1),
# X1=gsub("77","3_",X1),
# X1=gsub("71","6_",X1),
# X1=gsub("DA","DNR_",X1),
# X1=gsub("DX","DOX_",X1),
# X1=gsub("E","EPI_",X1),
# X1=gsub("T","TRZ_",X1),
# X1=gsub("M","MTX_",X1),
# X1=gsub("V","VEH_",X1)) %>%
# separate(X1, into=c("indv","trt","time"), sep= "_", remove = TRUE) %>%
# dplyr::rename(peak_number=X2) %>% write_delim(., file="data/Final_four_data/Peak_count_ff.txt", delim = "\t")
peakcount_ff <- read_delim("data/Final_four_data/Peak_count_ff.txt",delim= "\t")
peakcount_ff %>%
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"))) %>%
mutate(indv=factor(indv)) %>%
ggplot(., aes(x=trt,y=peak_number))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Peak number across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))
PCA4mat <- all_four_filt_counts%>%
dplyr::select(Geneid,Ind1_75DA24h:Ind6_71V3h) %>%
column_to_rownames("Geneid") %>%
as.matrix()
annotation_mat <- data.frame(timeset=colnames(PCA4mat)) %>%
mutate(sample = timeset) %>%
mutate(timeset=gsub("Ind1_75","1_",timeset)) %>%
mutate(timeset=gsub("Ind2_87","2_",timeset)) %>%
mutate(timeset=gsub("Ind3_77","3_",timeset)) %>%
mutate(timeset=gsub("Ind6_71","4_",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(indv = factor(indv, levels = c("1", "2", "3", "4", "5", "6"))) %>%
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")))
PCA4_info <- (prcomp(t(PCA4mat), scale. = TRUE))
PCA4_info_anno <- PCA4_info$x %>% cbind(.,annotation_mat)
# autoplot(PCA_info)
summary(PCA4_info)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 271.5384 161.7523 112.03795 84.95590 77.83204 62.46278
Proportion of Variance 0.4275 0.1517 0.07278 0.04185 0.03512 0.02262
Cumulative Proportion 0.4275 0.5792 0.65195 0.69380 0.72892 0.75154
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 55.50414 54.53991 50.63479 45.33378 42.9600 39.54174
Proportion of Variance 0.01786 0.01725 0.01486 0.01192 0.0107 0.00907
Cumulative Proportion 0.76940 0.78665 0.80151 0.81343 0.8241 0.83319
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 38.68789 36.80855 36.40024 34.44726 33.89483 33.36466
Proportion of Variance 0.00868 0.00786 0.00768 0.00688 0.00666 0.00645
Cumulative Proportion 0.84187 0.84973 0.85741 0.86429 0.87095 0.87740
PC19 PC20 PC21 PC22 PC23 PC24
Standard deviation 33.17758 32.55288 31.31915 30.91018 30.44559 30.19378
Proportion of Variance 0.00638 0.00614 0.00569 0.00554 0.00537 0.00529
Cumulative Proportion 0.88378 0.88993 0.89561 0.90115 0.90653 0.91181
PC25 PC26 PC27 PC28 PC29 PC30
Standard deviation 29.89402 29.6621 29.11618 28.74140 28.45611 28.05219
Proportion of Variance 0.00518 0.0051 0.00492 0.00479 0.00469 0.00456
Cumulative Proportion 0.91699 0.9221 0.92701 0.93180 0.93649 0.94106
PC31 PC32 PC33 PC34 PC35 PC36
Standard deviation 27.94847 27.87613 27.64626 27.04068 25.9458 25.73918
Proportion of Variance 0.00453 0.00451 0.00443 0.00424 0.0039 0.00384
Cumulative Proportion 0.94559 0.95009 0.95452 0.95876 0.9627 0.96651
PC37 PC38 PC39 PC40 PC41 PC42
Standard deviation 25.52298 25.2772 24.66403 24.2316 23.83300 23.52718
Proportion of Variance 0.00378 0.0037 0.00353 0.0034 0.00329 0.00321
Cumulative Proportion 0.97028 0.9740 0.97751 0.9809 0.98421 0.98742
PC43 PC44 PC45 PC46 PC47 PC48
Standard deviation 22.30846 21.65858 21.00631 20.3327 18.66373 5.022e-13
Proportion of Variance 0.00289 0.00272 0.00256 0.0024 0.00202 0.000e+00
Cumulative Proportion 0.99031 0.99303 0.99558 0.9980 1.00000 1.000e+00# %>% as.tibble() %>%
# kable(., caption = "Summary of PCA variables") %>%
# kable_paper("striped", full_width = TRUE) %>%
# kable_styling(full_width = FALSE, font_size = 16) %>%
# scroll_box(height = "500px")
# cpm(PCAmat, log=TRUE)
pca_plot(PCA4_info_anno, col_var='trt', shape_var = 'time')+ggtitle("PCA of raw counts by time and treatment")
pca_plot(PCA4_info_anno, col_var='trt', shape_var = 'indv')+ggtitle("PCA of raw counts by treatment and individual")
FCmatrix_full <- PCA4mat %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
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) %>%
cor()
filmat_groupmat_col <- data.frame(timeset = colnames(FCmatrix_full))
counts_corr_mat <-filmat_groupmat_col %>%
# mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(class = if_else(trt == "DNR", "AC",
if_else(trt == "DOX", "AC",
if_else(trt == "EPI", "AC", "nAC")))) %>%
mutate(TOP2i = if_else(trt == "DNR", "yes",
if_else(trt == "DOX", "yes",
if_else(trt == "EPI", "yes",
if_else(trt == "MTX", "yes", "no")))))
mat_colors <- list(
trt= c("#F1B72B","#8B006D","#DF707E","#3386DD","#707031","#41B333"),
indv=c("#1B9E77", "#D95F02" ,"#7570B3", "#E6AB02"),
time=c("pink", "chocolate4"),
class=c("yellow1","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
names(mat_colors$trt) <- unique(counts_corr_mat$trt)
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)
# mat_col_ff <-
# data.frame(
# time = c(rep("3 hours", 5), rep("24 hours", 5)),
# class = (c(
# "AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
# )))
# rownames(mat_col_ff) <- colnames(FCmatrix_ff)
#
# mat_colors_ff <-
# list(
# time = c("pink", "chocolate4"),
# class = c("yellow1", "lightgreen"))
# names(mat_colors_ff$time) <- unique(mat_col_ff$time)
# names(mat_colors_ff$class) <- unique(mat_col_ff$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
# corrFC_ff <- cor(FCmatrix_ff)
htanno_full <- HeatmapAnnotation(df = counts_corr_mat, col = mat_colors)
Heatmap(FCmatrix_full, top_annotation = htanno_full)
l4cpm <- cpm(PCA4mat, log=TRUE) ### for determining the basic cutoffs
dim(l4cpm)[1] 172481 48row_means <- rowMeans(l4cpm)
x4_filtered <- PCA4mat[row_means > 0,]
dim(x4_filtered)[1] 155560 48filt4_matrix_lcpm <- cpm(x4_filtered, log=TRUE)
PCA4_info_filter <- (prcomp(t(filt4_matrix_lcpm), scale. = TRUE))
summary(PCA4_info_filter)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 186.3046 145.1035 112.75334 92.12518 74.32713 65.16030
Proportion of Variance 0.2231 0.1353 0.08173 0.05456 0.03551 0.02729
Cumulative Proportion 0.2231 0.3585 0.44020 0.49476 0.53027 0.55757
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 62.14680 60.93584 55.20555 51.92794 50.29842 49.63894
Proportion of Variance 0.02483 0.02387 0.01959 0.01733 0.01626 0.01584
Cumulative Proportion 0.58240 0.60627 0.62586 0.64319 0.65945 0.67529
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 48.89413 46.56074 46.04427 45.27015 44.82248 44.19051
Proportion of Variance 0.01537 0.01394 0.01363 0.01317 0.01291 0.01255
Cumulative Proportion 0.69066 0.70460 0.71823 0.73140 0.74432 0.75687
PC19 PC20 PC21 PC22 PC23 PC24
Standard deviation 43.76294 43.54048 42.37991 41.72939 41.5544 40.40543
Proportion of Variance 0.01231 0.01219 0.01155 0.01119 0.0111 0.01049
Cumulative Proportion 0.76918 0.78137 0.79291 0.80411 0.8152 0.82570
PC25 PC26 PC27 PC28 PC29 PC30
Standard deviation 40.23834 39.2449 38.71749 38.53516 37.86304 37.48148
Proportion of Variance 0.01041 0.0099 0.00964 0.00955 0.00922 0.00903
Cumulative Proportion 0.83611 0.8460 0.85565 0.86519 0.87441 0.88344
PC31 PC32 PC33 PC34 PC35 PC36
Standard deviation 36.76445 36.31594 36.08503 35.40489 35.25544 34.3866
Proportion of Variance 0.00869 0.00848 0.00837 0.00806 0.00799 0.0076
Cumulative Proportion 0.89213 0.90061 0.90898 0.91704 0.92503 0.9326
PC37 PC38 PC39 PC40 PC41 PC42
Standard deviation 33.9352 33.63268 33.55568 32.46461 31.17213 31.02734
Proportion of Variance 0.0074 0.00727 0.00724 0.00678 0.00625 0.00619
Cumulative Proportion 0.9400 0.94730 0.95454 0.96132 0.96756 0.97375
PC43 PC44 PC45 PC46 PC47 PC48
Standard deviation 30.58641 30.12543 28.78207 27.44490 25.66405 7.625e-13
Proportion of Variance 0.00601 0.00583 0.00533 0.00484 0.00423 0.000e+00
Cumulative Proportion 0.97976 0.98560 0.99092 0.99577 1.00000 1.000e+00pca_var_plot(PCA4_info_filter)
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) new filtered peak set"))+
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"))
pca_final_four_anno %>%
ggplot(.,aes(x = PC3, y = PC4, 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) new filtered peak set"))+
theme_bw()+
guides(col="none", size =4)+
labs(y = "PC 4 (5.5%)", x ="PC 3 (8.2%)")+
theme(plot.title=element_text(size= 14,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"))
We go from 172481, 48 peaks to155560, 48 peaks using rowMeans (log2cpm) > 0 across all samples. This is my new high confidence peak set.
Contribution of variance in PCA plot
pending code
facs <- c("indv", "trt", "time")
names(facs) <- c("Individual", "Treatment", "Time")
drug1 <- c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")##for changing shapes and colors
time <- rep(c("24h", "3h"),24) %>% factor(., levels = c("3h","24h"))
##gglistmaking
for (f in facs) {
# PC1 v PC2
pca_plot(pca_final_four_anno, col_var = f, shape_var = time,
title = names(facs)[which(facs == f)])
print(last_plot())
# Plot f versus PC1 and PC2
f_v_pc1 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 1, f))
f_v_pc2 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 2, f))
grid.arrange(f_v_pc1, f_v_pc2, ncol = 2, top = names(facs)[which(facs == f)])
# summary(plot_versus_pc(PCA_info_anno_all, 1, f))
# summary(plot_versus_pc(PCA_info_anno_all, 2, f))
}
Comparing the peak sets, old and new
# write_delim((filt4_matrix_lcpm %>% as.data.frame(.) %>% rownames_to_column("Peakid")), "data/Final_four_data/LCPM_matrix_ff.txt", delim = "/t")
# filt4_matrix_lcpm <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/t")
new_peaks_list <- row.names(filt4_matrix_lcpm) %>% as.data.frame() %>% separate_wider_delim(., cols =.,names=c("chr","start","end"), delim = ".", cols_remove = FALSE) %>% dplyr::rename("peakid"=4)
new_peaks_gr <- new_peaks_list %>%
dplyr::filter(chr!="chrY") %>%
GRanges() %>%
keepStandardChromosomes(., pruning.mode = "coarse")
stdChr <- standardChromosomes(new_peaks_gr)
peakAnnoList_n45_motif <- readRDS("data/peakAnnoList_n45_motif.RDS")
old_peaks_gr <- peakAnnoList_n45_motif$background_gr %>% as.GRanges()
old_peaks_gr$old_width <- width(old_peaks_gr)
a <- join_overlap_intersect(old_peaks_gr,new_peaks_gr,suffix = c("old", "new")) %>%
as.data.frame()
a %>%
dplyr::select(id,peakid,width,old_width) %>%
mutate(check =if_else(id ==peakid, "TRUE","FALSE")) %>%
dplyr::filter(check=="FALSE") %>%
mutate(width_ol=if_else(width<old_width,"smaller","larger")) %>%
dplyr::rename("old_peak_id"="id","new_peak_id"="peakid") %>%
mutate(diff= old_width - width) %>%
kable(., caption = "summary of newpeaks that are smaller than old peaks, but are basically the same") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(
full_width = FALSE,
position = "left",
bootstrap_options = c("striped", "hover")
) %>%
scroll_box(width = "100%", height = "400px")| old_peak_id | new_peak_id | width | old_width | check | width_ol | diff |
|---|---|---|---|---|---|---|
| chr1.821114.821933 | chr1.821114.821757 | 644 | 821 | FALSE | smaller | 177 |
| chr1.920872.921848 | chr1.920872.921733 | 862 | 978 | FALSE | smaller | 116 |
| chr1.960487.960762 | chr1.960492.960762 | 271 | 277 | FALSE | smaller | 6 |
| chr1.961274.961788 | chr1.961274.961703 | 430 | 516 | FALSE | smaller | 86 |
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| chr1.232620098.232620846 | chr1.232620107.232620846 | 740 | 750 | FALSE | smaller | 10 |
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| chr1.233543500.233544163 | chr1.233543500.233544146 | 647 | 665 | FALSE | smaller | 18 |
| chr1.233707060.233708245 | chr1.233707060.233708236 | 1177 | 1187 | FALSE | smaller | 10 |
| chr1.234276287.234276647 | chr1.234276287.234276598 | 312 | 362 | FALSE | smaller | 50 |
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| chr1.234545884.234546300 | chr1.234545884.234546275 | 392 | 418 | FALSE | smaller | 26 |
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| chr1.237091639.237091977 | chr1.237091747.237091977 | 231 | 340 | FALSE | smaller | 109 |
| chr1.237108250.237108782 | chr1.237108250.237108695 | 446 | 534 | FALSE | smaller | 88 |
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| chr1.237244826.237245384 | chr1.237244826.237245341 | 516 | 560 | FALSE | smaller | 44 |
| chr1.237817181.237817792 | chr1.237817260.237817792 | 533 | 613 | FALSE | smaller | 80 |
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| chr1.238062228.238062659 | chr1.238062239.238062534 | 296 | 433 | FALSE | smaller | 137 |
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| chr1.239898096.239898448 | chr1.239898096.239898446 | 351 | 354 | FALSE | smaller | 3 |
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| chr1.240498464.240499409 | chr1.240498548.240499409 | 862 | 947 | FALSE | smaller | 85 |
| chr1.240692022.240692377 | chr1.240692027.240692377 | 351 | 357 | FALSE | smaller | 6 |
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| chr1.242763792.242764457 | chr1.242763946.242764457 | 512 | 667 | FALSE | smaller | 155 |
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| chr1.244730126.244730927 | chr1.244730126.244730879 | 754 | 803 | FALSE | smaller | 49 |
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| chr1.245085528.245086105 | chr1.245085528.245086026 | 499 | 579 | FALSE | smaller | 80 |
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| chr1.245791093.245792199 | chr1.245791135.245792199 | 1065 | 1108 | FALSE | smaller | 43 |
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| chr1.245797870.245799370 | chr1.245798515.245799370 | 856 | 1502 | FALSE | smaller | 646 |
| chr1.245856719.245857387 | chr1.245856719.245857380 | 662 | 670 | FALSE | smaller | 8 |
| chr1.246139728.246140020 | chr1.246139782.246140020 | 239 | 294 | FALSE | smaller | 55 |
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| chr1.246180959.246181689 | chr1.246181100.246181689 | 590 | 732 | FALSE | smaller | 142 |
| chr1.246200002.246200494 | chr1.246200002.246200472 | 471 | 494 | FALSE | smaller | 23 |
| chr1.246428498.246429001 | chr1.246428573.246429001 | 429 | 505 | FALSE | smaller | 76 |
| chr1.246444400.246445185 | chr1.246444471.246445185 | 715 | 787 | FALSE | smaller | 72 |
| chr1.246565847.246567056 | chr1.246565847.246567051 | 1205 | 1211 | FALSE | smaller | 6 |
| chr1.246620685.246620977 | chr1.246620685.246620973 | 289 | 294 | FALSE | smaller | 5 |
| chr1.246794326.246795125 | chr1.246794331.246795125 | 795 | 801 | FALSE | smaller | 6 |
| chr1.246798513.246798833 | chr1.246798571.246798833 | 263 | 322 | FALSE | smaller | 59 |
| chr1.246837737.246838134 | chr1.246837737.246838129 | 393 | 399 | FALSE | smaller | 6 |
| chr1.246931047.246932359 | chr1.246931047.246932355 | 1309 | 1314 | FALSE | smaller | 5 |
| chr1.247273050.247273423 | chr1.247273086.247273423 | 338 | 375 | FALSE | smaller | 37 |
| chr1.247371181.247371489 | chr1.247371192.247371489 | 298 | 310 | FALSE | smaller | 12 |
| chr1.247415285.247416090 | chr1.247415285.247416029 | 745 | 807 | FALSE | smaller | 62 |
| chr1.248752033.248753410 | chr1.248752033.248752594 | 562 | 1379 | FALSE | smaller | 817 |
| chr1.248821120.248821555 | chr1.248821120.248821533 | 414 | 437 | FALSE | smaller | 23 |
| chr2.11281.12035 | chr2.11281.11975 | 695 | 756 | FALSE | smaller | 61 |
| chr2.37992.38392 | chr2.38092.38392 | 301 | 402 | FALSE | smaller | 101 |
| chr2.114722.115816 | chr2.114722.114911 | 190 | 1096 | FALSE | smaller | 906 |
| chr2.114722.115816 | chr2.114944.115816 | 873 | 1096 | FALSE | smaller | 223 |
| chr2.263782.264467 | chr2.263806.264467 | 662 | 687 | FALSE | smaller | 25 |
| chr2.318711.320163 | chr2.318783.320163 | 1381 | 1454 | FALSE | smaller | 73 |
| chr2.393630.394434 | chr2.393701.394434 | 734 | 806 | FALSE | smaller | 72 |
| chr2.425792.426343 | chr2.425825.426324 | 500 | 553 | FALSE | smaller | 53 |
| chr2.526969.527968 | chr2.526969.527578 | 610 | 1001 | FALSE | smaller | 391 |
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| chr2.937393.937836 | chr2.937576.937836 | 261 | 445 | FALSE | smaller | 184 |
| chr2.950068.951084 | chr2.950068.950966 | 899 | 1018 | FALSE | smaller | 119 |
| chr2.960114.960592 | chr2.960114.960577 | 464 | 480 | FALSE | smaller | 16 |
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| chr2.1583442.1583774 | chr2.1583459.1583774 | 316 | 334 | FALSE | smaller | 18 |
| chr2.1704844.1705417 | chr2.1704844.1705313 | 470 | 575 | FALSE | smaller | 105 |
| chr2.1766245.1766976 | chr2.1766557.1766976 | 420 | 733 | FALSE | smaller | 313 |
| chr2.1819026.1819481 | chr2.1819026.1819425 | 400 | 457 | FALSE | smaller | 57 |
| chr2.2025175.2025554 | chr2.2025175.2025410 | 236 | 381 | FALSE | smaller | 145 |
| chr2.2572390.2572898 | chr2.2572390.2572893 | 504 | 510 | FALSE | smaller | 6 |
| chr2.2573276.2574719 | chr2.2573544.2574719 | 1176 | 1445 | FALSE | smaller | 269 |
| chr2.2664045.2664893 | chr2.2664159.2664893 | 735 | 850 | FALSE | smaller | 115 |
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| chr2.3045290.3046142 | chr2.3045430.3046142 | 713 | 854 | FALSE | smaller | 141 |
| chr2.3148110.3148859 | chr2.3148217.3148859 | 643 | 751 | FALSE | smaller | 108 |
| chr2.3170298.3172083 | chr2.3170866.3172083 | 1218 | 1787 | FALSE | smaller | 569 |
| chr2.3189221.3189671 | chr2.3189343.3189671 | 329 | 452 | FALSE | smaller | 123 |
| chr2.3200341.3200926 | chr2.3200341.3200873 | 533 | 587 | FALSE | smaller | 54 |
| chr2.3314534.3315065 | chr2.3314534.3315059 | 526 | 533 | FALSE | smaller | 7 |
| chr2.3316786.3317087 | chr2.3316793.3317087 | 295 | 303 | FALSE | smaller | 8 |
| chr2.3387764.3388879 | chr2.3387764.3388830 | 1067 | 1117 | FALSE | smaller | 50 |
| chr2.3527201.3528779 | chr2.3527201.3528712 | 1512 | 1580 | FALSE | smaller | 68 |
| chr2.3535719.3536978 | chr2.3535786.3536978 | 1193 | 1261 | FALSE | smaller | 68 |
| chr2.3650855.3651292 | chr2.3650855.3651277 | 423 | 439 | FALSE | smaller | 16 |
| chr2.3966005.3966474 | chr2.3966051.3966474 | 424 | 471 | FALSE | smaller | 47 |
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| chr2.4012199.4012516 | chr2.4012199.4012492 | 294 | 319 | FALSE | smaller | 25 |
