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Jensen_response_plots.R
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Jensen_response_plots.R
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# November 13, 2018
# Plot for response to Harris et al.
#####################################################
# BF heat map #
# Red and Grey scale, truncated x axis, 3 panels #
# Panel 1: Original plot, SNP based #
# Panel 2: More comprehensive dem model, SNP based #
# Panel 3: More comprehensive dem model, bp based #
#####################################################
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
peaks=read.table('peaks_082712_400_50_2_2_0_pan_509_top50_rhoForCalling.txt')
pdf("fig8_BFheatmap_panels.pdf",width=5.69, height=2.43, title = "Figure 2",paper="special")
#width=6.83,height=4.86
#width=4.86,height=2.43
s=as.vector(c(1,5))
split.screen(s)
change=0.06
mb1=0.1
ml1=0.4
mt1=0
mr1=0
mb2=0.1
ml2=0.35
mt2=0
mr2=0.05
mb3=0.1
ml3=0.3
mt3=0
mr3=0.1
mb4=0.1
ml4=0.25
mt4=0
mr4=0.15
mb5=0.1
ml5=0.2
mt5=0
mr5=0.2
mb6=0.1
ml6=0.15
mt6=0
mr6=0.25
tcl=0.2
pdjy=2.0
pdjx=-2.5
cex1=0.7
cex2=0.7
cex3=1
par(cex.axis=cex1) #The magnification to be used for axis annotation relative to the current setting of cex.
par(cex.lab=cex1) #The magnification to be used for x and y labels relative to the current setting of cex.
par(omi=c(0.35,0.1,0.35,0.1)) #A vector of the form c(bottom, left, top, right) giving the size of the outer margins in inches.
shift=0.02
screen(1)
par(mai=c(mb1,ml1,mt1,mr1)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("HS_vs_SS_likelihood_random509_admixture_sorted.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13), ylim=c(0,41))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(H2~"/"~H1),side=2,cex=cex2,padj=-3)
mtext(expression(A),at=-0.5,padj=-0.5,cex=cex3)
# Draw a legend on the upper right:
#rect(5-0.5, 40-0.5, 5+0.5, 40+0.5,density=-1,col='black',lwd=0., border="white")
#rect(5-0.5, 38.5-0.5, 5+0.5, 38.5+0.5,density=-1,col='gray35',lwd=0., border="white")
#rect(5-0.5, 37-0.5, 5+0.5, 37+0.5,density=-1,col='gray55',lwd=0., border="white")
#rect(5-0.5, 35.5-0.5, 5+0.5, 35.5+0.5,density=-1,col='gray65',lwd=0., border="white")
#rect(5-0.5, 34-0.5, 5+0.5, 34+0.5,density=-1,col='red',lwd=0., border="white")
#mtext(expression(BF>100), at=c(8.1), line=-.5, cex=0.35)
#mtext(expression(100>~BF>=30) , at=c(9.25), line=-0.8, cex=0.35)
#mtext(expression(30>~BF>=10) , at=c(9), line=-1.1, cex=0.35)
#mtext(expression(10>~BF>=1) , at=c(8.75), line=-1.4, cex=0.35)
#mtext(expression(1>~BF>=0) , at=c(8.5), line=-1.7, cex=0.35)
#rect(4,33,13,41, density=-1,border='black', lwd=0.5)
screen(2)
par(mai=c(mb2,ml2,mt2,mr2)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_posteriorDistn_95CI_rho_5e-9_theta_0.01_theta_10_selection_True_snps_BFs_numSim_16000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(B),at=-0.5,padj=-0.5,cex=cex3)
screen(3)
peaks=read.table('top50peaks_fixed_bp.txt')
par(mai=c(mb3,ml3,mt3,mr3)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_posteriorDistn_95CI_rho_5e-9_theta_0.01_theta_10_selection_True_bps_BFs_numSim_16000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,9],40*peaks[,10],col='gold',pch=20, cex=0.6)
mtext(expression(H12),side=1,cex=cex2,padj=2.25, at=16)
mtext(expression(C),at=-0.5,padj=-0.5,cex=cex3)
screen(4)
peaks=read.table('peaks_082712_400_50_2_2_0_pan_509_top50_rhoForCalling.txt')