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| chr2.4903798.4904357 | chr2.4903804.4904357 | 554 | 561 | FALSE | smaller | 7 |
| chr2.4937512.4938007 | chr2.4937512.4937881 | 370 | 497 | FALSE | smaller | 127 |
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| chr2.6694608.6694901 | chr2.6694608.6694894 | 287 | 295 | FALSE | smaller | 8 |
| chr2.6823114.6823611 | chr2.6823114.6823596 | 483 | 499 | FALSE | smaller | 16 |
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| chr2.8796376.8796915 | chr2.8796376.8796890 | 515 | 541 | FALSE | smaller | 26 |
| chr2.8893548.8893796 | chr2.8893585.8893796 | 212 | 250 | FALSE | smaller | 38 |
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| chr2.9071027.9071447 | chr2.9071027.9071394 | 368 | 422 | FALSE | smaller | 54 |
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| chr2.9124174.9124583 | chr2.9124174.9124494 | 321 | 411 | FALSE | smaller | 90 |
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| chr2.9654292.9654881 | chr2.9654301.9654881 | 581 | 591 | FALSE | smaller | 10 |
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| chr2.10122316.10123065 | chr2.10122316.10123056 | 741 | 751 | FALSE | smaller | 10 |
| chr2.10149691.10150094 | chr2.10149697.10150036 | 340 | 405 | FALSE | smaller | 65 |
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| chr2.10518107.10518898 | chr2.10518122.10518843 | 722 | 793 | FALSE | smaller | 71 |
| chr2.10565996.10566287 | chr2.10566024.10566287 | 264 | 293 | FALSE | smaller | 29 |
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| chr2.10654109.10654318 | chr2.10654116.10654318 | 203 | 211 | FALSE | smaller | 8 |
| chr2.10709091.10709852 | chr2.10709091.10709810 | 720 | 763 | FALSE | smaller | 43 |
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| chr2.10911260.10912609 | chr2.10911260.10912442 | 1183 | 1351 | FALSE | smaller | 168 |
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| chr2.11342666.11343149 | chr2.11342666.11343144 | 479 | 485 | FALSE | smaller | 6 |
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| chr2.11389803.11390203 | chr2.11389803.11390193 | 391 | 402 | FALSE | smaller | 11 |
| chr2.11444724.11445012 | chr2.11444741.11445012 | 272 | 290 | FALSE | smaller | 18 |
| chr2.11446738.11447379 | chr2.11446738.11447215 | 478 | 643 | FALSE | smaller | 165 |
| chr2.11499280.11499484 | chr2.11499280.11499481 | 202 | 206 | FALSE | smaller | 4 |
| chr2.11563350.11563941 | chr2.11563408.11563941 | 534 | 593 | FALSE | smaller | 59 |
| chr2.11618019.11619062 | chr2.11618233.11618922 | 690 | 1045 | FALSE | smaller | 355 |
| chr2.11657034.11657669 | chr2.11657127.11657647 | 521 | 637 | FALSE | smaller | 116 |
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| chr2.11842182.11842626 | chr2.11842182.11842621 | 440 | 446 | FALSE | smaller | 6 |
| chr2.12106294.12106688 | chr2.12106294.12106682 | 389 | 396 | FALSE | smaller | 7 |
| chr2.12325756.12326618 | chr2.12325756.12326548 | 793 | 864 | FALSE | smaller | 71 |
| chr2.12486490.12487133 | chr2.12486490.12486944 | 455 | 645 | FALSE | smaller | 190 |
| chr2.12554317.12554491 | chr2.12554317.12554447 | 131 | 176 | FALSE | smaller | 45 |
| chr2.12556626.12557191 | chr2.12556626.12557185 | 560 | 567 | FALSE | smaller | 7 |
| chr2.12637007.12637650 | chr2.12637007.12637531 | 525 | 645 | FALSE | smaller | 120 |
| chr2.12903095.12903773 | chr2.12903095.12903653 | 559 | 680 | FALSE | smaller | 121 |
| chr2.13821305.13821749 | chr2.13821305.13821625 | 321 | 446 | FALSE | smaller | 125 |
| chr2.14634277.14635722 | chr2.14634277.14635712 | 1436 | 1447 | FALSE | smaller | 11 |
| chr2.14745774.14747681 | chr2.14745774.14746226 | 453 | 1909 | FALSE | smaller | 1456 |
| chr2.14745774.14747681 | chr2.14746265.14747681 | 1417 | 1909 | FALSE | smaller | 492 |
| chr2.14756885.14757422 | chr2.14756885.14757380 | 496 | 539 | FALSE | smaller | 43 |
| chr2.15300527.15300992 | chr2.15300527.15300910 | 384 | 467 | FALSE | smaller | 83 |
| chr2.15632233.15632500 | chr2.15632235.15632500 | 266 | 269 | FALSE | smaller | 3 |
| chr2.15732549.15732800 | chr2.15732554.15732800 | 247 | 253 | FALSE | smaller | 6 |
| chr2.15747657.15748249 | chr2.15747709.15748249 | 541 | 594 | FALSE | smaller | 53 |
| chr2.15748752.15748963 | chr2.15748779.15748963 | 185 | 213 | FALSE | smaller | 28 |
| chr2.15749678.15750771 | chr2.15749697.15750771 | 1075 | 1095 | FALSE | smaller | 20 |
| chr2.15800824.15801280 | chr2.15800824.15801240 | 417 | 458 | FALSE | smaller | 41 |
| chr2.15939687.15942482 | chr2.15939691.15942472 | 2782 | 2797 | FALSE | smaller | 15 |
| chr2.16099929.16100510 | chr2.16099929.16100506 | 578 | 583 | FALSE | smaller | 5 |
| chr2.16112741.16114362 | chr2.16112741.16114048 | 1308 | 1623 | FALSE | smaller | 315 |
| chr2.16240303.16240973 | chr2.16240327.16240973 | 647 | 672 | FALSE | smaller | 25 |
| chr2.16281783.16282301 | chr2.16281783.16282291 | 509 | 520 | FALSE | smaller | 11 |
| chr2.16330606.16331253 | chr2.16330606.16331224 | 619 | 649 | FALSE | smaller | 30 |
| chr2.16393830.16395142 | chr2.16393830.16395115 | 1286 | 1314 | FALSE | smaller | 28 |
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| chr2.16493929.16494190 | chr2.16493929.16494129 | 201 | 263 | FALSE | smaller | 62 |
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| chr2.240457631.240458018 | chr2.240457663.240457982 | 320 | 389 | FALSE | smaller | 69 |
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| chr2.240877984.240878330 | chr2.240878012.240878330 | 319 | 348 | FALSE | smaller | 29 |
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| chr3.11555301.11555621 | chr3.11555324.11555621 | 298 | 322 | FALSE | smaller | 24 |
| chr3.11568505.11569144 | chr3.11568505.11569069 | 565 | 641 | FALSE | smaller | 76 |
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| chr3.11803921.11804367 | chr3.11803938.11804367 | 430 | 448 | FALSE | smaller | 18 |
| chr3.12513771.12514095 | chr3.12513771.12514001 | 231 | 326 | FALSE | smaller | 95 |
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| chr3.12741004.12741566 | chr3.12741024.12741566 | 543 | 564 | FALSE | smaller | 21 |
| chr3.12798345.12798905 | chr3.12798388.12798905 | 518 | 562 | FALSE | smaller | 44 |
| chr3.12875854.12876981 | chr3.12875854.12876702 | 849 | 1129 | FALSE | smaller | 280 |
| chr3.12905030.12905893 | chr3.12905030.12905802 | 773 | 865 | FALSE | smaller | 92 |
| chr3.12922164.12922712 | chr3.12922260.12922712 | 453 | 550 | FALSE | smaller | 97 |
| chr3.12935421.12935735 | chr3.12935421.12935697 | 277 | 316 | FALSE | smaller | 39 |
| chr3.12952982.12953740 | chr3.12953083.12953740 | 658 | 760 | FALSE | smaller | 102 |
| chr3.12969458.12969775 | chr3.12969459.12969775 | 317 | 319 | FALSE | smaller | 2 |
| chr3.12984022.12984725 | chr3.12984077.12984725 | 649 | 705 | FALSE | smaller | 56 |
| chr3.12992640.12994037 | chr3.12992640.12994035 | 1396 | 1399 | FALSE | smaller | 3 |
| chr3.13008146.13008415 | chr3.13008146.13008400 | 255 | 271 | FALSE | smaller | 16 |
| chr3.13072102.13073366 | chr3.13072102.13073361 | 1260 | 1266 | FALSE | smaller | 6 |
| chr3.13192783.13193156 | chr3.13192783.13193150 | 368 | 375 | FALSE | smaller | 7 |
| chr3.13199296.13200216 | chr3.13199792.13200216 | 425 | 922 | FALSE | smaller | 497 |
| chr3.13232241.13232587 | chr3.13232246.13232587 | 342 | 348 | FALSE | smaller | 6 |
| chr3.13308411.13308891 | chr3.13308411.13308785 | 375 | 482 | FALSE | smaller | 107 |
| chr3.13364943.13365688 | chr3.13364943.13365532 | 590 | 747 | FALSE | smaller | 157 |
| chr3.13377961.13378620 | chr3.13377961.13378615 | 655 | 661 | FALSE | smaller | 6 |
| chr3.13484995.13486057 | chr3.13484995.13485766 | 772 | 1064 | FALSE | smaller | 292 |
| chr3.13500235.13500633 | chr3.13500235.13500617 | 383 | 400 | FALSE | smaller | 17 |
| chr3.13548544.13549329 | chr3.13548652.13549329 | 678 | 787 | FALSE | smaller | 109 |
| chr3.13645987.13646320 | chr3.13646006.13646320 | 315 | 335 | FALSE | smaller | 20 |
| chr3.13753872.13754257 | chr3.13753948.13754241 | 294 | 387 | FALSE | smaller | 93 |
| chr3.13850055.13850856 | chr3.13850122.13850762 | 641 | 803 | FALSE | smaller | 162 |
| chr3.13890568.13890915 | chr3.13890568.13890845 | 278 | 349 | FALSE | smaller | 71 |
| chr3.14273922.14274399 | chr3.14273922.14274293 | 372 | 479 | FALSE | smaller | 107 |
| chr3.14440382.14440793 | chr3.14440383.14440793 | 411 | 413 | FALSE | smaller | 2 |
| chr3.14480252.14480993 | chr3.14480252.14480946 | 695 | 743 | FALSE | smaller | 48 |
| chr3.14555469.14555997 | chr3.14555469.14555987 | 519 | 530 | FALSE | smaller | 11 |
| chr3.14587938.14588364 | chr3.14587938.14588352 | 415 | 428 | FALSE | smaller | 13 |
| chr3.14643698.14644201 | chr3.14643733.14644201 | 469 | 505 | FALSE | smaller | 36 |
| chr3.14767704.14768131 | chr3.14767704.14768088 | 385 | 429 | FALSE | smaller | 44 |
| chr3.14810147.14811564 | chr3.14810147.14810575 | 429 | 1419 | FALSE | smaller | 990 |
| chr3.14810147.14811564 | chr3.14810680.14811564 | 885 | 1419 | FALSE | smaller | 534 |
| chr3.15174913.15175233 | chr3.15174913.15175228 | 316 | 322 | FALSE | smaller | 6 |
| chr3.15241487.15241597 | chr3.15241487.15241596 | 110 | 112 | FALSE | smaller | 2 |
| chr3.15310110.15311155 | chr3.15310110.15311123 | 1014 | 1047 | FALSE | smaller | 33 |
| chr3.15324517.15324726 | chr3.15324518.15324726 | 209 | 211 | FALSE | smaller | 2 |
| chr3.15523933.15524316 | chr3.15523938.15524316 | 379 | 385 | FALSE | smaller | 6 |
| chr3.15601037.15602322 | chr3.15601185.15602321 | 1137 | 1287 | FALSE | smaller | 150 |
| chr3.16074318.16074665 | chr3.16074376.16074665 | 290 | 349 | FALSE | smaller | 59 |
| chr3.16151131.16151442 | chr3.16151146.16151432 | 287 | 313 | FALSE | smaller | 26 |
| chr3.16467379.16469159 | chr3.16467383.16469159 | 1777 | 1782 | FALSE | smaller | 5 |
| chr3.16473610.16474796 | chr3.16473634.16474796 | 1163 | 1188 | FALSE | smaller | 25 |
| chr3.16604882.16606246 | chr3.16604882.16605473 | 592 | 1366 | FALSE | smaller | 774 |
| chr3.17094586.17095127 | chr3.17094606.17095127 | 522 | 543 | FALSE | smaller | 21 |
| chr3.17157535.17158129 | chr3.17157535.17158116 | 582 | 596 | FALSE | smaller | 14 |
| chr3.17179580.17180101 | chr3.17179621.17180101 | 481 | 523 | FALSE | smaller | 42 |
| chr3.17190366.17191052 | chr3.17190530.17191052 | 523 | 688 | FALSE | smaller | 165 |
| chr3.17494218.17494877 | chr3.17494316.17494867 | 552 | 661 | FALSE | smaller | 109 |
| chr3.17552723.17553435 | chr3.17552723.17553431 | 709 | 714 | FALSE | smaller | 5 |
| chr3.17662409.17663078 | chr3.17662409.17663063 | 655 | 671 | FALSE | smaller | 16 |
| chr3.18124498.18125024 | chr3.18124502.18125024 | 523 | 528 | FALSE | smaller | 5 |
| chr3.19147948.19148372 | chr3.19148013.19148372 | 360 | 426 | FALSE | smaller | 66 |
| chr3.19757662.19758175 | chr3.19757804.19758175 | 372 | 515 | FALSE | smaller | 143 |
| chr3.19934148.19934339 | chr3.19934176.19934339 | 164 | 193 | FALSE | smaller | 29 |
| chr3.20080963.20081579 | chr3.20080963.20081578 | 616 | 618 | FALSE | smaller | 2 |
| chr3.20511863.20512510 | chr3.20511863.20512379 | 517 | 649 | FALSE | smaller | 132 |
| chr3.20962514.20963082 | chr3.20962519.20963082 | 564 | 570 | FALSE | smaller | 6 |
| chr3.21017134.21017828 | chr3.21017134.21017813 | 680 | 696 | FALSE | smaller | 16 |
| chr3.21484167.21484837 | chr3.21484179.21484837 | 659 | 672 | FALSE | smaller | 13 |
| chr3.22979968.22980746 | chr3.22980036.22980731 | 696 | 780 | FALSE | smaller | 84 |
| chr3.23052494.23053079 | chr3.23052658.23053079 | 422 | 587 | FALSE | smaller | 165 |
| chr3.23096086.23096454 | chr3.23096086.23096417 | 332 | 370 | FALSE | smaller | 38 |
| chr3.23159667.23160513 | chr3.23159667.23160397 | 731 | 848 | FALSE | smaller | 117 |
| chr3.23485962.23486818 | chr3.23486087.23486818 | 732 | 858 | FALSE | smaller | 126 |
| chr3.23620060.23621083 | chr3.23620094.23621083 | 990 | 1025 | FALSE | smaller | 35 |
| chr3.23882445.23883033 | chr3.23882445.23882957 | 513 | 590 | FALSE | smaller | 77 |
| chr3.23944908.23946663 | chr3.23944953.23946663 | 1711 | 1757 | FALSE | smaller | 46 |
| chr3.24373313.24373710 | chr3.24373332.24373710 | 379 | 399 | FALSE | smaller | 20 |
| chr3.24487589.24488344 | chr3.24487591.24488344 | 754 | 757 | FALSE | smaller | 3 |
| chr3.24521135.24522469 | chr3.24521171.24522438 | 1268 | 1336 | FALSE | smaller | 68 |
| chr3.24524319.24524640 | chr3.24524340.24524640 | 301 | 323 | FALSE | smaller | 22 |
| chr3.25365765.25366121 | chr3.25365765.25366045 | 281 | 358 | FALSE | smaller | 77 |
| chr3.25433639.25434376 | chr3.25433654.25434376 | 723 | 739 | FALSE | smaller | 16 |
| chr3.25455477.25456007 | chr3.25455491.25456007 | 517 | 532 | FALSE | smaller | 15 |
| chr3.25789780.25790530 | chr3.25789780.25790524 | 745 | 752 | FALSE | smaller | 7 |
| chr3.26570213.26570576 | chr3.26570226.26570576 | 351 | 365 | FALSE | smaller | 14 |
| chr3.26622442.26624000 | chr3.26622477.26624000 | 1524 | 1560 | FALSE | smaller | 36 |
| chr3.27680851.27681568 | chr3.27680851.27681425 | 575 | 719 | FALSE | smaller | 144 |
| chr3.27729930.27730457 | chr3.27729930.27730435 | 506 | 529 | FALSE | smaller | 23 |
| chr3.28672351.28672665 | chr3.28672351.28672657 | 307 | 316 | FALSE | smaller | 9 |
| chr3.28745502.28746448 | chr3.28745550.28746448 | 899 | 948 | FALSE | smaller | 49 |
| chr3.28799110.28799390 | chr3.28799112.28799390 | 279 | 282 | FALSE | smaller | 3 |
| chr3.29311880.29312085 | chr3.29311882.29312085 | 204 | 207 | FALSE | smaller | 3 |
| chr3.29330128.29330627 | chr3.29330198.29330627 | 430 | 501 | FALSE | smaller | 71 |
| chr3.29639654.29640261 | chr3.29639654.29640223 | 570 | 609 | FALSE | smaller | 39 |
| chr3.29701362.29702054 | chr3.29701408.29702054 | 647 | 694 | FALSE | smaller | 47 |
| chr3.29718584.29719018 | chr3.29718589.29719015 | 427 | 436 | FALSE | smaller | 9 |
| chr3.29763954.29764875 | chr3.29763954.29764871 | 918 | 923 | FALSE | smaller | 5 |
| chr3.30606203.30607465 | chr3.30606203.30607442 | 1240 | 1264 | FALSE | smaller | 24 |
| chr3.31021597.31021822 | chr3.31021597.31021804 | 208 | 227 | FALSE | smaller | 19 |
| chr3.31196951.31197297 | chr3.31196953.31197297 | 345 | 348 | FALSE | smaller | 3 |
| chr3.31356980.31357202 | chr3.31357013.31357202 | 190 | 224 | FALSE | smaller | 34 |
| chr3.31361424.31362248 | chr3.31361424.31362232 | 809 | 826 | FALSE | smaller | 17 |
| chr3.31531803.31534314 | chr3.31531846.31534314 | 2469 | 2513 | FALSE | smaller | 44 |
| chr3.31546243.31546508 | chr3.31546244.31546508 | 265 | 267 | FALSE | smaller | 2 |
| chr3.31732488.31733302 | chr3.31732094.31732487 | 1 | 816 | FALSE | smaller | 815 |
| chr3.31771083.31771490 | chr3.31771083.31771370 | 288 | 409 | FALSE | smaller | 121 |
| chr3.31980366.31982043 | chr3.31980366.31982017 | 1652 | 1679 | FALSE | smaller | 27 |
| chr3.32143868.32144738 | chr3.32143902.32144738 | 837 | 872 | FALSE | smaller | 35 |
| chr3.32177179.32178310 | chr3.32177179.32178304 | 1126 | 1133 | FALSE | smaller | 7 |
| chr3.32864559.32864872 | chr3.32864559.32864829 | 271 | 315 | FALSE | smaller | 44 |
| chr3.33170482.33170898 | chr3.33170499.33170898 | 400 | 418 | FALSE | smaller | 18 |
| chr3.33299491.33300570 | chr3.33299493.33300570 | 1078 | 1081 | FALSE | smaller | 3 |
| chr3.33377846.33378400 | chr3.33377913.33378400 | 488 | 556 | FALSE | smaller | 68 |
| chr3.33446386.33447739 | chr3.33446390.33447739 | 1350 | 1355 | FALSE | smaller | 5 |
| chr3.34178352.34178880 | chr3.34178352.34178871 | 520 | 530 | FALSE | smaller | 10 |
| chr3.34207031.34208160 | chr3.34207057.34208160 | 1104 | 1131 | FALSE | smaller | 27 |
| chr3.34260705.34261430 | chr3.34260705.34261391 | 687 | 727 | FALSE | smaller | 40 |
| chr3.34357466.34357982 | chr3.34357493.34357982 | 490 | 518 | FALSE | smaller | 28 |
| chr3.34862471.34862885 | chr3.34862471.34862863 | 393 | 416 | FALSE | smaller | 23 |
| chr3.35505046.35505737 | chr3.35505046.35505717 | 672 | 693 | FALSE | smaller | 21 |
| chr3.35664360.35665172 | chr3.35664473.35665172 | 700 | 814 | FALSE | smaller | 114 |
| chr3.35745833.35746222 | chr3.35745833.35746043 | 211 | 391 | FALSE | smaller | 180 |
| chr3.35954261.35955082 | chr3.35954261.35955059 | 799 | 823 | FALSE | smaller | 24 |
| chr3.36362016.36362519 | chr3.36362016.36362473 | 458 | 505 | FALSE | smaller | 47 |
| chr3.36446285.36446716 | chr3.36446288.36446716 | 429 | 433 | FALSE | smaller | 4 |
| chr3.36759270.36759514 | chr3.36759277.36759514 | 238 | 246 | FALSE | smaller | 8 |
| chr3.36763800.36765082 | chr3.36763800.36764972 | 1173 | 1284 | FALSE | smaller | 111 |
| chr3.36893099.36893451 | chr3.36893134.36893451 | 318 | 354 | FALSE | smaller | 36 |
| chr3.36923465.36924009 | chr3.36923525.36924009 | 485 | 546 | FALSE | smaller | 61 |
| chr3.36943831.36944977 | chr3.36943831.36944936 | 1106 | 1148 | FALSE | smaller | 42 |
| chr3.37031316.37031652 | chr3.37031317.37031652 | 336 | 338 | FALSE | smaller | 2 |
| chr3.37055179.37055399 | chr3.37055205.37055399 | 195 | 222 | FALSE | smaller | 27 |
| chr3.37208605.37208859 | chr3.37208605.37208858 | 254 | 256 | FALSE | smaller | 2 |
| chr3.37274453.37274850 | chr3.37274453.37274795 | 343 | 399 | FALSE | smaller | 56 |
| chr3.37453360.37454309 | chr3.37453420.37454309 | 890 | 951 | FALSE | smaller | 61 |
| chr3.37479929.37480366 | chr3.37479929.37480350 | 422 | 439 | FALSE | smaller | 17 |
| chr3.37484610.37485306 | chr3.37484610.37485182 | 573 | 698 | FALSE | smaller | 125 |
| chr3.37491381.37492087 | chr3.37491381.37492065 | 685 | 708 | FALSE | smaller | 23 |
| chr3.37499090.37500048 | chr3.37499202.37500048 | 847 | 960 | FALSE | smaller | 113 |
| chr3.37765691.37765942 | chr3.37765691.37765937 | 247 | 253 | FALSE | smaller | 6 |
| chr3.37825861.37826686 | chr3.37825872.37826686 | 815 | 827 | FALSE | smaller | 12 |
| chr3.37860278.37861386 | chr3.37860278.37861305 | 1028 | 1110 | FALSE | smaller | 82 |
| chr3.38004191.38005050 | chr3.38004257.38004900 | 644 | 861 | FALSE | smaller | 217 |
| chr3.38029398.38029953 | chr3.38029398.38029920 | 523 | 557 | FALSE | smaller | 34 |
| chr3.38084629.38085778 | chr3.38084629.38085607 | 979 | 1151 | FALSE | smaller | 172 |
| chr3.38099150.38099774 | chr3.38099190.38099774 | 585 | 626 | FALSE | smaller | 41 |
| chr3.38264060.38265273 | chr3.38264060.38265229 | 1170 | 1215 | FALSE | smaller | 45 |
| chr3.38270715.38270950 | chr3.38270715.38270914 | 200 | 237 | FALSE | smaller | 37 |
| chr3.38397308.38397854 | chr3.38397367.38397854 | 488 | 548 | FALSE | smaller | 60 |
| chr3.38454129.38456258 | chr3.38454129.38456132 | 2004 | 2131 | FALSE | smaller | 127 |
| chr3.38472843.38473816 | chr3.38473040.38473816 | 777 | 975 | FALSE | smaller | 198 |
| chr3.38479612.38480088 | chr3.38479618.38480088 | 471 | 478 | FALSE | smaller | 7 |
| chr3.38500763.38501788 | chr3.38500763.38501699 | 937 | 1027 | FALSE | smaller | 90 |
| chr3.38574783.38575111 | chr3.38574783.38575088 | 306 | 330 | FALSE | smaller | 24 |
| chr3.38598258.38598814 | chr3.38598416.38598814 | 399 | 558 | FALSE | smaller | 159 |
| chr3.39125359.39126986 | chr3.39125359.39126917 | 1559 | 1629 | FALSE | smaller | 70 |
| chr3.39146741.39147558 | chr3.39146768.39147558 | 791 | 819 | FALSE | smaller | 28 |
| chr3.39180340.39181309 | chr3.39180407.39181309 | 903 | 971 | FALSE | smaller | 68 |
| chr3.39808718.39810263 | chr3.39808718.39810260 | 1543 | 1547 | FALSE | smaller | 4 |
| chr3.39978273.39978569 | chr3.39978273.39978551 | 279 | 298 | FALSE | smaller | 19 |
| chr3.40211452.40211887 | chr3.40211540.40211887 | 348 | 437 | FALSE | smaller | 89 |
| chr3.40457179.40457806 | chr3.40457179.40457767 | 589 | 629 | FALSE | smaller | 40 |
| chr3.40497288.40497748 | chr3.40497403.40497744 | 342 | 462 | FALSE | smaller | 120 |
| chr3.40504652.40505056 | chr3.40504652.40505038 | 387 | 406 | FALSE | smaller | 19 |
| chr3.40580453.40580978 | chr3.40580453.40580934 | 482 | 527 | FALSE | smaller | 45 |
| chr3.40602518.40603111 | chr3.40602872.40603111 | 240 | 595 | FALSE | smaller | 355 |
| chr3.40615308.40616582 | chr3.40615317.40616582 | 1266 | 1276 | FALSE | smaller | 10 |
| chr3.40930124.40930638 | chr3.40930124.40930627 | 504 | 516 | FALSE | smaller | 12 |
| chr3.40971170.40972120 | chr3.40971192.40972120 | 929 | 952 | FALSE | smaller | 23 |
| chr3.40989506.40990070 | chr3.40989754.40990070 | 317 | 566 | FALSE | smaller | 249 |
| chr3.41075912.41076349 | chr3.41075941.41076349 | 409 | 439 | FALSE | smaller | 30 |
| chr3.41101935.41102558 | chr3.41102018.41102558 | 541 | 625 | FALSE | smaller | 84 |
| chr3.41247172.41247956 | chr3.41247172.41247896 | 725 | 786 | FALSE | smaller | 61 |
| chr3.41265798.41266419 | chr3.41265798.41266398 | 601 | 623 | FALSE | smaller | 22 |
| chr3.41304629.41305884 | chr3.41304629.41305641 | 1013 | 1257 | FALSE | smaller | 244 |
| chr3.41352314.41353189 | chr3.41352314.41352971 | 658 | 877 | FALSE | smaller | 219 |
| chr3.41362434.41363692 | chr3.41362434.41363550 | 1117 | 1260 | FALSE | smaller | 143 |
| chr3.41527119.41527605 | chr3.41527119.41527580 | 462 | 488 | FALSE | smaller | 26 |
| chr3.41710844.41711372 | chr3.41710844.41711335 | 492 | 530 | FALSE | smaller | 38 |
| chr3.41961368.41962637 | chr3.41961373.41962633 | 1261 | 1271 | FALSE | smaller | 10 |
| chr3.41998067.41998508 | chr3.41998067.41998487 | 421 | 443 | FALSE | smaller | 22 |
| chr3.42035890.42036864 | chr3.42035890.42036736 | 847 | 976 | FALSE | smaller | 129 |
| chr3.42051717.42053205 | chr3.42051717.42052418 | 702 | 1490 | FALSE | smaller | 788 |
| chr3.42118815.42119268 | chr3.42118829.42119268 | 440 | 455 | FALSE | smaller | 15 |
| chr3.42399085.42399603 | chr3.42399140.42399589 | 450 | 520 | FALSE | smaller | 70 |
| chr3.42407865.42408131 | chr3.42407882.42408131 | 250 | 268 | FALSE | smaller | 18 |
| chr3.42489880.42490915 | chr3.42490007.42490915 | 909 | 1037 | FALSE | smaller | 128 |
| chr3.42515386.42516025 | chr3.42515452.42516025 | 574 | 641 | FALSE | smaller | 67 |
| chr3.42520925.42521224 | chr3.42520950.42521224 | 275 | 301 | FALSE | smaller | 26 |
| chr3.42772472.42773593 | chr3.42772573.42773593 | 1021 | 1123 | FALSE | smaller | 102 |
| chr3.42780790.42781278 | chr3.42780790.42781250 | 461 | 490 | FALSE | smaller | 29 |
| chr3.42805122.42805513 | chr3.42805122.42805511 | 390 | 393 | FALSE | smaller | 3 |
| chr3.42839734.42840088 | chr3.42839734.42840063 | 330 | 356 | FALSE | smaller | 26 |
| chr3.43094510.43095121 | chr3.43094510.43095063 | 554 | 613 | FALSE | smaller | 59 |
| chr3.43102794.43103625 | chr3.43102794.43103548 | 755 | 833 | FALSE | smaller | 78 |
| chr3.43473275.43473600 | chr3.43473275.43473500 | 226 | 327 | FALSE | smaller | 101 |
| chr3.43736727.43737407 | chr3.43736748.43737407 | 660 | 682 | FALSE | smaller | 22 |
| chr3.43747673.43748053 | chr3.43747678.43748053 | 376 | 382 | FALSE | smaller | 6 |
| chr3.43769367.43771282 | chr3.43769372.43771282 | 1911 | 1917 | FALSE | smaller | 6 |
| chr3.43970978.43971272 | chr3.43971061.43971272 | 212 | 296 | FALSE | smaller | 84 |
| chr3.43994758.43995625 | chr3.43994884.43995625 | 742 | 869 | FALSE | smaller | 127 |
| chr3.44036940.44037420 | chr3.44036967.44037420 | 454 | 482 | FALSE | smaller | 28 |
| chr3.44048164.44048704 | chr3.44048176.44048704 | 529 | 542 | FALSE | smaller | 13 |
| chr3.44403762.44404396 | chr3.44403762.44404260 | 499 | 636 | FALSE | smaller | 137 |
| chr3.44712358.44712998 | chr3.44712358.44712975 | 618 | 642 | FALSE | smaller | 24 |
| chr3.44913859.44914348 | chr3.44913888.44914348 | 461 | 491 | FALSE | smaller | 30 |
| chr3.44981175.44981712 | chr3.44981193.44981712 | 520 | 539 | FALSE | smaller | 19 |
| chr3.45036993.45038173 | chr3.45037077.45038173 | 1097 | 1182 | FALSE | smaller | 85 |
| chr3.45218709.45219348 | chr3.45218729.45219348 | 620 | 641 | FALSE | smaller | 21 |
| chr3.45225003.45226682 | chr3.45225003.45226643 | 1641 | 1681 | FALSE | smaller | 40 |
| chr3.45367247.45368036 | chr3.45367453.45368036 | 584 | 791 | FALSE | smaller | 207 |
| chr3.45425284.45425611 | chr3.45425329.45425611 | 283 | 329 | FALSE | smaller | 46 |
| chr3.45507868.45508448 | chr3.45507870.45508448 | 579 | 582 | FALSE | smaller | 3 |
| chr3.45628500.45629129 | chr3.45628500.45629127 | 628 | 631 | FALSE | smaller | 3 |
| chr3.45688294.45689844 | chr3.45688476.45689844 | 1369 | 1552 | FALSE | smaller | 183 |
| chr3.45716343.45717440 | chr3.45716592.45717440 | 849 | 1099 | FALSE | smaller | 250 |
| chr3.45792983.45793863 | chr3.45793055.45793863 | 809 | 882 | FALSE | smaller | 73 |
| chr3.45831480.45831734 | chr3.45831482.45831734 | 253 | 256 | FALSE | smaller | 3 |
| chr3.45906932.45907334 | chr3.45906932.45907165 | 234 | 404 | FALSE | smaller | 170 |
| chr3.45955230.45955624 | chr3.45955359.45955624 | 266 | 396 | FALSE | smaller | 130 |
| chr3.45968270.45969155 | chr3.45968290.45969155 | 866 | 887 | FALSE | smaller | 21 |
| chr3.46089287.46089797 | chr3.46089287.46089582 | 296 | 512 | FALSE | smaller | 216 |
| chr3.46447224.46447762 | chr3.46447241.46447762 | 522 | 540 | FALSE | smaller | 18 |
| chr3.46557877.46558796 | chr3.46558086.46558796 | 711 | 921 | FALSE | smaller | 210 |
| chr3.46664821.46665172 | chr3.46664828.46665172 | 345 | 353 | FALSE | smaller | 8 |
| chr3.46704768.46705612 | chr3.46704873.46705612 | 740 | 846 | FALSE | smaller | 106 |
| chr3.46716769.46718038 | chr3.46716769.46717933 | 1165 | 1271 | FALSE | smaller | 106 |
| chr3.46833378.46834589 | chr3.46833727.46834253 | 527 | 1213 | FALSE | smaller | 686 |
| chr3.46845637.46846522 | chr3.46845637.46846488 | 852 | 887 | FALSE | smaller | 35 |
| chr3.46847018.46847496 | chr3.46847018.46847460 | 443 | 480 | FALSE | smaller | 37 |
| chr3.46877407.46877986 | chr3.46877407.46877796 | 390 | 581 | FALSE | smaller | 191 |
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| chr3.46895300.46895457 | chr3.46895320.46895457 | 138 | 159 | FALSE | smaller | 21 |
| chr3.46909364.46910011 | chr3.46909426.46910011 | 586 | 649 | FALSE | smaller | 63 |
| chr3.46923402.46924026 | chr3.46923464.46924026 | 563 | 626 | FALSE | smaller | 63 |
| chr3.46925869.46926520 | chr3.46925871.46926520 | 650 | 653 | FALSE | smaller | 3 |
| chr3.46931642.46932107 | chr3.46931677.46932107 | 431 | 467 | FALSE | smaller | 36 |
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| chr3.46979393.46980028 | chr3.46979419.46980028 | 610 | 637 | FALSE | smaller | 27 |
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| chr3.47577467.47578314 | chr3.47577639.47578314 | 676 | 849 | FALSE | smaller | 173 |
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| chr3.47614630.47615119 | chr3.47614630.47615110 | 481 | 491 | FALSE | smaller | 10 |
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| chr3.47835932.47836197 | chr3.47835932.47836170 | 239 | 267 | FALSE | smaller | 28 |
| chr3.47854619.47855072 | chr3.47854619.47855013 | 395 | 455 | FALSE | smaller | 60 |
| chr3.47870067.47870438 | chr3.47870249.47870438 | 190 | 373 | FALSE | smaller | 183 |
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| chr3.48155124.48155401 | chr3.48155162.48155386 | 225 | 279 | FALSE | smaller | 54 |
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| chr3.48223155.48223585 | chr3.48223317.48223488 | 172 | 432 | FALSE | smaller | 260 |
| chr3.48433852.48434221 | chr3.48433861.48434221 | 361 | 371 | FALSE | smaller | 10 |
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| chr3.48507576.48508092 | chr3.48507625.48508092 | 468 | 518 | FALSE | smaller | 50 |
| chr3.48535691.48536373 | chr3.48535691.48536325 | 635 | 684 | FALSE | smaller | 49 |
| chr3.48536723.48537167 | chr3.48536723.48537078 | 356 | 446 | FALSE | smaller | 90 |
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| chr3.48661171.48662287 | chr3.48661237.48662287 | 1051 | 1118 | FALSE | smaller | 67 |
| chr3.48694320.48695172 | chr3.48694320.48695097 | 778 | 854 | FALSE | smaller | 76 |
| chr3.48715320.48715513 | chr3.48715320.48715503 | 184 | 195 | FALSE | smaller | 11 |
| chr3.48819715.48819993 | chr3.48819722.48819993 | 272 | 280 | FALSE | smaller | 8 |
| chr3.48820309.48821314 | chr3.48820309.48821309 | 1001 | 1007 | FALSE | smaller | 6 |
| chr3.48881040.48882165 | chr3.48881040.48882139 | 1100 | 1127 | FALSE | smaller | 27 |
| chr3.48898133.48899196 | chr3.48898133.48899146 | 1014 | 1065 | FALSE | smaller | 51 |
| chr3.48918125.48920058 | chr3.48918141.48920058 | 1918 | 1935 | FALSE | smaller | 17 |
| chr3.49012583.49012851 | chr3.49012591.49012851 | 261 | 270 | FALSE | smaller | 9 |
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| chr3.49294468.49294803 | chr3.49294468.49294675 | 208 | 337 | FALSE | smaller | 129 |
| chr3.49422091.49423060 | chr3.49422091.49423046 | 956 | 971 | FALSE | smaller | 15 |
| chr3.49456354.49456721 | chr3.49456407.49456721 | 315 | 369 | FALSE | smaller | 54 |
| chr3.49607319.49607610 | chr3.49607391.49607610 | 220 | 293 | FALSE | smaller | 73 |
| chr3.49718018.49718873 | chr3.49718018.49718709 | 692 | 857 | FALSE | smaller | 165 |
| chr3.49719113.49719863 | chr3.49719364.49719863 | 500 | 752 | FALSE | smaller | 252 |
| chr3.49799805.49799974 | chr3.49799805.49799966 | 162 | 171 | FALSE | smaller | 9 |
| chr3.49802541.49803740 | chr3.49802541.49803680 | 1140 | 1201 | FALSE | smaller | 61 |
| chr3.49856206.49857040 | chr3.49856269.49857040 | 772 | 836 | FALSE | smaller | 64 |
| chr3.49902653.49903534 | chr3.49902653.49903324 | 672 | 883 | FALSE | smaller | 211 |
| chr3.49913022.49913285 | chr3.49913056.49913285 | 230 | 265 | FALSE | smaller | 35 |
| chr3.49973231.49973964 | chr3.49973231.49973932 | 702 | 735 | FALSE | smaller | 33 |
| chr3.50057968.50058212 | chr3.50057987.50058212 | 226 | 246 | FALSE | smaller | 20 |
| chr3.50257077.50257441 | chr3.50257077.50257436 | 360 | 366 | FALSE | smaller | 6 |
| chr3.50259842.50260942 | chr3.50259863.50260942 | 1080 | 1102 | FALSE | smaller | 22 |
| chr3.50272978.50273427 | chr3.50272978.50273349 | 372 | 451 | FALSE | smaller | 79 |
| chr3.50274405.50274890 | chr3.50274405.50274863 | 459 | 487 | FALSE | smaller | 28 |
| chr3.50502669.50504392 | chr3.50502669.50504368 | 1700 | 1725 | FALSE | smaller | 25 |
| chr3.50590770.50591794 | chr3.50590770.50591657 | 888 | 1026 | FALSE | smaller | 138 |
| chr3.50616035.50616439 | chr3.50616049.50616439 | 391 | 406 | FALSE | smaller | 15 |
| chr3.51340305.51341482 | chr3.51340305.51341320 | 1016 | 1179 | FALSE | smaller | 163 |
| chr3.51390744.51392582 | chr3.51390834.51392582 | 1749 | 1840 | FALSE | smaller | 91 |
| chr3.51502109.51502320 | chr3.51502124.51502320 | 197 | 213 | FALSE | smaller | 16 |
| chr3.51559508.51560467 | chr3.51559508.51560286 | 779 | 961 | FALSE | smaller | 182 |
| chr3.51607999.51608862 | chr3.51607999.51608625 | 627 | 865 | FALSE | smaller | 238 |
| chr3.51669161.51669633 | chr3.51669161.51669575 | 415 | 474 | FALSE | smaller | 59 |
| chr3.51778064.51778336 | chr3.51778065.51778336 | 272 | 274 | FALSE | smaller | 2 |
| chr3.52032918.52033349 | chr3.52032919.52033349 | 431 | 433 | FALSE | smaller | 2 |
| chr3.52245679.52246596 | chr3.52245679.52246563 | 885 | 919 | FALSE | smaller | 34 |
| chr3.52253275.52254117 | chr3.52253311.52254117 | 807 | 844 | FALSE | smaller | 37 |
| chr3.52287022.52288301 | chr3.52287033.52288301 | 1269 | 1281 | FALSE | smaller | 12 |
| chr3.52299898.52301072 | chr3.52299898.52301020 | 1123 | 1176 | FALSE | smaller | 53 |
| chr3.52444952.52445468 | chr3.52444986.52445468 | 483 | 518 | FALSE | smaller | 35 |
| chr3.52449732.52450646 | chr3.52449734.52450519 | 786 | 916 | FALSE | smaller | 130 |
| chr3.52456248.52456796 | chr3.52456248.52456795 | 548 | 550 | FALSE | smaller | 2 |
| chr3.52519557.52519854 | chr3.52519557.52519797 | 241 | 299 | FALSE | smaller | 58 |
| chr3.52532909.52533610 | chr3.52532909.52533578 | 670 | 703 | FALSE | smaller | 33 |
| chr3.52536280.52537421 | chr3.52536298.52537421 | 1124 | 1143 | FALSE | smaller | 19 |
| chr3.52643077.52643693 | chr3.52643077.52643577 | 501 | 618 | FALSE | smaller | 117 |
| chr3.52714379.52715421 | chr3.52714389.52715421 | 1033 | 1044 | FALSE | smaller | 11 |
| chr3.52801837.52802525 | chr3.52801837.52802408 | 572 | 690 | FALSE | smaller | 118 |
| chr3.52998979.52999737 | chr3.52998979.52999564 | 586 | 760 | FALSE | smaller | 174 |
| chr3.53113814.53114567 | chr3.53113814.53114396 | 583 | 755 | FALSE | smaller | 172 |
| chr3.53162397.53163379 | chr3.53162397.53163348 | 952 | 984 | FALSE | smaller | 32 |
| chr3.53235959.53236688 | chr3.53235959.53236574 | 616 | 731 | FALSE | smaller | 115 |
| chr3.53242091.53242665 | chr3.53242203.53242665 | 463 | 576 | FALSE | smaller | 113 |
| chr3.53328008.53328320 | chr3.53328034.53328293 | 260 | 314 | FALSE | smaller | 54 |
| chr3.53501122.53501597 | chr3.53501122.53501463 | 342 | 477 | FALSE | smaller | 135 |
| chr3.53545801.53546362 | chr3.53545801.53546344 | 544 | 563 | FALSE | smaller | 19 |
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| chr3.53688860.53689530 | chr3.53688908.53689530 | 623 | 672 | FALSE | smaller | 49 |
| chr3.53780263.53780498 | chr3.53780272.53780498 | 227 | 237 | FALSE | smaller | 10 |
| chr3.53880476.53881166 | chr3.53880476.53880933 | 458 | 692 | FALSE | smaller | 234 |
| chr3.54374364.54374730 | chr3.54374424.54374696 | 273 | 368 | FALSE | smaller | 95 |
| chr3.54547871.54548181 | chr3.54547940.54548181 | 242 | 312 | FALSE | smaller | 70 |
| chr3.54906533.54907327 | chr3.54906573.54907327 | 755 | 796 | FALSE | smaller | 41 |
| chr3.54993993.54994471 | chr3.54993993.54994407 | 415 | 480 | FALSE | smaller | 65 |
| chr3.55077773.55078082 | chr3.55077839.55078082 | 244 | 311 | FALSE | smaller | 67 |
| chr3.55189347.55189801 | chr3.55189347.55189771 | 425 | 456 | FALSE | smaller | 31 |
| chr3.55486190.55488254 | chr3.55486190.55488141 | 1952 | 2066 | FALSE | smaller | 114 |
| chr3.55969311.55969597 | chr3.55969325.55969597 | 273 | 288 | FALSE | smaller | 15 |
| chr3.56019254.56019792 | chr3.56019426.56019792 | 367 | 540 | FALSE | smaller | 173 |
| chr3.56361704.56362150 | chr3.56361832.56362150 | 319 | 448 | FALSE | smaller | 129 |
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| chr3.56839807.56840243 | chr3.56839817.56840243 | 427 | 438 | FALSE | smaller | 11 |
| chr3.57009467.57009697 | chr3.57009488.57009697 | 210 | 232 | FALSE | smaller | 22 |
| chr3.57036421.57037511 | chr3.57036421.57037483 | 1063 | 1092 | FALSE | smaller | 29 |
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| chr3.57143254.57145305 | chr3.57143727.57145305 | 1579 | 2053 | FALSE | smaller | 474 |
| chr3.57159251.57159743 | chr3.57159251.57159635 | 385 | 494 | FALSE | smaller | 109 |
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| chr3.57892414.57892881 | chr3.57892414.57892868 | 455 | 469 | FALSE | smaller | 14 |
| chr3.58014218.58014536 | chr3.58014271.58014535 | 265 | 320 | FALSE | smaller | 55 |
| chr3.58032246.58032658 | chr3.58032246.58032616 | 371 | 414 | FALSE | smaller | 43 |
| chr3.58129755.58130484 | chr3.58129755.58130429 | 675 | 731 | FALSE | smaller | 56 |
| chr3.58187657.58188108 | chr3.58187671.58188108 | 438 | 453 | FALSE | smaller | 15 |
| chr3.58206419.58206989 | chr3.58206463.58206989 | 527 | 572 | FALSE | smaller | 45 |
| chr3.58411155.58411497 | chr3.58411155.58411371 | 217 | 344 | FALSE | smaller | 127 |
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| chr3.58487129.58487460 | chr3.58487129.58487280 | 152 | 333 | FALSE | smaller | 181 |
| chr3.58491308.58492795 | chr3.58491405.58492795 | 1391 | 1489 | FALSE | smaller | 98 |
| chr3.58532737.58533170 | chr3.58532737.58533156 | 420 | 435 | FALSE | smaller | 15 |
| chr3.58536952.58537560 | chr3.58536954.58537560 | 607 | 610 | FALSE | smaller | 3 |
| chr3.58568685.58569032 | chr3.58568685.58568974 | 290 | 349 | FALSE | smaller | 59 |
| chr3.58621023.58621868 | chr3.58621033.58621868 | 836 | 847 | FALSE | smaller | 11 |
| chr3.59049586.59050534 | chr3.59049760.59050534 | 775 | 950 | FALSE | smaller | 175 |
| chr3.59300711.59301135 | chr3.59300727.59301135 | 409 | 426 | FALSE | smaller | 17 |