par(mai=c(mb4,ml4,mt4,mr4)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_uniformPrior_rho_5e-9_theta_0.01_theta_10_selection_True_snps_BFs_numSim_16000.txt")
# try zeroing out entries with really low sim counts
a=d[,3]+d[,4]
#b=a<100
#d[b,3]=0
#d[b,4]=0
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(D),at=-0.5,padj=-0.5,cex=cex3)
screen(5)
peaks=read.table('top50peaks_fixed_bp.txt')
par(mai=c(mb4,ml4,mt4,mr4)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_uniformPrior_rho_5e-9_theta_0.01_theta_10_selection_True_bps_BFs_numSim_16000.txt")
# try zeroing out entries with really low sim counts
a=d[,3]+d[,4]
#b=a<100
#d[b,3]=0
#d[b,4]=0
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,9],40*peaks[,10],col='gold',pch=20, cex=0.6)
mtext(expression(E),at=-0.5,padj=-0.5,cex=cex3)
close.screen(all=TRUE)
dev.off()
############################################################################################
# Plot the distribution of H12 values observed under different demographic models and data #
############################################################################################
###############
# SNP windows #
###############
setwd("/Users/nanditagarud/Documents/Ace/neutralDemography")
a=read.table('constNe106_neutrality_400SNPs_MS_snps.txt')
#a=read.table('constantNe106_neutral.txt')
b=read.table('dadi1_recoded_neutrality_MS_snps.txt')
#b=read.table('neutralSims_dadi1_509_new.txt')
c=read.table('dadi2_neutrality_recoded_MS_snps.txt')
#c=read.table('neutralSims_dadi2_509_new.txt')
##d=read.table('admix_noSelection_509.txt')
##e=read.table('admix_bot_noSelection_5*10e-9.txt_2') # admixture + bottleneck # this has old growth rate
f=read.table('constNe2.2e6_neutrality_400SNPs_MS_snps.txt')
#f=read.table('constantNe2.7_neutral.txt') # constantNe=2.7x10^6
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis/")
##g=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0_selection_False_snps.txt') # add the admixture with paramteres drawn from 95% CIs
d=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_MS_snps.txt')
#d=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_snps.txt')
e=read.table('admixture_bottleneck_mode_corrected_neutrality_MS.txt_snps.txt')
#e=read.table('admixture_bottleneck_mode_corrected_neutrality.txt_snps.txt')
i=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_MS_snps.txt')
#i=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_snps.txt') # add the admixture with paramteres drawn from 95% CIs correcting the growth rate
setwd("/Users/nanditagarud/Documents/Ace/")
chr3R_509 = read.table('clusters_082712_400_50_2_2_0_chr3R_withRho_threshold.txt')
chr3L_509 = read.table('clusters_082712_400_50_2_2_0_chr3L_withRho_threshold.txt')
chr2L_509 = read.table('clusters_082712_400_50_2_2_0_chr2L_withRho_threshold.txt')
chr2R_509 = read.table('clusters_082712_400_50_2_2_0_chr2R_withRho_threshold.txt')
h=c(chr3R_509[,28],chr3L_509[,28],chr2R_509[,28],chr2L_509[,28])
peaks=read.table('peaks_082712_400_50_2_2_0_pan_509_top50.txt')
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
# plot:
pdf("Figure6A.pdf",width=3.27,height=3, title = "Figure S4",paper="special")
tcl=0.2
pdjy=2.5
pdjx=-3.0
cex1=0.5
cex2=0.6
cex3=1
par(cex.axis=cex1) #The magnification to be used for axis annotation relative to the current setting of cex.
par(cex.lab=cex1) #The magnification to be used for x and y labels relative to the current setting of cex.
par(omi=c(0,0,0,0)) #A vector of the form c(bottom, left, top, right) giving the size of the outer margins in inches.
par(mai=c(0.5,0.5,0.5,0.5)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
shift=0.02
boxplot(h,d[1:150000,9], e[1:150000,9],i[1:150000,9],a[1:150000,26],f[1:150000,26], b[1:150000,26],c[1:150000,26], ylim=c(0,1), col=c('black','orange','purple','brown','skyblue','forestgreen','blue','red','gold'), pch=20, cex=0.4, border=c('black','orange','purple','brown','skyblue','forestgreen','blue','red','gold'), xaxs="i",yaxs = "i", xlab="",ylab="", axes=F )
points(rep(1,50), peaks[,28], col='red', pch=20, cex=0.7)
axis(1,at=c(0,8),labels=F,tcl=0)
axis(2,at=c(0,1),labels=F,tcl=0)
axis(2,at=seq(0,1,by=0.05),labels=seq(0,1,by=0.05),padj=pdjy,tcl=-tcl)
axis(3,at=c(0,8),labels=F,tcl=0)
axis(4,at=c(0,1),labels=F,tcl=0)
mtext(expression("H12"),side=2,cex=cex2,padj=-2.25)
dev.off()
############
# Zoom in
############
boxplot(h, d[1:150000,9], e[1:150000,9], i[1:150000,9], ylim=c(0,0.1), col=c('grey','orange','purple','brown'), ylab='H12', names=c('data','admix_mode','admix_bot_mode','admix_95CI'), main='400 SNP windows, Zoom in')
points(rep(1,50), peaks[,28], col='red', pch=20, cex=0.1.3)
###############
# 10kb windows #
###############
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
#admix_mode=read.table('Admixture_mode_rho_5e-9_theta_0_selection_False_bps.txt')
#admix_mode_corr=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_bps.txt')
admix_mode_corr=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_MS_bps.txt')
#admix_posterior=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0_selection_False_bps.txt')
#admix_posterior_corr=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_bps.txt')
admix_posterior_corr=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_MS_bps.txt')
#admix_uniform=read.table('Admixture_uniformPrior_rho_5e-9_theta_0_selection_False_bps.txt')
#admix_bot_mode=read.table('admixture_bottleneck_mode_neutrality.txt_bps.txt') # admixture + bottleneck
#admix_bot_mode_corr=read.table('admixture_bottleneck_mode_corrected_neutrality.txt_bps.txt') # admixture + bottleneck
admix_bot_mode_corr=read.table('admixture_bottleneck_mode_corrected_neutrality_MS.txt_bps.txt') # admixture + bottleneck
#const106=read.table('constNe106_neutrality_bps.txt') # constantNe=2.7x10^6
#const22106=read.table('constNe2.2e6_neutrality_bps.txt') # constantNe=2.7x10^6
#dadi1=read.table('dadi1_neutrality_bps.txt') # constantNe=2.7x10^6
#dadi2=read.table('dadi2_neutrality_bps.txt') # constantNe=2.7x10^6
const106=read.table('constNe106_neutrality_MS_bps.txt') # constantNe=2.7x10^6
const22106=read.table('constNe2.2e6_neutrality_MS_bps.txt') # constantNe=2.7x10^6
dadi1=read.table('dadi1_neutrality_bps.txt') # constantNe=2.7x10^6
dadi2=read.table('dadi2_neutrality_recoded_MS_bps.txt') # constantNe=2.7x10^6
# report s and pi for each of these models
i=12
mean(admix_joint[,i])/100
mean(admix_mode[,i])/100
mean(admix_posterior[,i])/100
mean(admix_uniform[,i])/100
mean(admix_bot_mode[,i])/100
mean(const106[,i])/100
mean(const22106[,i])/100
mean(dadi1[,i])/100
mean(dadi2[,i])/100
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis/data_10kb_scan")
chr3R_509 = read.table('H12_scan_10kb_chr3R_rho5e-9removed.txt')
chr3L_509 = read.table('H12_scan_10kb_chr3L_rho5e-9removed.txt')
chr2L_509 = read.table('H12_scan_10kb_chr2L_rho5e-9removed.txt')
chr2R_509 = read.table('H12_scan_10kb_chr2R_rho5e-9removed.txt')
h=c(chr3R_509[,9],chr3L_509[,9],chr2R_509[,9],chr2L_509[,9])
#peaks=read.table('peaks_082712_400_50_2_2_0_pan_509_top50.txt')
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
# plot:
pdf("Figure6B.pdf",width=3.27,height=3, title = "Figure S4",paper="special")
tcl=0.2
pdjy=2.5
pdjx=-3.0
cex1=0.5
cex2=0.6
cex3=1
par(cex.axis=cex1) #The magnification to be used for axis annotation relative to the current setting of cex.
par(cex.lab=cex1) #The magnification to be used for x and y labels relative to the current setting of cex.
par(omi=c(0,0,0,0)) #A vector of the form c(bottom, left, top, right) giving the size of the outer margins in inches.