| chr3.59829510.59829772 | chr3.59829517.59829772 | 256 | 264 | FALSE | smaller | 8 |
| chr3.60013132.60013665 | chr3.60013132.60013624 | 493 | 535 | FALSE | smaller | 42 |
| chr3.60112814.60113327 | chr3.60112814.60113258 | 445 | 515 | FALSE | smaller | 70 |
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| chr3.60303225.60303572 | chr3.60303225.60303507 | 283 | 349 | FALSE | smaller | 66 |
| chr3.60636674.60637394 | chr3.60636674.60637372 | 699 | 722 | FALSE | smaller | 23 |
| chr3.60641342.60641905 | chr3.60641342.60641816 | 475 | 565 | FALSE | smaller | 90 |
| chr3.61141174.61141617 | chr3.61141174.61141589 | 416 | 445 | FALSE | smaller | 29 |
| chr3.61315982.61316271 | chr3.61315989.61316271 | 283 | 291 | FALSE | smaller | 8 |
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| chr3.61601910.61602214 | chr3.61601925.61602214 | 290 | 306 | FALSE | smaller | 16 |
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| chr3.63048576.63049290 | chr3.63048576.63049145 | 570 | 716 | FALSE | smaller | 146 |
| chr3.63134576.63134900 | chr3.63134586.63134900 | 315 | 326 | FALSE | smaller | 11 |
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| chr3.63603236.63603490 | chr3.63603238.63603479 | 242 | 256 | FALSE | smaller | 14 |
| chr3.63872146.63872692 | chr3.63872214.63872692 | 479 | 548 | FALSE | smaller | 69 |
| chr3.63954971.63955507 | chr3.63954971.63955497 | 527 | 538 | FALSE | smaller | 11 |
| chr3.63960150.63960702 | chr3.63960160.63960702 | 543 | 554 | FALSE | smaller | 11 |
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| chr3.64209681.64210136 | chr3.64209681.64210128 | 448 | 457 | FALSE | smaller | 9 |
| chr3.64351976.64352826 | chr3.64352043.64352826 | 784 | 852 | FALSE | smaller | 68 |
| chr3.64355506.64355739 | chr3.64355584.64355739 | 156 | 235 | FALSE | smaller | 79 |
| chr3.64489192.64490398 | chr3.64489192.64490271 | 1080 | 1208 | FALSE | smaller | 128 |
| chr3.64687525.64688504 | chr3.64687561.64688488 | 928 | 981 | FALSE | smaller | 53 |
| chr3.64694052.64694527 | chr3.64694103.64694527 | 425 | 477 | FALSE | smaller | 52 |
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| chr3.65190586.65191305 | chr3.65190619.65191305 | 687 | 721 | FALSE | smaller | 34 |
| chr3.65194282.65194645 | chr3.65194312.65194645 | 334 | 365 | FALSE | smaller | 31 |
| chr3.65342127.65342429 | chr3.65342189.65342356 | 168 | 304 | FALSE | smaller | 136 |
| chr3.65356411.65357138 | chr3.65356411.65356802 | 392 | 729 | FALSE | smaller | 337 |
| chr3.65508824.65508986 | chr3.65508824.65508982 | 159 | 164 | FALSE | smaller | 5 |
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| chr3.65746740.65747340 | chr3.65746740.65747317 | 578 | 602 | FALSE | smaller | 24 |
| chr3.65784162.65785619 | chr3.65784162.65784472 | 311 | 1459 | FALSE | smaller | 1148 |
| chr3.65784162.65785619 | chr3.65784607.65785619 | 1013 | 1459 | FALSE | smaller | 446 |
| chr3.65835987.65836475 | chr3.65835987.65836358 | 372 | 490 | FALSE | smaller | 118 |
| chr3.65837262.65837610 | chr3.65837283.65837610 | 328 | 350 | FALSE | smaller | 22 |
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| chr3.65996634.65997053 | chr3.65996634.65996957 | 324 | 421 | FALSE | smaller | 97 |
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| chr3.66454002.66454380 | chr3.66454002.66454373 | 372 | 380 | FALSE | smaller | 8 |
| chr3.66499968.66501484 | chr3.66500025.66501484 | 1460 | 1518 | FALSE | smaller | 58 |
| chr3.66748566.66749005 | chr3.66748614.66749005 | 392 | 441 | FALSE | smaller | 49 |
| chr3.66801059.66801383 | chr3.66801059.66801363 | 305 | 326 | FALSE | smaller | 21 |
| chr3.66841960.66842197 | chr3.66841960.66842172 | 213 | 239 | FALSE | smaller | 26 |
| chr3.66997887.66999071 | chr3.66997969.66999071 | 1103 | 1186 | FALSE | smaller | 83 |
| chr3.67011621.67012138 | chr3.67011626.67012138 | 513 | 519 | FALSE | smaller | 6 |
| chr3.67357791.67358317 | chr3.67357791.67358259 | 469 | 528 | FALSE | smaller | 59 |
| chr3.67930258.67930793 | chr3.67930330.67930793 | 464 | 537 | FALSE | smaller | 73 |
| chr3.67947991.67948640 | chr3.67947991.67948588 | 598 | 651 | FALSE | smaller | 53 |
| chr3.69079651.69080053 | chr3.69079651.69080035 | 385 | 404 | FALSE | smaller | 19 |
| chr3.69080273.69080506 | chr3.69080281.69080506 | 226 | 235 | FALSE | smaller | 9 |
| chr3.69084329.69085790 | chr3.69084329.69085760 | 1432 | 1463 | FALSE | smaller | 31 |
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| chr3.69197515.69197905 | chr3.69197515.69197887 | 373 | 392 | FALSE | smaller | 19 |
| chr3.69219371.69220302 | chr3.69219371.69220203 | 833 | 933 | FALSE | smaller | 100 |
| chr3.69391614.69391892 | chr3.69391614.69391881 | 268 | 280 | FALSE | smaller | 12 |
| chr3.69998769.69999681 | chr3.69998769.69999676 | 908 | 914 | FALSE | smaller | 6 |
| chr3.70558922.70559230 | chr3.70558922.70559219 | 298 | 310 | FALSE | smaller | 12 |
| chr3.71022639.71022960 | chr3.71022647.71022960 | 314 | 323 | FALSE | smaller | 9 |
| chr3.71050012.71051462 | chr3.71050251.71051462 | 1212 | 1452 | FALSE | smaller | 240 |
| chr3.71174383.71174985 | chr3.71174483.71174985 | 503 | 604 | FALSE | smaller | 101 |
| chr3.71246009.71246301 | chr3.71246009.71246295 | 287 | 294 | FALSE | smaller | 7 |
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| chr5.28808299.28809192 | chr5.28808299.28809167 | 869 | 895 | FALSE | smaller | 26 |
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| chr5.29796059.29796346 | chr5.29796061.29796346 | 286 | 289 | FALSE | smaller | 3 |
| chr5.29942186.29942594 | chr5.29942186.29942568 | 383 | 410 | FALSE | smaller | 27 |
| chr5.30428736.30429349 | chr5.30428736.30429297 | 562 | 615 | FALSE | smaller | 53 |
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| chr5.31638481.31639836 | chr5.31638516.31639836 | 1321 | 1357 | FALSE | smaller | 36 |
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| chr5.31733459.31733831 | chr5.31733459.31733816 | 358 | 374 | FALSE | smaller | 16 |
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| chr5.32283726.32284146 | chr5.32283788.32284146 | 359 | 422 | FALSE | smaller | 63 |
| chr5.33227553.33228217 | chr5.33227553.33228203 | 651 | 666 | FALSE | smaller | 15 |
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| chr5.33685915.33686478 | chr5.33685915.33686411 | 497 | 565 | FALSE | smaller | 68 |
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| chr5.34826357.34826991 | chr5.34826357.34826867 | 511 | 636 | FALSE | smaller | 125 |
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| chr5.36140060.36140459 | chr5.36140106.36140459 | 354 | 401 | FALSE | smaller | 47 |
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| chr5.37232937.37233446 | chr5.37232937.37233435 | 499 | 511 | FALSE | smaller | 12 |
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| chr5.41509236.41510936 | chr5.41509786.41510936 | 1151 | 1702 | FALSE | smaller | 551 |
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| chr5.42691413.42691935 | chr5.42691413.42691911 | 499 | 524 | FALSE | smaller | 25 |
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| chr5.73447533.73448387 | chr5.73448150.73448387 | 238 | 856 | FALSE | smaller | 618 |
| chr5.73596837.73597144 | chr5.73596837.73597128 | 292 | 309 | FALSE | smaller | 17 |
| chr5.73630759.73631119 | chr5.73630795.73631119 | 325 | 362 | FALSE | smaller | 37 |
| chr5.73796310.73797081 | chr5.73796361.73797081 | 721 | 773 | FALSE | smaller | 52 |
| chr5.74367016.74367470 | chr5.74367016.74367457 | 442 | 456 | FALSE | smaller | 14 |
| chr5.74493332.74493867 | chr5.74493332.74493865 | 534 | 537 | FALSE | smaller | 3 |
| chr5.74865491.74867135 | chr5.74865491.74867070 | 1580 | 1646 | FALSE | smaller | 66 |
| chr5.74916845.74917213 | chr5.74916845.74917189 | 345 | 370 | FALSE | smaller | 25 |
| chr5.74947785.74948616 | chr5.74947785.74948512 | 728 | 833 | FALSE | smaller | 105 |
| chr5.74949155.74949421 | chr5.74949156.74949421 | 266 | 268 | FALSE | smaller | 2 |
| chr5.74954113.74954764 | chr5.74954239.74954764 | 526 | 653 | FALSE | smaller | 127 |
| chr5.75046283.75047046 | chr5.75046307.75047046 | 740 | 765 | FALSE | smaller | 25 |
| chr5.75049932.75050165 | chr5.75049945.75050165 | 221 | 235 | FALSE | smaller | 14 |
| chr5.75052426.75054381 | chr5.75052499.75054381 | 1883 | 1957 | FALSE | smaller | 74 |
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| chr5.75236399.75237344 | chr5.75236399.75237314 | 916 | 947 | FALSE | smaller | 31 |
| chr5.75510565.75512194 | chr5.75510565.75512175 | 1611 | 1631 | FALSE | smaller | 20 |
| chr5.75536787.75537191 | chr5.75536787.75537163 | 377 | 406 | FALSE | smaller | 29 |
| chr5.75708017.75708350 | chr5.75708059.75708344 | 286 | 335 | FALSE | smaller | 49 |
| chr5.75735069.75735627 | chr5.75735071.75735627 | 557 | 560 | FALSE | smaller | 3 |
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| chr5.76787279.76787797 | chr5.76787291.76787797 | 507 | 520 | FALSE | smaller | 13 |
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| chr5.77030030.77031082 | chr5.77030030.77031074 | 1045 | 1054 | FALSE | smaller | 9 |
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| chr5.78806511.78807006 | chr5.78806511.78806919 | 409 | 497 | FALSE | smaller | 88 |
| chr5.78908151.78908766 | chr5.78908186.78908766 | 581 | 617 | FALSE | smaller | 36 |
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| chr5.85587850.85588230 | chr5.85587921.85588230 | 310 | 382 | FALSE | smaller | 72 |
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| chr5.90093929.90094386 | chr5.90093929.90094378 | 450 | 459 | FALSE | smaller | 9 |
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| chr5.176005992.176006324 | chr5.176005992.176006307 | 316 | 334 | FALSE | smaller | 18 |
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| chr5.176508505.176509091 | chr5.176508692.176509091 | 400 | 588 | FALSE | smaller | 188 |
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| chr5.176542593.176544250 | chr5.176542623.176544250 | 1628 | 1659 | FALSE | smaller | 31 |
| chr5.176578255.176578646 | chr5.176578373.176578646 | 274 | 393 | FALSE | smaller | 119 |
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| chr5.178535243.178535818 | chr5.178535243.178535772 | 530 | 577 | FALSE | smaller | 47 |
| chr5.178793285.178793457 | chr5.178793285.178793445 | 161 | 174 | FALSE | smaller | 13 |
| chr5.178797213.178797665 | chr5.178797213.178797559 | 347 | 454 | FALSE | smaller | 107 |
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| chr5.178940453.178941529 | chr5.178940453.178941501 | 1049 | 1078 | FALSE | smaller | 29 |
| chr5.178994609.178995760 | chr5.178994618.178995760 | 1143 | 1153 | FALSE | smaller | 10 |
| chr5.179354491.179354945 | chr5.179354675.179354945 | 271 | 456 | FALSE | smaller | 185 |
| chr5.179494737.179495136 | chr5.179494775.179495136 | 362 | 401 | FALSE | smaller | 39 |
| chr5.179553942.179554419 | chr5.179553982.179554419 | 438 | 479 | FALSE | smaller | 41 |
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| chr5.179832193.179833328 | chr5.179832193.179833003 | 811 | 1137 | FALSE | smaller | 326 |
| chr5.180084924.180085547 | chr5.180085025.180085547 | 523 | 625 | FALSE | smaller | 102 |
| chr5.180171424.180171822 | chr5.180171457.180171822 | 366 | 400 | FALSE | smaller | 34 |
| chr5.180208588.180209386 | chr5.180208588.180209003 | 416 | 800 | FALSE | smaller | 384 |
| chr5.180208588.180209386 | chr5.180209019.180209386 | 368 | 800 | FALSE | smaller | 432 |
| chr5.180304655.180305301 | chr5.180304655.180305296 | 642 | 648 | FALSE | smaller | 6 |
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| chr5.180333674.180334401 | chr5.180333674.180334003 | 330 | 729 | FALSE | smaller | 399 |
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| chr5.180816816.180817485 | chr5.180816816.180817470 | 655 | 671 | FALSE | smaller | 16 |
| chr5.181099926.181100822 | chr5.181099926.181100776 | 851 | 898 | FALSE | smaller | 47 |
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| chr5.181197433.181197901 | chr5.181197433.181197850 | 418 | 470 | FALSE | smaller | 52 |
| chr5.181234498.181235139 | chr5.181234498.181235134 | 637 | 643 | FALSE | smaller | 6 |
| chr5.181260566.181262717 | chr5.181260614.181262717 | 2104 | 2153 | FALSE | smaller | 49 |
| chr5.181281019.181282342 | chr5.181281029.181282342 | 1314 | 1325 | FALSE | smaller | 11 |
| chr6.186484.187113 | chr6.186489.187113 | 625 | 631 | FALSE | smaller | 6 |
| chr6.324202.325426 | chr6.324255.325426 | 1172 | 1226 | FALSE | smaller | 54 |
| chr6.436098.437124 | chr6.436098.437104 | 1007 | 1028 | FALSE | smaller | 21 |
| chr6.551383.551909 | chr6.551425.551909 | 485 | 528 | FALSE | smaller | 43 |
| chr6.572806.573445 | chr6.572869.573399 | 531 | 641 | FALSE | smaller | 110 |
| chr6.624990.625395 | chr6.624990.625381 | 392 | 407 | FALSE | smaller | 15 |
| chr6.626271.626646 | chr6.626271.626551 | 281 | 377 | FALSE | smaller | 96 |
| chr6.683825.684353 | chr6.683825.684348 | 524 | 530 | FALSE | smaller | 6 |
| chr6.692360.693771 | chr6.692360.693676 | 1317 | 1413 | FALSE | smaller | 96 |
| chr6.706919.707257 | chr6.706919.707252 | 334 | 340 | FALSE | smaller | 6 |
| chr6.1010813.1011209 | chr6.1010928.1011209 | 282 | 398 | FALSE | smaller | 116 |
| chr6.1068757.1069321 | chr6.1068757.1069312 | 556 | 566 | FALSE | smaller | 10 |
| chr6.1390024.1391285 | chr6.1390024.1390817 | 794 | 1263 | FALSE | smaller | 469 |
| chr6.1390024.1391285 | chr6.1390872.1391285 | 414 | 1263 | FALSE | smaller | 849 |
| chr6.1609273.1611097 | chr6.1609275.1611097 | 1823 | 1826 | FALSE | smaller | 3 |
| chr6.1952658.1953104 | chr6.1952714.1953104 | 391 | 448 | FALSE | smaller | 57 |
| chr6.2158691.2158988 | chr6.2158691.2158887 | 197 | 299 | FALSE | smaller | 102 |
| chr6.2388904.2389423 | chr6.2388969.2389423 | 455 | 521 | FALSE | smaller | 66 |
| chr6.2397030.2397567 | chr6.2397030.2397559 | 530 | 539 | FALSE | smaller | 9 |
| chr6.2611187.2611573 | chr6.2611187.2611471 | 285 | 388 | FALSE | smaller | 103 |
| chr6.2659768.2660514 | chr6.2659803.2660514 | 712 | 748 | FALSE | smaller | 36 |
| chr6.2790616.2791503 | chr6.2790625.2791503 | 879 | 889 | FALSE | smaller | 10 |
| chr6.2793788.2794040 | chr6.2793788.2794031 | 244 | 254 | FALSE | smaller | 10 |
| chr6.2876237.2876900 | chr6.2876244.2876900 | 657 | 665 | FALSE | smaller | 8 |
| chr6.2908113.2909173 | chr6.2908231.2909173 | 943 | 1062 | FALSE | smaller | 119 |
| chr6.2911815.2912126 | chr6.2911815.2911943 | 129 | 313 | FALSE | smaller | 184 |
| chr6.2960135.2960793 | chr6.2960206.2960793 | 588 | 660 | FALSE | smaller | 72 |
| chr6.3013444.3013948 | chr6.3013444.3013935 | 492 | 506 | FALSE | smaller | 14 |
| chr6.3109093.3109459 | chr6.3109093.3109333 | 241 | 368 | FALSE | smaller | 127 |
| chr6.3158241.3158532 | chr6.3158241.3158502 | 262 | 293 | FALSE | smaller | 31 |
| chr6.3324237.3324767 | chr6.3324237.3324761 | 525 | 532 | FALSE | smaller | 7 |
| chr6.3345744.3346379 | chr6.3345744.3346332 | 589 | 637 | FALSE | smaller | 48 |
| chr6.3363237.3363649 | chr6.3363237.3363623 | 387 | 414 | FALSE | smaller | 27 |
| chr6.3487721.3488353 | chr6.3487721.3488273 | 553 | 634 | FALSE | smaller | 81 |
| chr6.3510552.3511088 | chr6.3510552.3511011 | 460 | 538 | FALSE | smaller | 78 |
| chr6.3511334.3511851 | chr6.3511334.3511841 | 508 | 519 | FALSE | smaller | 11 |
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| chr6.3748316.3748689 | chr6.3748316.3748649 | 334 | 375 | FALSE | smaller | 41 |
| chr6.3801088.3801631 | chr6.3801088.3801603 | 516 | 545 | FALSE | smaller | 29 |
| chr6.3808764.3809365 | chr6.3808773.3809365 | 593 | 603 | FALSE | smaller | 10 |
| chr6.4274493.4275649 | chr6.4274493.4275252 | 760 | 1158 | FALSE | smaller | 398 |
| chr6.4274493.4275649 | chr6.4275276.4275649 | 374 | 1158 | FALSE | smaller | 784 |
| chr6.4295414.4295917 | chr6.4295453.4295917 | 465 | 505 | FALSE | smaller | 40 |
| chr6.4297137.4297800 | chr6.4297137.4297791 | 655 | 665 | FALSE | smaller | 10 |
| chr6.4371246.4372001 | chr6.4371250.4372001 | 752 | 757 | FALSE | smaller | 5 |
| chr6.4567138.4567463 | chr6.4567155.4567463 | 309 | 327 | FALSE | smaller | 18 |
| chr6.4580390.4580695 | chr6.4580390.4580530 | 141 | 307 | FALSE | smaller | 166 |
| chr6.4589297.4589892 | chr6.4589297.4589737 | 441 | 597 | FALSE | smaller | 156 |
| chr6.4931536.4932179 | chr6.4931550.4932179 | 630 | 645 | FALSE | smaller | 15 |
| chr6.4964792.4965238 | chr6.4964792.4965192 | 401 | 448 | FALSE | smaller | 47 |
| chr6.5030684.5030972 | chr6.5030684.5030970 | 287 | 290 | FALSE | smaller | 3 |
| chr6.5036331.5036924 | chr6.5036331.5036829 | 499 | 595 | FALSE | smaller | 96 |
| chr6.5084384.5086535 | chr6.5084384.5086432 | 2049 | 2153 | FALSE | smaller | 104 |
| chr6.5108094.5108660 | chr6.5108104.5108660 | 557 | 568 | FALSE | smaller | 11 |
| chr6.5213733.5215483 | chr6.5213814.5215483 | 1670 | 1752 | FALSE | smaller | 82 |
| chr6.5469085.5469626 | chr6.5469099.5469626 | 528 | 543 | FALSE | smaller | 15 |
| chr6.5672160.5672978 | chr6.5672266.5672978 | 713 | 820 | FALSE | smaller | 107 |
| chr6.6008555.6009630 | chr6.6008596.6009630 | 1035 | 1077 | FALSE | smaller | 42 |
| chr6.6218685.6219264 | chr6.6218685.6219112 | 428 | 581 | FALSE | smaller | 153 |
| chr6.6494729.6495080 | chr6.6494729.6495079 | 351 | 353 | FALSE | smaller | 2 |
| chr6.6546199.6547715 | chr6.6546199.6547682 | 1484 | 1518 | FALSE | smaller | 34 |
| chr6.6676736.6677202 | chr6.6676736.6677200 | 465 | 468 | FALSE | smaller | 3 |
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| chr6.6890347.6890858 | chr6.6890347.6890853 | 507 | 513 | FALSE | smaller | 6 |
| chr6.6892977.6893609 | chr6.6892977.6893607 | 631 | 634 | FALSE | smaller | 3 |
| chr6.7052513.7052769 | chr6.7052532.7052769 | 238 | 258 | FALSE | smaller | 20 |
| chr6.7172752.7173196 | chr6.7172752.7173128 | 377 | 446 | FALSE | smaller | 69 |
| chr6.7216864.7217545 | chr6.7216864.7217541 | 678 | 683 | FALSE | smaller | 5 |