par(mai=c(0.5,0.5,0.5,0.5)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
shift=0.02
boxplot(h, admix_mode_corr[1:150000,9], admix_bot_mode_corr[1:150000,9],admix_posterior_corr[1:150000,9],const106[1:150000,9],const22106[1:150000,9], dadi1[1:150000,9],dadi2[1:150000,9], ylim=c(0,1), col=c('black','orange','purple','brown','forestgreen','blue','red','gold'), pch=20, cex=0.4, border=c('black','orange','purple','brown','forestgreen','blue','red','gold'), xaxs="i",yaxs = "i", xlab="",ylab="", axes=F )
#points(rep(1,50), peaks[,28], col='red', pch=20, cex=0.7)
axis(1,at=c(0,8),labels=F,tcl=0)
axis(2,at=c(0,1),labels=F,tcl=0)
axis(2,at=seq(0,1,by=0.05),labels=seq(0,1,by=0.05),padj=pdjy,tcl=-tcl)
axis(3,at=c(0,8),labels=F,tcl=0)
axis(4,at=c(0,1),labels=F,tcl=0)
mtext(expression("H12"),side=2,cex=cex2,padj=-2.25)
dev.off()
###############
# Zoom in #
###############
boxplot(h, admix_mode_corr[1:150000,9], admix_bot_mode_corr[1:150000,9], admix_posterior_corr[1:150000,9], ylim=c(0,0.1), col=c('grey','orange','purple','brown'), ylab='H12', names=c('data','admix_mode','admix_bot_mode','admix_95CI'), main='10KB windows, Zoom in')
######################################
# plot the H12 scan in 10kb windows #
######################################
setwd('/Users/nanditagarud/Documents/Jensen_response/analysis/data_10kb_scan')
# recombination >=5*10^-9
chr3R_509 = read.table('H12_scan_10kb_chr3R_rho5e-9removed.txt')
chr3L_509 = read.table('H12_scan_10kb_chr3L_rho5e-9removed.txt')
chr2L_509 = read.table('H12_scan_10kb_chr2L_rho5e-9removed.txt')
chr2R_509 = read.table('H12_scan_10kb_chr2R_rho5e-9removed.txt')
# recombination <5*10^-9
chr3R_509_low = read.table('H12_scan_10kb_chr3R_rho5e-9_only.txt')
chr3L_509_low = read.table('H12_scan_10kb_chr3L_rho5e-9_only.txt')
chr2L_509_low = read.table('H12_scan_10kb_chr2L_rho5e-9_only.txt')
chr2R_509_low = read.table('H12_scan_10kb_chr2R_rho5e-9_only.txt')
plot(chr3R_509_low[,1], chr3R_509_low[,9], col='grey', pch=20, xlab='position', ylab='H12 measured in 10kb windows', main='Chr3R')
points(chr3R_509[,1], chr3R_509[,9], pch=20 )
top_candidates=c(560, 3344, 14136, 16974)
points(chr3R_509[top_candidates,1], chr3R_509[top_candidates,9], pch=20, col='red' )
plot(chr3L_509_low[,1], chr3L_509_low[,9], col='grey', pch=20, xlab='position', ylab='H12 measured in 10kb windows', main='Chr3L')
top_candidates=c(4060, 5201, 11537)
points(chr3L_509[,1], chr3L_509[,9], pch=20 )
points(chr3L_509[top_candidates,1], chr3L_509[top_candidates,9], pch=20, col='red' )
plot(chr2R_509_low[,1], chr2R_509_low[,9], col='grey', pch=20, xlab='position', ylab='H12 measured in 10kb windows', main='Chr2R')
top_candidates=c(3188, 6236, 9991,14663)
points(chr2R_509[,1], chr2R_509[,9], pch=20 )
points(chr2R_509[top_candidates,1], chr2R_509[top_candidates,9], pch=20, col='red' )
plot(chr2L_509_low[,1], chr2L_509_low[,9], col='grey', pch=20, xlab='position', ylab='H12 measured in 10kb windows', main='Chr2L')
top_candidates=c(5227,6619,7812,13251,14834)
points(chr2L_509[,1], chr2L_509[,9], pch=20 )
points(chr2L_509[top_candidates,1], chr2L_509[top_candidates,9], pch=20, col='red' )
#######################################################
# plot H12 values vs num SNPs in 10KB window for data #
#######################################################
plot(chr3R_509[,12], chr3R_509[,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='Chr 3R')
plot(chr3L_509[,12], chr3L_509[,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='Chr 3L')
plot(chr2R_509[,12], chr2R_509[,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='Chr 2R')
plot(chr2L_509[,12], chr2L_509[,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='Chr 2L')