| chr6.7246342.7246751 | chr6.7246342.7246745 | 404 | 411 | FALSE | smaller | 7 |
| chr6.7275514.7276327 | chr6.7275563.7276327 | 765 | 815 | FALSE | smaller | 50 |
| chr6.7339088.7339362 | chr6.7339088.7339360 | 273 | 276 | FALSE | smaller | 3 |
| chr6.7414652.7415228 | chr6.7414652.7415224 | 573 | 578 | FALSE | smaller | 5 |
| chr6.7568032.7568407 | chr6.7568032.7568354 | 323 | 377 | FALSE | smaller | 54 |
| chr6.7615160.7615889 | chr6.7615285.7615889 | 605 | 731 | FALSE | smaller | 126 |
| chr6.7640759.7641535 | chr6.7640802.7641535 | 734 | 778 | FALSE | smaller | 44 |
| chr6.7672830.7673859 | chr6.7672870.7673703 | 834 | 1031 | FALSE | smaller | 197 |
| chr6.7699371.7699892 | chr6.7699371.7699875 | 505 | 523 | FALSE | smaller | 18 |
| chr6.7727149.7728817 | chr6.7727431.7728817 | 1387 | 1670 | FALSE | smaller | 283 |
| chr6.7805553.7806372 | chr6.7805553.7806246 | 694 | 821 | FALSE | smaller | 127 |
| chr6.8406009.8406328 | chr6.8406014.8406328 | 315 | 321 | FALSE | smaller | 6 |
| chr6.8562117.8562455 | chr6.8562192.8562439 | 248 | 340 | FALSE | smaller | 92 |
| chr6.8579482.8579938 | chr6.8579482.8579902 | 421 | 458 | FALSE | smaller | 37 |
| chr6.9481631.9482603 | chr6.9481631.9482599 | 969 | 974 | FALSE | smaller | 5 |
| chr6.9851905.9852322 | chr6.9851905.9852270 | 366 | 419 | FALSE | smaller | 53 |
| chr6.10383298.10383864 | chr6.10383337.10383864 | 528 | 568 | FALSE | smaller | 40 |
| chr6.10412592.10413153 | chr6.10412594.10413153 | 560 | 563 | FALSE | smaller | 3 |
| chr6.10487683.10488554 | chr6.10487688.10488550 | 863 | 873 | FALSE | smaller | 10 |
| chr6.10587620.10589575 | chr6.10587620.10589574 | 1955 | 1957 | FALSE | smaller | 2 |
| chr6.10621213.10621825 | chr6.10621213.10621806 | 594 | 614 | FALSE | smaller | 20 |
| chr6.11224340.11225004 | chr6.11224423.11225004 | 582 | 666 | FALSE | smaller | 84 |
| chr6.11270111.11270879 | chr6.11270111.11270785 | 675 | 770 | FALSE | smaller | 95 |
| chr6.11316186.11316469 | chr6.11316186.11316425 | 240 | 285 | FALSE | smaller | 45 |
| chr6.11427103.11427809 | chr6.11427262.11427809 | 548 | 708 | FALSE | smaller | 160 |
| chr6.11434801.11435180 | chr6.11434802.11435180 | 379 | 381 | FALSE | smaller | 2 |
| chr6.11494713.11495124 | chr6.11494788.11495124 | 337 | 413 | FALSE | smaller | 76 |
| chr6.11687453.11688197 | chr6.11687453.11688187 | 735 | 746 | FALSE | smaller | 11 |
| chr6.11702275.11702777 | chr6.11702307.11702732 | 426 | 504 | FALSE | smaller | 78 |
| chr6.11847705.11848377 | chr6.11847705.11848341 | 637 | 674 | FALSE | smaller | 37 |
| chr6.11920931.11921375 | chr6.11920931.11921369 | 439 | 446 | FALSE | smaller | 7 |
| chr6.12010758.12011829 | chr6.12010764.12011829 | 1066 | 1073 | FALSE | smaller | 7 |
| chr6.12012723.12013513 | chr6.12012723.12013508 | 786 | 792 | FALSE | smaller | 6 |
| chr6.12031649.12031988 | chr6.12031738.12031988 | 251 | 341 | FALSE | smaller | 90 |
| chr6.12039606.12040104 | chr6.12039612.12040104 | 493 | 500 | FALSE | smaller | 7 |
| chr6.12065349.12066254 | chr6.12065407.12066254 | 848 | 907 | FALSE | smaller | 59 |
| chr6.12107534.12108503 | chr6.12107573.12108465 | 893 | 971 | FALSE | smaller | 78 |
| chr6.12275045.12275449 | chr6.12275098.12275449 | 352 | 406 | FALSE | smaller | 54 |
| chr6.12301013.12301409 | chr6.12301013.12301364 | 352 | 398 | FALSE | smaller | 46 |
| chr6.12577031.12577776 | chr6.12577077.12577732 | 656 | 747 | FALSE | smaller | 91 |
| chr6.12585108.12586143 | chr6.12585108.12586020 | 913 | 1037 | FALSE | smaller | 124 |
| chr6.12749909.12750356 | chr6.12749909.12750323 | 415 | 449 | FALSE | smaller | 34 |
| chr6.13013355.13015207 | chr6.13013359.13015207 | 1849 | 1854 | FALSE | smaller | 5 |
| chr6.13044256.13044720 | chr6.13044256.13044680 | 425 | 466 | FALSE | smaller | 41 |
| chr6.13079144.13079814 | chr6.13079180.13079814 | 635 | 672 | FALSE | smaller | 37 |
| chr6.13303596.13304390 | chr6.13303596.13304359 | 764 | 796 | FALSE | smaller | 32 |
| chr6.13334745.13335324 | chr6.13334745.13335317 | 573 | 581 | FALSE | smaller | 8 |
| chr6.13351170.13351650 | chr6.13351170.13351616 | 447 | 482 | FALSE | smaller | 35 |
| chr6.13385012.13385484 | chr6.13385017.13385484 | 468 | 474 | FALSE | smaller | 6 |
| chr6.13400680.13401134 | chr6.13400743.13401134 | 392 | 456 | FALSE | smaller | 64 |
| chr6.13412498.13412774 | chr6.13412498.13412742 | 245 | 278 | FALSE | smaller | 33 |
| chr6.13422708.13423089 | chr6.13422723.13423089 | 367 | 383 | FALSE | smaller | 16 |
| chr6.13437702.13437900 | chr6.13437702.13437893 | 192 | 200 | FALSE | smaller | 8 |
| chr6.13468911.13469198 | chr6.13468916.13469198 | 283 | 289 | FALSE | smaller | 6 |
| chr6.13480086.13481630 | chr6.13480086.13481427 | 1342 | 1546 | FALSE | smaller | 204 |
| chr6.13493754.13494587 | chr6.13494278.13494587 | 310 | 835 | FALSE | smaller | 525 |
| chr6.13693429.13694016 | chr6.13693458.13694016 | 559 | 589 | FALSE | smaller | 30 |
| chr6.13859883.13860880 | chr6.13859883.13860297 | 415 | 999 | FALSE | smaller | 584 |
| chr6.13859883.13860880 | chr6.13860328.13860880 | 553 | 999 | FALSE | smaller | 446 |
| chr6.14182958.14183171 | chr6.14182965.14183171 | 207 | 215 | FALSE | smaller | 8 |
| chr6.14270836.14271363 | chr6.14270967.14271363 | 397 | 529 | FALSE | smaller | 132 |
| chr6.14547641.14548023 | chr6.14547653.14548023 | 371 | 384 | FALSE | smaller | 13 |
| chr6.14732601.14733489 | chr6.14732601.14733315 | 715 | 890 | FALSE | smaller | 175 |
| chr6.14855259.14855643 | chr6.14855260.14855643 | 384 | 386 | FALSE | smaller | 2 |
| chr6.14932376.14933488 | chr6.14932452.14933488 | 1037 | 1114 | FALSE | smaller | 77 |
| chr6.15245275.15246079 | chr6.15245275.15246078 | 804 | 806 | FALSE | smaller | 2 |
| chr6.15247905.15248338 | chr6.15247915.15248338 | 424 | 435 | FALSE | smaller | 11 |
| chr6.15264975.15265907 | chr6.15264975.15265805 | 831 | 934 | FALSE | smaller | 103 |
| chr6.15293780.15294234 | chr6.15293780.15294089 | 310 | 456 | FALSE | smaller | 146 |
| chr6.15323033.15323424 | chr6.15323239.15323424 | 186 | 393 | FALSE | smaller | 207 |
| chr6.15358905.15359820 | chr6.15358910.15359820 | 911 | 917 | FALSE | smaller | 6 |
| chr6.15400017.15400363 | chr6.15400030.15400363 | 334 | 348 | FALSE | smaller | 14 |
| chr6.15400563.15400975 | chr6.15400563.15400973 | 411 | 414 | FALSE | smaller | 3 |
| chr6.15409325.15409570 | chr6.15409325.15409545 | 221 | 247 | FALSE | smaller | 26 |
| chr6.15512113.15512431 | chr6.15512120.15512431 | 312 | 320 | FALSE | smaller | 8 |
| chr6.15754171.15754573 | chr6.15754296.15754573 | 278 | 404 | FALSE | smaller | 126 |
| chr6.15793090.15793639 | chr6.15793090.15793615 | 526 | 551 | FALSE | smaller | 25 |
| chr6.15962247.15962782 | chr6.15962412.15962782 | 371 | 537 | FALSE | smaller | 166 |
| chr6.15977011.15977448 | chr6.15977023.15977448 | 426 | 439 | FALSE | smaller | 13 |
| chr6.15997685.15998307 | chr6.15997685.15998258 | 574 | 624 | FALSE | smaller | 50 |
| chr6.16215350.16215920 | chr6.16215350.16215919 | 570 | 572 | FALSE | smaller | 2 |
| chr6.16244479.16245283 | chr6.16244598.16245283 | 686 | 806 | FALSE | smaller | 120 |
| chr6.16257941.16258616 | chr6.16257972.16258616 | 645 | 677 | FALSE | smaller | 32 |
| chr6.16319874.16320631 | chr6.16319874.16320627 | 754 | 759 | FALSE | smaller | 5 |
| chr6.16326933.16328587 | chr6.16326946.16328587 | 1642 | 1656 | FALSE | smaller | 14 |
| chr6.16333641.16334072 | chr6.16333641.16334050 | 410 | 433 | FALSE | smaller | 23 |
| chr6.16416693.16416908 | chr6.16416695.16416907 | 213 | 217 | FALSE | smaller | 4 |
| chr6.16477795.16478082 | chr6.16477810.16478082 | 273 | 289 | FALSE | smaller | 16 |
| chr6.16494802.16495172 | chr6.16494858.16495172 | 315 | 372 | FALSE | smaller | 57 |
| chr6.16497143.16497851 | chr6.16497143.16497849 | 707 | 710 | FALSE | smaller | 3 |
| chr6.16564016.16564757 | chr6.16564139.16564757 | 619 | 743 | FALSE | smaller | 124 |
| chr6.16646102.16646773 | chr6.16646102.16646715 | 614 | 673 | FALSE | smaller | 59 |
| chr6.16803276.16804024 | chr6.16803330.16804021 | 692 | 750 | FALSE | smaller | 58 |
| chr6.17045378.17046200 | chr6.17045378.17046197 | 820 | 824 | FALSE | smaller | 4 |
| chr6.17115688.17116376 | chr6.17115696.17116376 | 681 | 690 | FALSE | smaller | 9 |
| chr6.17278745.17279190 | chr6.17278771.17279190 | 420 | 447 | FALSE | smaller | 27 |
| chr6.17385361.17386063 | chr6.17385405.17386063 | 659 | 704 | FALSE | smaller | 45 |
| chr6.17692089.17692509 | chr6.17692089.17692497 | 409 | 422 | FALSE | smaller | 13 |
| chr6.17815063.17815795 | chr6.17815067.17815795 | 729 | 734 | FALSE | smaller | 5 |
| chr6.18154654.18156402 | chr6.18154654.18156286 | 1633 | 1750 | FALSE | smaller | 117 |
| chr6.18209595.18210025 | chr6.18209665.18210014 | 350 | 432 | FALSE | smaller | 82 |
| chr6.18280181.18280461 | chr6.18280181.18280460 | 280 | 282 | FALSE | smaller | 2 |
| chr6.18609737.18610090 | chr6.18609737.18610037 | 301 | 355 | FALSE | smaller | 54 |
| chr6.19013017.19013679 | chr6.19013119.19013679 | 561 | 664 | FALSE | smaller | 103 |
| chr6.19180671.19181104 | chr6.19180716.19181104 | 389 | 435 | FALSE | smaller | 46 |
| chr6.19205057.19205667 | chr6.19205057.19205566 | 510 | 612 | FALSE | smaller | 102 |
| chr6.19368537.19368995 | chr6.19368589.19368995 | 407 | 460 | FALSE | smaller | 53 |
| chr6.19369818.19370285 | chr6.19369823.19370285 | 463 | 469 | FALSE | smaller | 6 |
| chr6.19836783.19839260 | chr6.19836789.19839260 | 2472 | 2479 | FALSE | smaller | 7 |
| chr6.19935868.19936515 | chr6.19935879.19936464 | 586 | 649 | FALSE | smaller | 63 |
| chr6.20030060.20030557 | chr6.20030063.20030557 | 495 | 499 | FALSE | smaller | 4 |
| chr6.20238704.20239247 | chr6.20238704.20239089 | 386 | 545 | FALSE | smaller | 159 |
| chr6.20328319.20328822 | chr6.20328353.20328822 | 470 | 505 | FALSE | smaller | 35 |
| chr6.20401140.20402026 | chr6.20401140.20401939 | 800 | 888 | FALSE | smaller | 88 |
| chr6.20464098.20464702 | chr6.20464098.20464697 | 600 | 606 | FALSE | smaller | 6 |
| chr6.20528274.20528639 | chr6.20528286.20528639 | 354 | 367 | FALSE | smaller | 13 |
| chr6.21200193.21200819 | chr6.21200193.21200801 | 609 | 628 | FALSE | smaller | 19 |
| chr6.21357348.21357952 | chr6.21357389.21357952 | 564 | 606 | FALSE | smaller | 42 |
| chr6.21360249.21360870 | chr6.21360249.21360814 | 566 | 623 | FALSE | smaller | 57 |
| chr6.21593450.21596124 | chr6.21593450.21595624 | 2175 | 2676 | FALSE | smaller | 501 |
| chr6.21593450.21596124 | chr6.21595646.21596124 | 479 | 2676 | FALSE | smaller | 2197 |
| chr6.21753320.21754079 | chr6.21753479.21754079 | 601 | 761 | FALSE | smaller | 160 |
| chr6.21826033.21826495 | chr6.21826033.21826348 | 316 | 464 | FALSE | smaller | 148 |
| chr6.21846707.21848579 | chr6.21846707.21848505 | 1799 | 1874 | FALSE | smaller | 75 |
| chr6.21870303.21870629 | chr6.21870354.21870629 | 276 | 328 | FALSE | smaller | 52 |
| chr6.22108235.22108640 | chr6.22108315.22108640 | 326 | 407 | FALSE | smaller | 81 |
| chr6.22658859.22659264 | chr6.22658859.22659240 | 382 | 407 | FALSE | smaller | 25 |
| chr6.22877752.22878187 | chr6.22877752.22878171 | 420 | 437 | FALSE | smaller | 17 |
| chr6.22917180.22917649 | chr6.22917180.22917619 | 440 | 471 | FALSE | smaller | 31 |
| chr6.23228329.23228792 | chr6.23228382.23228789 | 408 | 465 | FALSE | smaller | 57 |
| chr6.23454071.23455028 | chr6.23454575.23455028 | 454 | 959 | FALSE | smaller | 505 |
| chr6.23981004.23981385 | chr6.23981004.23981363 | 360 | 383 | FALSE | smaller | 23 |
| chr6.24052552.24053035 | chr6.24052595.24052933 | 339 | 485 | FALSE | smaller | 146 |
| chr6.24389184.24389517 | chr6.24389184.24389510 | 327 | 335 | FALSE | smaller | 8 |
| chr6.24402784.24403747 | chr6.24402797.24403747 | 951 | 965 | FALSE | smaller | 14 |
| chr6.24854056.24854744 | chr6.24854349.24854744 | 396 | 690 | FALSE | smaller | 294 |
| chr6.25346081.25346318 | chr6.25346081.25346314 | 234 | 239 | FALSE | smaller | 5 |
| chr6.25717950.25718385 | chr6.25717955.25718385 | 431 | 437 | FALSE | smaller | 6 |
| chr6.25748684.25749191 | chr6.25748689.25749191 | 503 | 509 | FALSE | smaller | 6 |
| chr6.26043151.26044677 | chr6.26043219.26044677 | 1459 | 1528 | FALSE | smaller | 69 |
| chr6.26044929.26045734 | chr6.26044929.26045671 | 743 | 807 | FALSE | smaller | 64 |
| chr6.26165762.26166545 | chr6.26165762.26166457 | 696 | 785 | FALSE | smaller | 89 |
| chr6.26224901.26226228 | chr6.26224901.26226181 | 1281 | 1329 | FALSE | smaller | 48 |
| chr6.26272131.26273547 | chr6.26272170.26273547 | 1378 | 1418 | FALSE | smaller | 40 |
| chr6.26284576.26286563 | chr6.26284638.26286563 | 1926 | 1989 | FALSE | smaller | 63 |
| chr6.26388047.26388701 | chr6.26388110.26388701 | 592 | 656 | FALSE | smaller | 64 |
| chr6.26595623.26596200 | chr6.26595623.26596198 | 576 | 579 | FALSE | smaller | 3 |
| chr6.26659244.26660327 | chr6.26659258.26660327 | 1070 | 1085 | FALSE | smaller | 15 |
| chr6.27019527.27020530 | chr6.27019527.27020491 | 965 | 1005 | FALSE | smaller | 40 |
| chr6.27139125.27140143 | chr6.27139125.27140142 | 1018 | 1020 | FALSE | smaller | 2 |
| chr6.27237231.27237657 | chr6.27237236.27237657 | 422 | 428 | FALSE | smaller | 6 |
| chr6.27374431.27375603 | chr6.27374431.27375477 | 1047 | 1174 | FALSE | smaller | 127 |
| chr6.27472327.27473562 | chr6.27472337.27473562 | 1226 | 1237 | FALSE | smaller | 11 |
| chr6.27693611.27694839 | chr6.27693752.27694839 | 1088 | 1230 | FALSE | smaller | 142 |
| chr6.27791753.27792711 | chr6.27791816.27792711 | 896 | 960 | FALSE | smaller | 64 |
| chr6.27807188.27808731 | chr6.27807262.27808696 | 1435 | 1545 | FALSE | smaller | 110 |
| chr6.27809895.27810664 | chr6.27809898.27810664 | 767 | 771 | FALSE | smaller | 4 |
| chr6.27814587.27815508 | chr6.27814587.27815488 | 902 | 923 | FALSE | smaller | 21 |
| chr6.27830709.27831489 | chr6.27830709.27831383 | 675 | 782 | FALSE | smaller | 107 |
| chr6.28080033.28081613 | chr6.28080033.28081578 | 1546 | 1582 | FALSE | smaller | 36 |
| chr6.28161443.28162026 | chr6.28161443.28161941 | 499 | 585 | FALSE | smaller | 86 |
| chr6.28207217.28208125 | chr6.28207217.28207923 | 707 | 910 | FALSE | smaller | 203 |
| chr6.28281277.28282175 | chr6.28281325.28282175 | 851 | 900 | FALSE | smaller | 49 |
| chr6.33512063.33512904 | chr6.33512063.33512750 | 688 | 843 | FALSE | smaller | 155 |
| chr6.33552926.33553400 | chr6.33552926.33553395 | 470 | 476 | FALSE | smaller | 6 |
| chr6.33562250.33562839 | chr6.33562250.33562650 | 401 | 591 | FALSE | smaller | 190 |
| chr6.33632589.33633014 | chr6.33632616.33633014 | 399 | 427 | FALSE | smaller | 28 |
| chr6.33751117.33751488 | chr6.33751172.33751488 | 317 | 373 | FALSE | smaller | 56 |
| chr6.33830552.33831086 | chr6.33830552.33831040 | 489 | 536 | FALSE | smaller | 47 |
| chr6.33839367.33839667 | chr6.33839446.33839667 | 222 | 302 | FALSE | smaller | 80 |
| chr6.33864657.33865239 | chr6.33864657.33865208 | 552 | 584 | FALSE | smaller | 32 |
| chr6.33904599.33905228 | chr6.33904600.33905228 | 629 | 631 | FALSE | smaller | 2 |
| chr6.33984062.33984590 | chr6.33984089.33984527 | 439 | 530 | FALSE | smaller | 91 |
| chr6.34043548.34044985 | chr6.34043643.34044985 | 1343 | 1439 | FALSE | smaller | 96 |
| chr6.34073322.34073993 | chr6.34073322.34073946 | 625 | 673 | FALSE | smaller | 48 |
| chr6.34143966.34144604 | chr6.34143994.34144604 | 611 | 640 | FALSE | smaller | 29 |
| chr6.34324736.34325176 | chr6.34324736.34325153 | 418 | 442 | FALSE | smaller | 24 |
| chr6.34359579.34360201 | chr6.34359579.34360032 | 454 | 624 | FALSE | smaller | 170 |
| chr6.34465515.34465781 | chr6.34465606.34465781 | 176 | 268 | FALSE | smaller | 92 |
| chr6.34465930.34466381 | chr6.34465940.34466381 | 442 | 453 | FALSE | smaller | 11 |
| chr6.34851436.34851930 | chr6.34851436.34851853 | 418 | 496 | FALSE | smaller | 78 |
| chr6.34858753.34859194 | chr6.34858753.34859131 | 379 | 443 | FALSE | smaller | 64 |
| chr6.34901433.34902524 | chr6.34901492.34902524 | 1033 | 1093 | FALSE | smaller | 60 |
| chr6.35128510.35128798 | chr6.35128525.35128798 | 274 | 290 | FALSE | smaller | 16 |
| chr6.35168480.35169382 | chr6.35168499.35169382 | 884 | 904 | FALSE | smaller | 20 |
| chr6.35209966.35210184 | chr6.35209977.35210184 | 208 | 220 | FALSE | smaller | 12 |
| chr6.35281074.35281479 | chr6.35281074.35281375 | 302 | 407 | FALSE | smaller | 105 |
| chr6.35327361.35327744 | chr6.35327361.35327722 | 362 | 385 | FALSE | smaller | 23 |
| chr6.35382971.35383472 | chr6.35382971.35383421 | 451 | 503 | FALSE | smaller | 52 |
| chr6.35397003.35397355 | chr6.35397003.35397283 | 281 | 354 | FALSE | smaller | 73 |
| chr6.35449340.35449885 | chr6.35449340.35449884 | 545 | 547 | FALSE | smaller | 2 |
| chr6.35601228.35602903 | chr6.35601269.35602903 | 1635 | 1677 | FALSE | smaller | 42 |
| chr6.35691324.35691817 | chr6.35691324.35691774 | 451 | 495 | FALSE | smaller | 44 |
| chr6.35731418.35732560 | chr6.35731418.35732541 | 1124 | 1144 | FALSE | smaller | 20 |
| chr6.35736400.35736551 | chr6.35736400.35736522 | 123 | 153 | FALSE | smaller | 30 |
| chr6.35931021.35931708 | chr6.35931021.35931672 | 652 | 689 | FALSE | smaller | 37 |
| chr6.36083264.36083882 | chr6.36083288.36083882 | 595 | 620 | FALSE | smaller | 25 |
| chr6.36123628.36124107 | chr6.36123633.36124107 | 475 | 481 | FALSE | smaller | 6 |
| chr6.36127554.36127796 | chr6.36127589.36127796 | 208 | 244 | FALSE | smaller | 36 |
| chr6.36235388.36235649 | chr6.36235388.36235599 | 212 | 263 | FALSE | smaller | 51 |
| chr6.36345693.36345954 | chr6.36345754.36345951 | 198 | 263 | FALSE | smaller | 65 |
| chr6.36396182.36396939 | chr6.36396182.36396874 | 693 | 759 | FALSE | smaller | 66 |
| chr6.36578369.36578700 | chr6.36578513.36578700 | 188 | 333 | FALSE | smaller | 145 |
| chr6.36590292.36590557 | chr6.36590292.36590555 | 264 | 267 | FALSE | smaller | 3 |
| chr6.36623324.36624312 | chr6.36623324.36624122 | 799 | 990 | FALSE | smaller | 191 |
| chr6.36660953.36661896 | chr6.36660953.36661819 | 867 | 945 | FALSE | smaller | 78 |
| chr6.36682423.36683048 | chr6.36682505.36683048 | 544 | 627 | FALSE | smaller | 83 |
| chr6.36693050.36693556 | chr6.36693052.36693426 | 375 | 508 | FALSE | smaller | 133 |
| chr6.36699196.36699692 | chr6.36699196.36699673 | 478 | 498 | FALSE | smaller | 20 |
| chr6.36711662.36712181 | chr6.36711662.36712105 | 444 | 521 | FALSE | smaller | 77 |
| chr6.36839145.36839666 | chr6.36839368.36839666 | 299 | 523 | FALSE | smaller | 224 |
| chr6.36840540.36841316 | chr6.36840576.36841316 | 741 | 778 | FALSE | smaller | 37 |
| chr6.36858495.36858977 | chr6.36858495.36858964 | 470 | 484 | FALSE | smaller | 14 |
| chr6.36885598.36886323 | chr6.36885598.36886309 | 712 | 727 | FALSE | smaller | 15 |
| chr6.36907869.36908288 | chr6.36907880.36908288 | 409 | 421 | FALSE | smaller | 12 |
| chr6.36927678.36928349 | chr6.36927678.36928332 | 655 | 673 | FALSE | smaller | 18 |
| chr6.36970438.36971496 | chr6.36970548.36971496 | 949 | 1060 | FALSE | smaller | 111 |
| chr6.36971681.36972505 | chr6.36971699.36972505 | 807 | 826 | FALSE | smaller | 19 |
| chr6.36975780.36976369 | chr6.36975780.36976286 | 507 | 591 | FALSE | smaller | 84 |
| chr6.37006825.37007483 | chr6.37006865.37007483 | 619 | 660 | FALSE | smaller | 41 |
| chr6.37029354.37029797 | chr6.37029362.37029797 | 436 | 445 | FALSE | smaller | 9 |
| chr6.37077613.37078425 | chr6.37077695.37078425 | 731 | 814 | FALSE | smaller | 83 |
| chr6.37141402.37141800 | chr6.37141404.37141747 | 344 | 400 | FALSE | smaller | 56 |
| chr6.37172091.37172608 | chr6.37172091.37172530 | 440 | 519 | FALSE | smaller | 79 |
| chr6.37207924.37208460 | chr6.37207924.37208362 | 439 | 538 | FALSE | smaller | 99 |
| chr6.37325676.37326680 | chr6.37325676.37326676 | 1001 | 1006 | FALSE | smaller | 5 |
| chr6.37481613.37482068 | chr6.37481613.37482055 | 443 | 457 | FALSE | smaller | 14 |
| chr6.37512367.37512857 | chr6.37512367.37512709 | 343 | 492 | FALSE | smaller | 149 |
| chr6.37565061.37566793 | chr6.37565061.37566728 | 1668 | 1734 | FALSE | smaller | 66 |
| chr6.37585575.37586466 | chr6.37585575.37586261 | 687 | 893 | FALSE | smaller | 206 |
| chr6.37657283.37658663 | chr6.37657283.37658000 | 718 | 1382 | FALSE | smaller | 664 |
| chr6.37657283.37658663 | chr6.37658038.37658472 | 435 | 1382 | FALSE | smaller | 947 |
| chr6.37664183.37665701 | chr6.37664229.37665701 | 1473 | 1520 | FALSE | smaller | 47 |
| chr6.37691895.37692390 | chr6.37691895.37692375 | 481 | 497 | FALSE | smaller | 16 |
| chr6.37713584.37713994 | chr6.37713762.37713994 | 233 | 412 | FALSE | smaller | 179 |
| chr6.37725717.37726493 | chr6.37725739.37726493 | 755 | 778 | FALSE | smaller | 23 |
| chr6.37820253.37820545 | chr6.37820253.37820504 | 252 | 294 | FALSE | smaller | 42 |
| chr6.37823361.37823645 | chr6.37823367.37823645 | 279 | 286 | FALSE | smaller | 7 |
| chr6.37931105.37931382 | chr6.37931163.37931382 | 220 | 279 | FALSE | smaller | 59 |
| chr6.37994234.37994605 | chr6.37994262.37994605 | 344 | 373 | FALSE | smaller | 29 |
| chr6.38115164.38115558 | chr6.38115266.38115558 | 293 | 396 | FALSE | smaller | 103 |
| chr6.38124290.38124740 | chr6.38124290.38124694 | 405 | 452 | FALSE | smaller | 47 |
| chr6.38239932.38240386 | chr6.38239932.38240376 | 445 | 456 | FALSE | smaller | 11 |
| chr6.38346682.38347050 | chr6.38346762.38347048 | 287 | 370 | FALSE | smaller | 83 |
| chr6.38571687.38571933 | chr6.38571697.38571933 | 237 | 248 | FALSE | smaller | 11 |
| chr6.38639064.38640397 | chr6.38639064.38640395 | 1332 | 1335 | FALSE | smaller | 3 |
| chr6.38974515.38975104 | chr6.38974523.38975104 | 582 | 591 | FALSE | smaller | 9 |
| chr6.39063024.39063917 | chr6.39063024.39063875 | 852 | 895 | FALSE | smaller | 43 |
| chr6.39295346.39295961 | chr6.39295597.39295961 | 365 | 617 | FALSE | smaller | 252 |
| chr6.39303121.39303650 | chr6.39303140.39303650 | 511 | 531 | FALSE | smaller | 20 |
| chr6.39425888.39426566 | chr6.39425888.39426436 | 549 | 680 | FALSE | smaller | 131 |
| chr6.39573122.39573308 | chr6.39573122.39573303 | 182 | 188 | FALSE | smaller | 6 |
| chr6.39849422.39850092 | chr6.39849422.39850090 | 669 | 672 | FALSE | smaller | 3 |
| chr6.39887451.39888503 | chr6.39887656.39888503 | 848 | 1054 | FALSE | smaller | 206 |
| chr6.40171101.40172080 | chr6.40171108.40172080 | 973 | 981 | FALSE | smaller | 8 |
| chr6.40196857.40197391 | chr6.40196857.40197304 | 448 | 536 | FALSE | smaller | 88 |
| chr6.40210594.40211191 | chr6.40210596.40211191 | 596 | 599 | FALSE | smaller | 3 |
| chr6.40308490.40309130 | chr6.40308490.40309125 | 636 | 642 | FALSE | smaller | 6 |
| chr6.40629887.40630585 | chr6.40629887.40630551 | 665 | 700 | FALSE | smaller | 35 |
| chr6.40775697.40776108 | chr6.40775727.40776108 | 382 | 413 | FALSE | smaller | 31 |
| chr6.40779483.40779817 | chr6.40779483.40779811 | 329 | 336 | FALSE | smaller | 7 |
| chr6.40848073.40848448 | chr6.40848113.40848443 | 331 | 377 | FALSE | smaller | 46 |
| chr6.40867514.40867870 | chr6.40867526.40867870 | 345 | 358 | FALSE | smaller | 13 |
| chr6.41039171.41039745 | chr6.41039171.41039658 | 488 | 576 | FALSE | smaller | 88 |
| chr6.41071825.41074166 | chr6.41071845.41074166 | 2322 | 2343 | FALSE | smaller | 21 |
| chr6.41373738.41374775 | chr6.41373914.41374775 | 862 | 1039 | FALSE | smaller | 177 |
| chr6.41424363.41424913 | chr6.41424396.41424913 | 518 | 552 | FALSE | smaller | 34 |
| chr6.41625472.41625894 | chr6.41625472.41625882 | 411 | 424 | FALSE | smaller | 13 |
| chr6.41713102.41714286 | chr6.41713102.41714099 | 998 | 1186 | FALSE | smaller | 188 |
| chr6.41734671.41735944 | chr6.41734671.41735940 | 1270 | 1275 | FALSE | smaller | 5 |
| chr6.41779558.41780654 | chr6.41779575.41780654 | 1080 | 1098 | FALSE | smaller | 18 |
| chr6.41786200.41788119 | chr6.41786234.41788119 | 1886 | 1921 | FALSE | smaller | 35 |
| chr6.41804235.41804474 | chr6.41804235.41804466 | 232 | 241 | FALSE | smaller | 9 |
| chr6.41805577.41806974 | chr6.41805577.41806947 | 1371 | 1399 | FALSE | smaller | 28 |
| chr6.41920656.41922063 | chr6.41920656.41922018 | 1363 | 1409 | FALSE | smaller | 46 |
| chr6.41936087.41936636 | chr6.41936087.41936631 | 545 | 551 | FALSE | smaller | 6 |
| chr6.41966385.41967577 | chr6.41966385.41967532 | 1148 | 1194 | FALSE | smaller | 46 |
| chr6.42010059.42010802 | chr6.42010059.42010714 | 656 | 745 | FALSE | smaller | 89 |
| chr6.42067974.42068579 | chr6.42067974.42068542 | 569 | 607 | FALSE | smaller | 38 |
| chr6.42099355.42100709 | chr6.42099379.42100709 | 1331 | 1356 | FALSE | smaller | 25 |
| chr6.42124313.42124792 | chr6.42124418.42124792 | 375 | 481 | FALSE | smaller | 106 |
| chr6.42166398.42167156 | chr6.42166398.42167089 | 692 | 760 | FALSE | smaller | 68 |
| chr6.42251609.42252578 | chr6.42251700.42252578 | 879 | 971 | FALSE | smaller | 92 |
| chr6.42278982.42279938 | chr6.42279138.42279938 | 801 | 958 | FALSE | smaller | 157 |
| chr6.42315774.42317087 | chr6.42315774.42317084 | 1311 | 1315 | FALSE | smaller | 4 |
| chr6.42377451.42378097 | chr6.42377475.42378097 | 623 | 648 | FALSE | smaller | 25 |
| chr6.42451524.42452059 | chr6.42451524.42452057 | 534 | 537 | FALSE | smaller | 3 |
| chr6.42553907.42554410 | chr6.42554036.42554410 | 375 | 505 | FALSE | smaller | 130 |
| chr6.42702143.42702552 | chr6.42702170.42702552 | 383 | 411 | FALSE | smaller | 28 |
| chr6.42901452.42902065 | chr6.42901452.42902044 | 593 | 615 | FALSE | smaller | 22 |
| chr6.42923626.42924251 | chr6.42923626.42924221 | 596 | 627 | FALSE | smaller | 31 |
| chr6.43075507.43077360 | chr6.43075507.43077339 | 1833 | 1855 | FALSE | smaller | 22 |
| chr6.43174131.43174920 | chr6.43174131.43174892 | 762 | 791 | FALSE | smaller | 29 |
| chr6.43181846.43182659 | chr6.43181858.43182562 | 705 | 815 | FALSE | smaller | 110 |
| chr6.43257167.43257474 | chr6.43257270.43257423 | 154 | 309 | FALSE | smaller | 155 |
| chr6.43280855.43281089 | chr6.43280855.43281046 | 192 | 236 | FALSE | smaller | 44 |
| chr6.43454442.43455565 | chr6.43454453.43455565 | 1113 | 1125 | FALSE | smaller | 12 |
| chr6.43516087.43517723 | chr6.43516217.43517723 | 1507 | 1638 | FALSE | smaller | 131 |
| chr6.43575279.43576535 | chr6.43575285.43576535 | 1251 | 1258 | FALSE | smaller | 7 |
| chr6.43635278.43636807 | chr6.43635479.43636807 | 1329 | 1531 | FALSE | smaller | 202 |
| chr6.43681348.43682042 | chr6.43681709.43682042 | 334 | 696 | FALSE | smaller | 362 |
| chr6.43807288.43808074 | chr6.43807321.43808074 | 754 | 788 | FALSE | smaller | 34 |
| chr6.43813207.43813440 | chr6.43813207.43813436 | 230 | 235 | FALSE | smaller | 5 |
| chr6.43851021.43851670 | chr6.43851046.43851670 | 625 | 651 | FALSE | smaller | 26 |
| chr6.43855989.43856639 | chr6.43856058.43856639 | 582 | 652 | FALSE | smaller | 70 |
| chr6.43903840.43904149 | chr6.43903842.43904149 | 308 | 311 | FALSE | smaller | 3 |
| chr6.43943758.43944259 | chr6.43943787.43944259 | 473 | 503 | FALSE | smaller | 30 |
| chr6.44051586.44051809 | chr6.44051588.44051809 | 222 | 225 | FALSE | smaller | 3 |
| chr6.44166489.44166865 | chr6.44166623.44166865 | 243 | 378 | FALSE | smaller | 135 |
| chr6.44180855.44181411 | chr6.44180855.44181405 | 551 | 558 | FALSE | smaller | 7 |
| chr6.44221243.44221836 | chr6.44221260.44221836 | 577 | 595 | FALSE | smaller | 18 |
| chr6.44245898.44248087 | chr6.44245959.44248087 | 2129 | 2191 | FALSE | smaller | 62 |
| chr6.44264898.44265815 | chr6.44264910.44265815 | 906 | 919 | FALSE | smaller | 13 |
| chr6.44270865.44271218 | chr6.44270865.44271211 | 347 | 355 | FALSE | smaller | 8 |
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| chr6.44275171.44277282 | chr6.44276248.44277282 | 1035 | 2113 | FALSE | smaller | 1078 |
| chr6.44309069.44309439 | chr6.44309086.44309401 | 316 | 372 | FALSE | smaller | 56 |
| chr6.44328336.44328787 | chr6.44328336.44328758 | 423 | 453 | FALSE | smaller | 30 |
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| chr6.44593360.44594065 | chr6.44593457.44594065 | 609 | 707 | FALSE | smaller | 98 |
| chr6.44668034.44668412 | chr6.44668042.44668412 | 371 | 380 | FALSE | smaller | 9 |
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| chr6.45986665.45987405 | chr6.45986665.45987398 | 734 | 742 | FALSE | smaller | 8 |
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| chr6.46008328.46008554 | chr6.46008345.46008554 | 210 | 228 | FALSE | smaller | 18 |
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| chr6.47264590.47265139 | chr6.47264590.47265072 | 483 | 551 | FALSE | smaller | 68 |
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| chr6.49336566.49337142 | chr6.49336566.49337125 | 560 | 578 | FALSE | smaller | 18 |
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| chr6.52929304.52929838 | chr6.52929319.52929838 | 520 | 536 | FALSE | smaller | 16 |
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| chr6.53358750.53360020 | chr6.53358844.53360020 | 1177 | 1272 | FALSE | smaller | 95 |
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| chr6.53894132.53894502 | chr6.53894134.53894422 | 289 | 372 | FALSE | smaller | 83 |
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| chr6.54824496.54824936 | chr6.54824506.54824880 | 375 | 442 | FALSE | smaller | 67 |
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| chr6.56757285.56757958 | chr6.56757285.56757861 | 577 | 675 | FALSE | smaller | 98 |
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| chr6.56820890.56821468 | chr6.56820890.56821390 | 501 | 580 | FALSE | smaller | 79 |
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| chr6.62492467.62492844 | chr6.62492467.62492749 | 283 | 379 | FALSE | smaller | 96 |
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| chr6.99618661.99619306 | chr6.99618683.99619306 | 624 | 647 | FALSE | smaller | 23 |
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| chr6.152044758.152045367 | chr6.152044857.152045367 | 511 | 611 | FALSE | smaller | 100 |
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| chr6.152221832.152222172 | chr6.152221850.152222172 | 323 | 342 | FALSE | smaller | 19 |
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| chr7.1693229.1695224 | chr7.1693229.1693883 | 655 | 1997 | FALSE | smaller | 1342 |
| chr7.1693229.1695224 | chr7.1693970.1695224 | 1255 | 1997 | FALSE | smaller | 742 |
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| chr7.1979443.1980110 | chr7.1979443.1979996 | 554 | 669 | FALSE | smaller | 115 |
| chr7.2041894.2042432 | chr7.2041941.2042324 | 384 | 540 | FALSE | smaller | 156 |
| chr7.2082231.2082604 | chr7.2082278.2082604 | 327 | 375 | FALSE | smaller | 48 |
| chr7.2099727.2100576 | chr7.2099740.2100576 | 837 | 851 | FALSE | smaller | 14 |
| chr7.2102361.2103630 | chr7.2102406.2103630 | 1225 | 1271 | FALSE | smaller | 46 |
| chr7.2146732.2147660 | chr7.2146732.2147133 | 402 | 930 | FALSE | smaller | 528 |
| chr7.2165301.2165733 | chr7.2165301.2165566 | 266 | 434 | FALSE | smaller | 168 |
| chr7.2172666.2173201 | chr7.2172688.2173201 | 514 | 537 | FALSE | smaller | 23 |
| chr7.2174997.2175804 | chr7.2175002.2175804 | 803 | 809 | FALSE | smaller | 6 |
| chr7.2241707.2242988 | chr7.2241707.2242946 | 1240 | 1283 | FALSE | smaller | 43 |
| chr7.2309850.2310386 | chr7.2309850.2310339 | 490 | 538 | FALSE | smaller | 48 |
| chr7.2321092.2321469 | chr7.2321184.2321469 | 286 | 379 | FALSE | smaller | 93 |
| chr7.2380127.2380457 | chr7.2380127.2380349 | 223 | 332 | FALSE | smaller | 109 |
| chr7.2403143.2404655 | chr7.2403158.2404655 | 1498 | 1514 | FALSE | smaller | 16 |
| chr7.2468607.2469339 | chr7.2468800.2469339 | 540 | 734 | FALSE | smaller | 194 |
| chr7.2523959.2524331 | chr7.2523995.2524331 | 337 | 374 | FALSE | smaller | 37 |
| chr7.2577008.2577543 | chr7.2577008.2577530 | 523 | 537 | FALSE | smaller | 14 |
| chr7.2577975.2578821 | chr7.2577984.2578821 | 838 | 848 | FALSE | smaller | 10 |
| chr7.2605869.2606417 | chr7.2605929.2606417 | 489 | 550 | FALSE | smaller | 61 |
| chr7.2612969.2613733 | chr7.2612969.2613598 | 630 | 766 | FALSE | smaller | 136 |
| chr7.2639852.2640396 | chr7.2639928.2640396 | 469 | 546 | FALSE | smaller | 77 |
| chr7.2647127.2647773 | chr7.2647133.2647773 | 641 | 648 | FALSE | smaller | 7 |
| chr7.2717309.2717823 | chr7.2717315.2717823 | 509 | 516 | FALSE | smaller | 7 |
| chr7.2789699.2790128 | chr7.2789761.2790128 | 368 | 431 | FALSE | smaller | 63 |
| chr7.2803115.2803371 | chr7.2803170.2803371 | 202 | 258 | FALSE | smaller | 56 |
| chr7.2815034.2815679 | chr7.2815034.2815565 | 532 | 647 | FALSE | smaller | 115 |
| chr7.2843898.2844809 | chr7.2843898.2844106 | 209 | 913 | FALSE | smaller | 704 |
| chr7.2843898.2844809 | chr7.2844145.2844809 | 665 | 913 | FALSE | smaller | 248 |
| chr7.3272617.3273293 | chr7.3272617.3273166 | 550 | 678 | FALSE | smaller | 128 |
| chr7.3399952.3400223 | chr7.3399958.3400223 | 266 | 273 | FALSE | smaller | 7 |
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| chr7.4709245.4709541 | chr7.4709289.4709541 | 253 | 298 | FALSE | smaller | 45 |
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| chr7.17600061.17600668 | chr7.17600061.17600658 | 598 | 609 | FALSE | smaller | 11 |
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| chr7.17773251.17773524 | chr7.17773251.17773519 | 269 | 275 | FALSE | smaller | 6 |
| chr7.17901161.17901686 | chr7.17901327.17901639 | 313 | 527 | FALSE | smaller | 214 |
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| chr7.23298183.23300094 | chr7.23298183.23298616 | 434 | 1913 | FALSE | smaller | 1479 |
| chr7.23298183.23300094 | chr7.23298762.23300094 | 1333 | 1913 | FALSE | smaller | 580 |
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| chr7.24698687.24699067 | chr7.24698772.24699067 | 296 | 382 | FALSE | smaller | 86 |
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| chr7.24826148.24826568 | chr7.24826148.24826531 | 384 | 422 | FALSE | smaller | 38 |
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| chr7.25636076.25637380 | chr7.25636661.25637380 | 720 | 1306 | FALSE | smaller | 586 |
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| chr7.31335951.31336969 | chr7.31335995.31336969 | 975 | 1020 | FALSE | smaller | 45 |
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| chr7.32489652.32490638 | chr7.32489724.32490638 | 915 | 988 | FALSE | smaller | 73 |
| chr7.32587436.32587951 | chr7.32587436.32587918 | 483 | 517 | FALSE | smaller | 34 |
| chr7.32588725.32589305 | chr7.32588725.32589258 | 534 | 582 | FALSE | smaller | 48 |
| chr7.32871891.32872451 | chr7.32872061.32872396 | 336 | 562 | FALSE | smaller | 226 |
| chr7.33043431.33043782 | chr7.33043431.33043765 | 335 | 353 | FALSE | smaller | 18 |
| chr7.33602772.33603228 | chr7.33602772.33603213 | 442 | 458 | FALSE | smaller | 16 |
| chr7.33796021.33796757 | chr7.33796021.33796686 | 666 | 738 | FALSE | smaller | 72 |
| chr7.33979479.33980572 | chr7.33979504.33980572 | 1069 | 1095 | FALSE | smaller | 26 |
| chr7.34069605.34070148 | chr7.34069605.34070106 | 502 | 545 | FALSE | smaller | 43 |
| chr7.34133421.34133902 | chr7.34133421.34133861 | 441 | 483 | FALSE | smaller | 42 |
| chr7.34296326.34296995 | chr7.34296433.34296995 | 563 | 671 | FALSE | smaller | 108 |
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| chr7.34338960.34339506 | chr7.34338960.34339482 | 523 | 548 | FALSE | smaller | 25 |
| chr7.34844755.34845278 | chr7.34844870.34845278 | 409 | 525 | FALSE | smaller | 116 |
| chr7.35185667.35187252 | chr7.35185667.35187199 | 1533 | 1587 | FALSE | smaller | 54 |
| chr7.35227347.35227842 | chr7.35227370.35227842 | 473 | 497 | FALSE | smaller | 24 |
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| chr7.35263444.35263865 | chr7.35263488.35263865 | 378 | 423 | FALSE | smaller | 45 |
| chr7.35415224.35415736 | chr7.35415224.35415712 | 489 | 514 | FALSE | smaller | 25 |
| chr7.35467389.35468006 | chr7.35467389.35467955 | 567 | 619 | FALSE | smaller | 52 |
| chr7.35487232.35487861 | chr7.35487244.35487861 | 618 | 631 | FALSE | smaller | 13 |
| chr7.35578314.35578900 | chr7.35578508.35578900 | 393 | 588 | FALSE | smaller | 195 |
| chr7.35593668.35594151 | chr7.35593789.35594141 | 353 | 485 | FALSE | smaller | 132 |
| chr7.35596095.35596541 | chr7.35596148.35596541 | 394 | 448 | FALSE | smaller | 54 |
| chr7.35613200.35613469 | chr7.35613203.35613469 | 267 | 271 | FALSE | smaller | 4 |
| chr7.35820810.35821192 | chr7.35820810.35821172 | 363 | 384 | FALSE | smaller | 21 |
| chr7.35875382.35875567 | chr7.35875389.35875567 | 179 | 187 | FALSE | smaller | 8 |
| chr7.35967468.35968355 | chr7.35967512.35968353 | 842 | 889 | FALSE | smaller | 47 |
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| chr7.36214742.36215483 | chr7.36214774.36215483 | 710 | 743 | FALSE | smaller | 33 |
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| chr7.36400630.36401045 | chr7.36400638.36401045 | 408 | 417 | FALSE | smaller | 9 |
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| chr7.36601184.36601444 | chr7.36601231.36601444 | 214 | 262 | FALSE | smaller | 48 |
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| chr7.36789758.36790753 | chr7.36789800.36790753 | 954 | 997 | FALSE | smaller | 43 |
| chr7.37117275.37117716 | chr7.37117344.37117716 | 373 | 443 | FALSE | smaller | 70 |
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| chr7.37447451.37449303 | chr7.37447479.37449303 | 1825 | 1854 | FALSE | smaller | 29 |
| chr7.37491975.37492215 | chr7.37491975.37492135 | 161 | 242 | FALSE | smaller | 81 |
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| chr7.39413982.39414891 | chr7.39414035.39414891 | 857 | 911 | FALSE | smaller | 54 |
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| chr7.42082087.42082466 | chr7.42082154.42082466 | 313 | 381 | FALSE | smaller | 68 |
| chr7.42247332.42247876 | chr7.42247332.42247822 | 491 | 546 | FALSE | smaller | 55 |
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| chr7.43197864.43199017 | chr7.43197873.43199017 | 1145 | 1155 | FALSE | smaller | 10 |
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| chr7.43868925.43869852 | chr7.43869278.43869852 | 575 | 929 | FALSE | smaller | 354 |
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| chr7.44246292.44247083 | chr7.44246417.44247083 | 667 | 793 | FALSE | smaller | 126 |
| chr7.44397099.44397450 | chr7.44397183.44397450 | 268 | 353 | FALSE | smaller | 85 |
| chr7.44565284.44566017 | chr7.44565322.44566017 | 696 | 735 | FALSE | smaller | 39 |
| chr7.44615014.44615540 | chr7.44615116.44615540 | 425 | 528 | FALSE | smaller | 103 |
| chr7.44634758.44635632 | chr7.44634758.44635625 | 868 | 876 | FALSE | smaller | 8 |
| chr7.44639208.44640393 | chr7.44639239.44640393 | 1155 | 1187 | FALSE | smaller | 32 |
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| chr7.44979711.44980154 | chr7.44979732.44980154 | 423 | 445 | FALSE | smaller | 22 |
| chr7.44999251.45000788 | chr7.44999251.45000418 | 1168 | 1539 | FALSE | smaller | 371 |
| chr7.44999251.45000788 | chr7.45000504.45000788 | 285 | 1539 | FALSE | smaller | 1254 |
| chr7.45156862.45157114 | chr7.45156862.45157102 | 241 | 254 | FALSE | smaller | 13 |
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| chr7.45363470.45364210 | chr7.45363514.45364210 | 697 | 742 | FALSE | smaller | 45 |
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| chr7.45689444.45689961 | chr7.45689444.45689864 | 421 | 519 | FALSE | smaller | 98 |
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| chr7.45974314.45974849 | chr7.45974339.45974839 | 501 | 537 | FALSE | smaller | 36 |
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| chr7.47481994.47483022 | chr7.47482009.47483022 | 1014 | 1030 | FALSE | smaller | 16 |
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| chr7.47604608.47605391 | chr7.47604608.47605317 | 710 | 785 | FALSE | smaller | 75 |
| chr7.47635110.47635682 | chr7.47635114.47635682 | 569 | 574 | FALSE | smaller | 5 |
| chr7.47651399.47651979 | chr7.47651404.47651979 | 576 | 582 | FALSE | smaller | 6 |
| chr7.47799617.47799894 | chr7.47799664.47799894 | 231 | 279 | FALSE | smaller | 48 |
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| chr7.47923064.47923441 | chr7.47923064.47923440 | 377 | 379 | FALSE | smaller | 2 |
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| chr7.50436266.50436819 | chr7.50436266.50436744 | 479 | 555 | FALSE | smaller | 76 |
| chr7.50502356.50502765 | chr7.50502382.50502765 | 384 | 411 | FALSE | smaller | 27 |
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| chr7.50821534.50821966 | chr7.50821564.50821966 | 403 | 434 | FALSE | smaller | 31 |
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| chr7.55307166.55307475 | chr7.55307235.55307475 | 241 | 311 | FALSE | smaller | 70 |
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| chr7.66075488.66076530 | chr7.66075488.66076502 | 1015 | 1044 | FALSE | smaller | 29 |
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| chr7.70707909.70708234 | chr7.70707909.70708216 | 308 | 327 | FALSE | smaller | 19 |
| chr7.70856907.70857384 | chr7.70856924.70857384 | 461 | 479 | FALSE | smaller | 18 |
| chr7.70953862.70954198 | chr7.70953865.70954198 | 334 | 338 | FALSE | smaller | 4 |
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| chr7.71603462.71604068 | chr7.71603462.71604004 | 543 | 608 | FALSE | smaller | 65 |
| chr7.71633291.71634122 | chr7.71633291.71634039 | 749 | 833 | FALSE | smaller | 84 |
| chr7.71718607.71719003 | chr7.71718607.71718915 | 309 | 398 | FALSE | smaller | 89 |
| chr7.71868604.71869213 | chr7.71868675.71869213 | 539 | 611 | FALSE | smaller | 72 |
| chr7.73154411.73155055 | chr7.73154477.73155055 | 579 | 646 | FALSE | smaller | 67 |
| chr7.73311493.73311738 | chr7.73311508.73311738 | 231 | 247 | FALSE | smaller | 16 |
| chr7.73330130.73330658 | chr7.73330130.73330569 | 440 | 530 | FALSE | smaller | 90 |
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| chr9.134328058.134328673 | chr9.134328085.134328673 | 589 | 617 | FALSE | smaller | 28 |
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| chr9.137114651.137117076 | chr9.137115059.137117076 | 2018 | 2427 | FALSE | smaller | 409 |
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| chr10.13650047.13650636 | chr10.13650085.13650636 | 552 | 591 | FALSE | smaller | 39 |
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| chr10.14476926.14477228 | chr10.14476926.14477223 | 298 | 304 | FALSE | smaller | 6 |
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| chr10.14608468.14608695 | chr10.14608473.14608695 | 223 | 229 | FALSE | smaller | 6 |
| chr10.14959168.14960468 | chr10.14959211.14960468 | 1258 | 1302 | FALSE | smaller | 44 |
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| chr10.16828251.16829116 | chr10.16828251.16829001 | 751 | 867 | FALSE | smaller | 116 |
| chr10.17059282.17059635 | chr10.17059298.17059635 | 338 | 355 | FALSE | smaller | 17 |
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| chr10.17425952.17426417 | chr10.17425952.17426254 | 303 | 467 | FALSE | smaller | 164 |
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| chr11.1842381.1843246 | chr11.1842381.1843211 | 831 | 867 | FALSE | smaller | 36 |
| chr11.2097486.2098130 | chr11.2097491.2098130 | 640 | 646 | FALSE | smaller | 6 |
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| chr11.2427968.2428645 | chr11.2427968.2428605 | 638 | 679 | FALSE | smaller | 41 |
| chr11.2449725.2450337 | chr11.2449859.2450337 | 479 | 614 | FALSE | smaller | 135 |
| chr11.2471424.2472292 | chr11.2471500.2472292 | 793 | 870 | FALSE | smaller | 77 |
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| chr11.32884938.32885184 | chr11.32884938.32885177 | 240 | 248 | FALSE | smaller | 8 |
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| chr11.57514110.57516117 | chr11.57514159.57516117 | 1959 | 2009 | FALSE | smaller | 50 |
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| chr11.86469436.86469946 | chr11.86469502.86469946 | 445 | 512 | FALSE | smaller | 67 |
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| chrX.153946967.153947648 | chrX.153946972.153947648 | 677 | 683 | FALSE | smaller | 6 |
| chrX.153955301.153956265 | chrX.153955362.153956077 | 716 | 966 | FALSE | smaller | 250 |
| chrX.153981981.153982671 | chrX.153981981.153982607 | 627 | 692 | FALSE | smaller | 65 |
| chrX.154013546.154014024 | chrX.154013570.154014024 | 455 | 480 | FALSE | smaller | 25 |
| chrX.154019846.154020421 | chrX.154019868.154020421 | 554 | 577 | FALSE | smaller | 23 |
| chrX.154021171.154021572 | chrX.154021267.154021572 | 306 | 403 | FALSE | smaller | 97 |
| chrX.154025317.154025625 | chrX.154025317.154025600 | 284 | 310 | FALSE | smaller | 26 |
| chrX.154033549.154034120 | chrX.154033784.154034120 | 337 | 573 | FALSE | smaller | 236 |
| chrX.154239940.154240402 | chrX.154239940.154240330 | 391 | 464 | FALSE | smaller | 73 |
| chrX.154378284.154379642 | chrX.154378284.154379589 | 1306 | 1360 | FALSE | smaller | 54 |
| chrX.154546321.154548405 | chrX.154546321.154548356 | 2036 | 2086 | FALSE | smaller | 50 |
| chrX.154600370.154600755 | chrX.154600370.154600680 | 311 | 387 | FALSE | smaller | 76 |
| chrX.154729514.154730201 | chrX.154729514.154730150 | 637 | 689 | FALSE | smaller | 52 |
| chrX.154774967.154775961 | chrX.154774967.154775408 | 442 | 996 | FALSE | smaller | 554 |
| chrX.154774967.154775961 | chrX.154775513.154775961 | 449 | 996 | FALSE | smaller | 547 |
b <- filter_by_overlaps(old_peaks_gr,new_peaks_gr)
b.a <- filter_by_overlaps(new_peaks_gr, old_peaks_gr)
b.b <- filter_by_non_overlaps(new_peaks_gr, old_peaks_gr)
b.c <- filter_by_non_overlaps(old_peaks_gr, new_peaks_gr)
new_peaks_gr %>% as.data.frame %>% summarize(mean =mean(width)) mean
1 606.7886number of peaks in old data set: 151666
number of peaks in new data set: 155557
number of overlaps in between both sets: 149895
number of peaks in new set that do not overlap old set: 5678
number of peaks in old set that do not overlap new set: 1985
Differential analysis
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"))
short_names <- paste0(pca_final_four_anno$indv,"_",pca_final_four_anno$trt,"_",pca_final_four_anno$time)
dge <- DGEList.data.frame(counts = x4_filtered, group = group, genes = row.names(x4_filtered))
group_1 <- c(rep(c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3"),4))
dge$group$indv <- pca_final_four_anno$indv
dge$group$time <- pca_final_four_anno$time
dge$group$trt <- pca_final_four_anno$trt
indv <- pca_final_four_anno$indv
efit4 <- readRDS("data/Final_four_data/efit4_filt_bl.RDS")
# mm <- model.matrix(~0 +group_1)
# colnames(mm) <- c("DNR_24", "DNR_3", "DOX_24","DOX_3","EPI_24", "EPI_3","MTX_24", "MTX_3", "TRZ_24","TRZ_3","VEH_24", "VEH_3")
#
# y <- voom(dge$counts, mm,plot =TRUE)
# #
# corfit <- duplicateCorrelation(y, mm, block = indv)
# #
# v <- voom(dge$counts, mm, block = indv, correlation = corfit$consensus)
# #
# fit <- lmFit(v, mm, block = indv, correlation = corfit$consensus)
# colnames(mm) <- c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3")
#
#
# cm <- makeContrasts(
# DNR_3.VEH_3 = DNR_3-VEH_3,
# DOX_3.VEH_3 = DOX_3-VEH_3,
# EPI_3.VEH_3 = EPI_3-VEH_3,
# MTX_3.VEH_3 = MTX_3-VEH_3,
# TRZ_3.VEH_3 = TRZ_3-VEH_3,
# DNR_24.VEH_24 =DNR_24-VEH_24,
# DOX_24.VEH_24= DOX_24-VEH_24,
# EPI_24.VEH_24= EPI_24-VEH_24,
# MTX_24.VEH_24= MTX_24-VEH_24,
# TRZ_24.VEH_24= TRZ_24-VEH_24,
# levels = mm)
# vfit <- lmFit(y, mm)
# vfit<- contrasts.fit(vfit, contrasts=cm)
#
# efit4 <- eBayes(vfit)
# saveRDS(efit4,"data/Final_four_data/efit4_filt_bl.RDS")
results = decideTests(efit4)
summary(results) DNR_3.VEH_3 DOX_3.VEH_3 EPI_3.VEH_3 MTX_3.VEH_3 TRZ_3.VEH_3
Down 5925 448 2959 22 0
NotSig 146648 155046 151480 155536 155560
Up 2987 66 1121 2 0
DNR_24.VEH_24 DOX_24.VEH_24 EPI_24.VEH_24 MTX_24.VEH_24 TRZ_24.VEH_24
Down 27539 21704 23244 1585 0
NotSig 92353 107936 107084 149730 155560
Up 35668 25920 25232 4245 0# V.DNR_3.top_ff= topTable(efit4, coef=1, adjust.method="BH", number=Inf, sort.by="p") %>% rownames_to_column("peak")
# V.DOX_3.top_ff= topTable(efit4, coef=2, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.EPI_3.top_ff= topTable(efit4, coef=3, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.MTX_3.top_ff= topTable(efit4, coef=4, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.TRZ_3.top_ff= topTable(efit4, coef=5, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.DNR_24.top_ff= topTable(efit4, coef=6, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.DOX_24.top_ff= topTable(efit4, coef=7, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.EPI_24.top_ff= topTable(efit4, coef=8, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.MTX_24.top_ff= topTable(efit4, coef=9, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.TRZ_24.top_ff= topTable(efit4, coef=10, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
#
# toplist_full_ff <- list(V.DNR_3.top_ff, V.DOX_3.top_ff,V.EPI_3.top_ff,V.MTX_3.top_ff,V.TRZ_3.top_ff,V.DNR_24.top_ff, V.DOX_24.top_ff,V.EPI_24.top_ff,V.MTX_24.top_ff,V.TRZ_24.top_ff)
# names(toplist_full_ff) <- c("DNR_3", "DOX_3","EPI_3","MTX_3","TRZ_3","DNR_24", "DOX_24","EPI_24","MTX_24","TRZ_24")
# toplist_ff <-map_df(toplist_full_ff, ~as.data.frame(.x), .id="trt_time")
# #
# toplist_ff <- toplist_ff %>%
# separate(trt_time, into= c("trt","time"), sep = "_") %>%
# mutate(trt=factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ"))) %>%
# mutate(time = factor(time, levels = c("3", "24"), labels = c("3 hours", "24 hours")))
# saveRDS(toplist_ff,"data/Final_four_data/toplist_ff.RDS")
toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")### making median lfc
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)
write_csv(median_3_lfc, "data/Final_four_data/median_3_lfc.csv")
write_csv(median_24_lfc, "data/Final_four_data/median_24_lfc.csv")Correlation of LFC
FCmatrix_ff <- subset(efit4$coefficients)
colnames(FCmatrix_ff) <-
c("DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"TRZ\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h",
"TRZ\n24h"
)
mat_col_ff <-
data.frame(
time = c(rep("3 hours", 5), rep("24 hours", 5)),
class = (c(
"AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
)))
rownames(mat_col_ff) <- colnames(FCmatrix_ff)
mat_colors_ff <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"))
names(mat_colors_ff$time) <- unique(mat_col_ff$time)
names(mat_colors_ff$class) <- unique(mat_col_ff$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
corrFC_ff <- cor(FCmatrix_ff)
htanno_ff <- HeatmapAnnotation(df = mat_col_ff, col = mat_colors_ff)
Heatmap(corrFC_ff, top_annotation = htanno_ff)
V.DNR_3.top= topTable(efit4, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_3.top= topTable(efit4, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_3.top= topTable(efit4, coef=3, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_3.top= topTable(efit4, coef=4, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_3.top= topTable(efit4, coef=5, adjust.method="BH", number=Inf, sort.by="p")
V.DNR_24.top= topTable(efit4, coef=6, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_24.top= topTable(efit4, coef=7, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_24.top= topTable(efit4, coef=8, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_24.top= topTable(efit4, coef=9, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_24.top= topTable(efit4, coef=10, adjust.method="BH", number=Inf, sort.by="p")
# volcanoplot(efit2,coef = c(1,2,3,4,5,6),style = "p-value",
# # highlight = 8,
# # names = efit2$genes$SYMBOL,
# hl.col = "red",xlab = "Log2 Fold Change",
# ylab = NULL,pch = 16,cex = 0.35,
# main = "test")
#
plot_filenames <- c("V.DNR_3.top","V.DOX_3.top","V.EPI_3.top","V.MTX_3.top",
"V.TRZ_.top","V.DNR_24.top","V.DOX_24.top","V.EPI_24.top",
"V.MTX_24.top","V.TRZ_24.top")
plot_files <- c( V.DNR_3.top,V.DOX_3.top,V.EPI_3.top,V.MTX_3.top,
V.TRZ_3.top,V.DNR_24.top,V.DOX_24.top,V.EPI_24.top,
V.MTX_24.top,V.TRZ_24.top)
volcanosig <- function(df, psig.lvl) {
df <- df %>%
mutate(threshold = ifelse(adj.P.Val > psig.lvl, "A", ifelse(adj.P.Val <= psig.lvl & logFC<=0,"B","C")))
# ifelse(adj.P.Val <= psig.lvl & logFC >= 0,"B", "C")))
##This is where I could add labels, but I have taken out
# df <- df %>% mutate(genelabels = "")
# df$genelabels[1:topg] <- df$rownames[1:topg]
ggplot(df, aes(x=logFC, y=-log10(P.Value))) +
geom_point(aes(color=threshold))+
# geom_text_repel(aes(label = genelabels), segment.curvature = -1e-20,force = 1,size=2.5,
# arrow = arrow(length = unit(0.015, "npc")), max.overlaps = Inf) +
#geom_hline(yintercept = -log10(psig.lvl))+
xlab(expression("Log"[2]*" FC"))+
ylab(expression("-log"[10]*"P Value"))+
scale_color_manual(values = c("black", "red","blue"))+
theme_cowplot()+
ylim(0,20)+
xlim(-6,6)+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
}
#v1<- volcanosig(V.DA24.top, 0.01,0)
v1 <- volcanosig(V.DNR_3.top, 0.01)+ ggtitle("DNR 3 hour")
v2 <- volcanosig(V.DNR_24.top, 0.01)+ ggtitle("DNR 24 hour")+ylab("")
v3 <- volcanosig(V.DOX_3.top, 0.01)+ ggtitle("DOX 3 hour")
v4 <- volcanosig(V.DOX_24.top, 0.01)+ ggtitle("DOX 24 hour")+ylab("")
v5 <- volcanosig(V.EPI_3.top, 0.01)+ ggtitle("EPI 3 hour")
v6 <- volcanosig(V.EPI_24.top, 0.01)+ ggtitle("EPI 24 hour")+ylab("")
v7 <- volcanosig(V.MTX_3.top, 0.01)+ ggtitle("MTX 3 hour")
v8 <- volcanosig(V.MTX_24.top, 0.01)+ ggtitle("MTX 24 hour")+ylab("")
v9 <- volcanosig(V.TRZ_3.top, 0.01)+ ggtitle("TRZ 3 hour")
v10 <- volcanosig(V.TRZ_24.top, 0.01)+ ggtitle("TRZ 24 hour")+ylab("")
# volcanoplot(efit2,coef = 10,style = "p-value",
# highlight = 8,
# names = efit2$genes$SYMBOL,
# hl.col = "red",xlab = "Log2 Fold Change",
# ylab = NULL,pch = 16,cex = 0.35,
# main = "Using Trastuzumab 24 hour data and volcanoplot function")
plot_grid(v1,v2, rel_widths =c(.8,1))
plot_grid(v3,v4, rel_widths =c(.8,1))
plot_grid(v5,v6, rel_widths =c(.8,1))
plot_grid(v7,v8, rel_widths =c(.8,1))
plot_grid(v9,v10, rel_widths =c(.8,1))
DNR_3_top3_ff <- row.names(V.DNR_3.top[1:3,])
log_filt_ff <- filt4_matrix_lcpm %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
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",.))
row.names(log_filt_ff) <- row.names(filt4_matrix_lcpm)
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()
DOX_3_top3_ff <- row.names(V.DOX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
EPI_3_top3_ff <- row.names(V.EPI_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()
MTX_3_top3_ff <- row.names(V.MTX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
TRZ_3_top3_ff <- row.names(V.TRZ_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()
DNR_24_top3_ff <- row.names(V.DNR_24.top[1:3,])
# row.names(log_filt_23s) <- row.names(lcpm_h3_23s_filtered)
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()
DOX_24_top3_ff <- row.names(V.DOX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
EPI_24_top3_ff <- row.names(V.EPI_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()
MTX_24_top3_ff <- row.names(V.MTX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
TRZ_24_top3_ff <- row.names(V.TRZ_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()
Cormotif
# indv_n45 <- df_names_n45$indv
# indv <- factor(indv, levels = c(1,2,3,4,5,6))
# time_n45 <- df_names_n45$time
# time <- factor(time, levels =c("3h","24"))
# trt_n45 <- df_names_n45$trt
# label_n45 <- paste0(indv_n45,"_",trt_n45,"_",time_n45)
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(x4_filtered, log = TRUE)
colnames(y_TMM_cpm_ff) <- short_names
# y_TMM_cpm
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_four_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=4, max.iter = 500, runtype = "logCPM")
# saveRDS(cormotif_four_ff,"data/Final_four_data/cormotif_only4_run.RDS")
cormotif_initial_ff <- readRDS("data/Final_four_data/cormotif_ff_4_run.RDS")
gene_prob_tran_ff <- cormotif_initial_ff$bestmotif$p.post
rownames(gene_prob_tran_ff) <- rownames(x4_filtered)
motif_prob_ff <- cormotif_initial_ff$bestmotif$clustlike
rownames(motif_prob_ff) <- rownames(gene_prob_tran_ff)
# write.csv(motif_prob_n45_he,"data/cormotif_probability_45_list_he.csv")myColors <- rev(c("#FFFFFF", "#E6E6E6" ,"#CCCCCC", "#B3B3B3", "#999999", "#808080", "#666666","#4C4C4C", "#333333", "#191919","#000000"))
Cormotif::plotIC(cormotif_initial_ff)
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)
Cormotif::plotMotif(cormotif_initial_ff)
Study breakdown: 1 = DNR_3, 2 = DOX_3, 3 = EPI_3, 4 = MTX_3, 5 =
TRZ_3
6 = DNR_24, 7 = DOX_24, 8 = EPI_24, 9 = MTX_24, 10 = TRZ_24
# motif_prob_ff <- read.csv("data/cormotif_probability_45_list_he.csv")
# motif_prob_ff %>%
# as.data.frame() %>%
# dplyr::filter(V1<.5 & V2>.5 & V3 <.5& V4<0.5)
background_peaks <- motif_prob_ff %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
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)Number of Peaks by main grouping:
-EAR group has 7454 peaks.
-ESR group has 15781 peaks.
-LR group has 42609 peaks.
-NR group has 86331 peaks.
–Total number of peaks is 155560 –Total number of peaks in clusters is
152175
-percent of peaks classified into clusters: 97.8239907
-number of peaks not classified: 3385
Peak distribution characteristics
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
EAR_ff_gr <- EAR_ff %>% GRanges()
ESR_ff_gr <- ESR_ff %>% GRanges()
LR_ff_gr <- LR_ff %>% GRanges()
NR_ff_gr <- NR_ff %>% GRanges()
background_gr <- background_peaks %>% GRanges()
# mylist <- list(EAR_ff_gr,ESR_ff_gr,LR_ff_gr,NR_ff_gr,background_gr)
# peakAnnoList<- lapply(mylist, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR","ESR","LR","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_motif.RDS")
peakAnnoList_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_motif.RDS")
plotAnnoBar(peakAnnoList_motif[c(4,1:3)])+
ggtitle ("Genomic Feature Distribution, Four groups")
#### HOw I assigned expressed NG TSS to new peak file
### export new peak file as .bed file
a4gr <- all_four_filt_counts %>%
dplyr::select(seqnames:Geneid) %>%
dplyr::rename("Peakid"=Geneid) %>%
GRanges()
# write_delim((as.data.frame(a4gr)),delim = "\t",file = "data/Final_four_data/all_four_filt.bed", col_name=FALSE)## write tab delim datafile without column headers
###Do the same with TSS file
# ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl")
# my_attributes <- c('chromosome_name', 'start_position', 'end_position','entrezgene_id', 'ensembl_gene_id', 'hgnc_symbol')
# searchAttributes(mart = ensembl, pattern = "refseq")
# searchDatasets(mart=ensembl,pattern="NCBI")
# gene_list <- getBM(attributes=my_attributes,
# filters = "hgnc_symbol",
# values = S13Table$SYMBOL,
# mart = ensembl)
# write.csv(gene_ref,"data/gene_ref.csv")
# listFilters(ensembl)
####
# gene_ref <- read.csv("data/gene_ref.csv")
# gene_full_list <- gene_list %>%
# mutate(chromosome_name=as.numeric(chromosome_name))%>%
# # filter(!is.na(chromosome_name)) #%>%
# na.omit(.)
# dplyr::select(chromosome_name,start_position,end_position,entrezgene_id,ensembl_gene_id)
### I performed the bedtools using cli. I moved the files to the correct directory on my hardrive to do this.
# bedtools closest -a all_four_filt.bed -b All_TSS_NGs.bed -D b >assigned_newNGbyTSS.bed
#### This file only has the tss, I had to link the geneIDs and symbols and names using the lookup_table (contains TSS, Start and end, ENTREZID, ensembl_id,and SYBOL)
lookup_table <- read.csv("data/Final_four_data/expressedgene_lookuptable.csv", row.names = 1)
NG_assigned_table <- read_delim("data/Final_four_data/assigned_newNGbyTSS.bed", col_names = FALSE)
TSS_NG_table <- NG_assigned_table %>%
dplyr::rename("chr"=X1,
"start"=X2,
"end"= X3,
"Peakid"=X6,
"NG_chr"=X7,
"NG_start"=X8 ,
"NG_end"=X9 ,
"dist_to_NG"=X13) %>%
dplyr::select(chr:end,Peakid:NG_end,dist_to_NG) %>%
left_join(., lookup_table, by= c("NG_chr"="NG_chr","NG_end"="NG_start")) %>%
dplyr::rename("NG_end"="NG_end.y", "NG_TSS"=NG_end) %>%
dplyr::select(chr:NG_start,NG_end,NG_TSS,ENTREZID:SYMBOL,dist_to_NG) #%>%
# write_delim(.,"data/Final_four_data/TSS_assigned_NG.txt",delim = "\t",col_names = TRUE)
Collapsed_new_peaks <- TSS_NG_table %>%
group_by(chr, start, end, Peakid) %>%
summarise(chr=paste(unique(chr)),
start=paste(unique(start)),
end=paste(unique(end)),
NG_chr=paste(NG_chr,collapse=":"),
NG_start=paste(NG_start,collapse=":"),
NG_end=paste(NG_end,collapse=":"),
NG_TSS=paste(NG_TSS,collapse=":"),
NCBI_gene = paste(unique(ENTREZID),collapse=","),
ensembl_ID= paste(unique(ensembl_id),collapse = ","),
SYMBOL= paste(unique(SYMBOL),collapse = ",") ,
dist_to_NG =min(dist_to_NG))
# write_delim(Collapsed_new_peaks,"data/Final_four_data/collapsed_new_peaks.txt",delim = "\t",col_names = TRUE)Eight group peak characteristics
TSS_assigned_NG <- read_delim("data/Final_four_data/TSS_assigned_NG.txt", delim = "\t", col_names = TRUE)
Collapsed_new_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
open_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc > 0)
close_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc < 0)
open_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc > 0)
close_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc < 0)
medA <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0 & med_24h_lfc>0)
medB <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc < 0)
medC <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0& med_24h_lfc <0)
medD <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak"))%>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc > 0)
EAR_open <- EAR_ff %>%
dplyr::filter(Peakid %in% open_3med$peak)
EAR_open_gr <- EAR_open %>% GRanges()
EAR_close <- EAR_ff %>%
dplyr::filter(Peakid %in% close_3med$peak)
EAR_close_gr <- EAR_close %>% GRanges()
LR_open <- LR_ff %>%
dplyr::filter(Peakid %in% open_24med$peak)
LR_open_gr <- LR_open %>% GRanges()
LR_close <- LR_ff %>%
dplyr::filter(Peakid %in% close_24med$peak)
LR_close_gr <- LR_close %>% GRanges()
NR_gr <- NR_ff %>%
GRanges()
ESR_open <- ESR_ff %>%
dplyr::filter(Peakid %in% medA$peak)
ESR_open_gr <- ESR_open %>% GRanges()
ESR_close <- ESR_ff %>%
dplyr::filter(Peakid %in% medB$peak)
ESR_close_gr <- ESR_close %>% GRanges()
ESR_C <- ESR_ff %>%
dplyr::filter(Peakid %in% medC$peak)
ESR_D <- ESR_ff %>%
dplyr::filter(Peakid %in% medD$peak)
ESR_OC <- ESR_C %>%
rbind(ESR_D)
ESR_OC_gr <- ESR_OC %>% GRanges()
mylist_new <- list(EAR_open_gr, EAR_close_gr,ESR_open_gr, ESR_close_gr,ESR_OC_gr, LR_open_gr, LR_close_gr,NR_gr, background_gr)
names(mylist_new) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# peakAnnoList<- lapply(mylist_new, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_8motif.RDS")
peakAnnoList_8_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_8motif.RDS")
plotAnnoBar(peakAnnoList_8_motif[c(8,1:7)])+
ggtitle ("Genomic Feature Distribution, eight groups")
Overlapping peaks with H3K27ac data
raodah_fullpeaks <- readRDS( "data/Final_four_data/All_Raodahpeaks.RDS") %>%
GRanges %>%
GenomeInfoDb::keepStandardChromosomes(., pruning.mode = "coarse") %>%
dplyr::filter(seqnames!="chrM") %>%
dplyr::filter(seqnames!="chrY")
overlap_ac <- join_overlap_intersect((new_peaks_gr %>% dplyr::filter(seqnames !="chrY")),raodah_fullpeaks) %>% as.data.frame()
# overlap_ac %>%
# distinct(peakid)
# saveRDS(overlap_ac, "data/Final_four_data/overlapping_ac_atac_peaks.RDS")abs LFC across clusters
toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
mean_lfc <- toplist_ff %>%
mutate(EAR_open = if_else(peak %in% EAR_open$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$Peakid, "y", "no")) %>%
mutate(ESR_OC= if_else(peak %in% ESR_OC$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_OC = mean(absFC[ESR_OC == "y"]),
LR_op = mean(absFC[LR_open == "y"]),
LR_cl = mean(absFC[LR_close == "y"]),
NR = mean(absFC[NR == "y"])
) %>%
as.data.frame()
mean_lfc %>%
ungroup() %>%
pivot_longer(!c(trt, time), names_to = "Motif",
values_to = "meanLFC") %>%
# mutate(Set = dplyr::case_match(Motif,
# "EAR open"~ EAR_op ,
# EAR_cm ~"EAR close" ,
# ESR_om ~"ESR open",
# ESR_cm ~ "ESR close",
# ESR_ocm ~ "ESR open/close",
# LR_om~ "LR open",
# LR_cm~ "LR close",
# NRm ~ "No Response",
# .default = Motif)) %>%
ggplot(., aes(x = time,y = meanLFC,col = trt,
group = trt
)) +
geom_point(size = 2) +
geom_line(size = 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 = 1.5),
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"
)
)
sessionInfo()R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cowplot_1.1.3
[2] ComplexHeatmap_2.20.0
[3] ggrepel_0.9.6
[4] plyranges_1.24.0
[5] ggsignif_0.6.4
[6] genomation_1.36.0
[7] eulerr_7.0.2
[8] devtools_2.4.5
[9] usethis_3.0.0
[10] ggpubr_0.6.0
[11] BiocParallel_1.38.0
[12] Cormotif_1.50.0
[13] affy_1.82.0
[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] edgeR_4.2.1
[20] limma_3.60.4
[21] rtracklayer_1.64.0
[22] org.Hs.eg.db_3.19.1
[23] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[24] GenomicFeatures_1.56.0
[25] AnnotationDbi_1.66.0
[26] Biobase_2.64.0
[27] GenomicRanges_1.56.1
[28] GenomeInfoDb_1.40.1
[29] IRanges_2.38.1
[30] S4Vectors_0.42.1
[31] BiocGenerics_0.50.0
[32] ChIPseeker_1.40.0
[33] RColorBrewer_1.1-3
[34] broom_1.0.6
[35] kableExtra_1.4.0
[36] lubridate_1.9.3
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.2
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.2.1
[44] ggplot2_3.5.1
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.4
[2] matrixStats_1.4.1
[3] bitops_1.0-8
[4] enrichplot_1.24.4
[5] doParallel_1.0.17
[6] httr_1.4.7
[7] profvis_0.3.8
[8] tools_4.4.1
[9] backports_1.5.0
[10] utf8_1.2.4
[11] R6_2.5.1
[12] lazyeval_0.2.2
[13] GetoptLong_1.0.5
[14] urlchecker_1.0.1
[15] withr_3.0.1
[16] preprocessCore_1.66.0
[17] cli_3.6.3
[18] formatR_1.14
[19] scatterpie_0.2.4
[20] labeling_0.4.3
[21] sass_0.4.9
[22] Rsamtools_2.20.0
[23] systemfonts_1.1.0
[24] yulab.utils_0.1.7
[25] DOSE_3.30.5
[26] svglite_2.1.3
[27] R.utils_2.12.3
[28] sessioninfo_1.2.2
[29] plotrix_3.8-4
[30] BSgenome_1.72.0
[31] impute_1.78.0
[32] rstudioapi_0.16.0
[33] RSQLite_2.3.7
[34] shape_1.4.6.1
[35] generics_0.1.3
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] BiocIO_1.14.0
[39] vroom_1.6.5
[40] gtools_3.9.5
[41] car_3.1-2
[42] GO.db_3.19.1
[43] Matrix_1.7-0
[44] fansi_1.0.6
[45] abind_1.4-8
[46] R.methodsS3_1.8.2
[47] lifecycle_1.0.4
[48] whisker_0.4.1
[49] yaml_2.3.10
[50] carData_3.0-5
[51] SummarizedExperiment_1.34.0
[52] gplots_3.1.3.1
[53] qvalue_2.36.0
[54] SparseArray_1.4.8
[55] blob_1.2.4
[56] promises_1.3.0
[57] crayon_1.5.3
[58] miniUI_0.1.1.1
[59] lattice_0.22-6
[60] KEGGREST_1.44.1
[61] magick_2.8.4
[62] pillar_1.9.0
[63] knitr_1.48
[64] fgsea_1.30.0
[65] rjson_0.2.23
[66] boot_1.3-31
[67] codetools_0.2-20
[68] fastmatch_1.1-4
[69] glue_1.7.0
[70] getPass_0.2-4
[71] ggfun_0.1.6
[72] remotes_2.5.0
[73] data.table_1.16.0
[74] vctrs_0.6.5
[75] png_0.1-8
[76] treeio_1.28.0
[77] gtable_0.3.5
[78] cachem_1.1.0
[79] xfun_0.47
[80] mime_0.12
[81] S4Arrays_1.4.1
[82] tidygraph_1.3.1
[83] iterators_1.0.14
[84] statmod_1.5.0
[85] ellipsis_0.3.2
[86] nlme_3.1-166
[87] ggtree_3.12.0
[88] bit64_4.0.5
[89] rprojroot_2.0.4
[90] bslib_0.8.0
[91] affyio_1.74.0
[92] KernSmooth_2.23-24
[93] colorspace_2.1-1
[94] DBI_1.2.3
[95] seqPattern_1.36.0
[96] tidyselect_1.2.1
[97] processx_3.8.4
[98] bit_4.0.5
[99] compiler_4.4.1
[100] curl_5.2.2
[101] git2r_0.33.0
[102] httr2_1.0.4
[103] xml2_1.3.6
[104] DelayedArray_0.30.1
[105] shadowtext_0.1.4
[106] caTools_1.18.3
[107] callr_3.7.6
[108] rappdirs_0.3.3
[109] digest_0.6.37
[110] rmarkdown_2.28
[111] XVector_0.44.0
[112] htmltools_0.5.8.1
[113] pkgconfig_2.0.3
[114] MatrixGenerics_1.16.0
[115] highr_0.11
[116] fastmap_1.2.0
[117] GlobalOptions_0.1.2
[118] htmlwidgets_1.6.4
[119] rlang_1.1.4
[120] UCSC.utils_1.0.0
[121] shiny_1.9.1
[122] farver_2.1.2
[123] jquerylib_0.1.4
[124] jsonlite_1.8.8
[125] GOSemSim_2.30.2
[126] R.oo_1.26.0
[127] RCurl_1.98-1.16
[128] magrittr_2.0.3
[129] GenomeInfoDbData_1.2.12
[130] ggplotify_0.1.2
[131] patchwork_1.3.0
[132] munsell_0.5.1
[133] Rcpp_1.0.13
[134] ape_5.8
[135] viridis_0.6.5
[136] stringi_1.8.4
[137] ggraph_2.2.1
[138] zlibbioc_1.50.0
[139] MASS_7.3-61
[140] pkgbuild_1.4.4
[141] plyr_1.8.9
[142] parallel_4.4.1
[143] Biostrings_2.72.1
[144] graphlayouts_1.1.1
[145] splines_4.4.1
[146] circlize_0.4.16
[147] hms_1.1.3
[148] locfit_1.5-9.10
[149] ps_1.8.0
[150] igraph_2.0.3
[151] pkgload_1.4.0
[152] reshape2_1.4.4
[153] futile.options_1.0.1
[154] XML_3.99-0.17
[155] evaluate_1.0.0
[156] lambda.r_1.2.4
[157] BiocManager_1.30.25
[158] foreach_1.5.2
[159] tzdb_0.4.0
[160] tweenr_2.0.3
[161] httpuv_1.6.15
[162] polyclip_1.10-7
[163] clue_0.3-65
[164] gridBase_0.4-7
[165] ggforce_0.4.2
[166] xtable_1.8-4
[167] restfulr_0.0.15
[168] tidytree_0.4.6
[169] rstatix_0.7.2
[170] later_1.3.2
[171] viridisLite_0.4.2
[172] aplot_0.2.3
[173] memoise_2.0.1
[174] GenomicAlignments_1.40.0
[175] cluster_2.1.6
[176] timechange_0.3.0