##############################################################
# plot H12 values vs num SNPs in 10KB window for simulations #
##############################################################
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
admix_posterior_neutral=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0_selection_False_bps.txt')
admix_posterior_HS=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0.01_selection_True_bps.txt')
admix_posterior_SS=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_10_selection_True_bps.txt')
plot(admix_posterior_neutral[1:150000,12], admix_posterior_neutral[1:150000,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='neutrality', ylim=c(0,1))
plot(admix_posterior_HS[1:150000,12], admix_posterior_HS[1:150000,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='HS')
plot(admix_posterior_SS[1:150000,12], admix_posterior_SS[1:150000,9], xlab='Number of SNPs in 10Kb window', ylab='H12', main='SS')
##########################################################
# Compute mean pi and S for different demographic models #
##########################################################
setwd('/Users/nanditagarud/Documents/Jensen_response/analysis')
admix_posterior_Pi_S=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0_selection_False_Pi_S_bps.txt')
admix_posterior_noSingletons_Pi_S=read.table('Admixture_posteriorDistn_95CI_rho_5e-9_theta_0_selection_False_Pi_S_noSingletons.txt')
admix_posterior_corrected_Pi_S=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_Pi_S_TajD_bps.txt')
admix_posterior_corrected_noSingletons_Pi_S=read.table('Admixture_posteriorDistn_95CI_corrected_rho_5e-9_theta_0_selection_False_Pi_S_TajD_noSingletons.txt')
admix_posterior_outside_Pi_S=read.table('Admixture_posteriorDistn_outside_95CI_rho_5e-9_theta_0_selection_False_Pi_S_bps.txt')
admix_posterior_outside_noSingletons_Pi_S=read.table('Admixture_posteriorDistn_outside_95CI_rho_5e-9_theta_0_selection_False_Pi_S_noSingletons.txt')
admix_uniform_Pi_S=read.table('Admixture_uniformPrior_rho_5e-9_theta_0_selection_False_Pi_S_bps.txt') #something wrong with model
admix_uniform_noSingletons_Pi_S=read.table('Admixture_uniformPrior_rho_5e-9_theta_0_selection_False_Pi_S_noSingletons.txt')
admix_bot_mode_Pi_S=read.table('admixture_bottleneck_mode_neutrality.txt_Pi_S.txt')
admix_bot_mode_noSingletons_Pi_S=read.table('admixture_bottleneck_mode_neutrality.txt_Pi_S_noSingletons.txt') #fix
admix_bot_mode_corrected_Pi_S=read.table('admixture_bottleneck_mode_corrected_neutrality.txt_Pi_S_TajD.txt')
admix_bot_mode_corrected_no_Singletons_Pi_S=read.table('admixture_bottleneck_mode_corrected_neutrality.txt_Pi_S_TajD_noSingletons.txt')
admix_mode_Pi_S=read.table('Admixture_mode_rho_5e-9_theta_0_selection_False_Pi_S.txt')
admix_mode_noSingletons_Pi_S=read.table('Admixture_mode_rho_5e-9_theta_0_selection_False_Pi_S_noSingletons.txt')
admix_mode_DuchenRhoTheta_Pi_S=read.table('Admixture_mode_rho_5e-9_theta_0_selection_False_Pi_S_Singletons_DuchenRhoTheta.txt')
admix_mode_DuchenRhoTheta_noSingletons_Pi_S=read.table('Admixture_mode_rho_5e-9_theta_0_selection_False_Pi_S_noSingletons_DuchenRhoTheta.txt')
admix_mode_corrected_Pi_S=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_Pi_S_TajD.txt')
admix_mode_corrected_noSingletons_Pi_S=read.table('Admixture_mode_corrected_rho_5e-9_theta_0_selection_False_Pi_S_TajD_noSingletons.txt')
const106_Pi_S=read.table('constNe106_neutrality_Pi_S.txt')
const22106_Pi_S=read.table('constNe2.2e6_neutrality_Pi_S.txt')
dadi1_Pi_S=read.table('dadi1_neutrality_Pi_S.txt')
dadi2_Pi_S=read.table('dadi2_neutrality_Pi_S.txt')
dadi1_recoded_Pi_S=read.table('dadi1_recoded_neutrality_Pi_S_TajD.txt')
dadi2_recoded_Pi_S=read.table('dadi2_neutrality_recoded_Pi_S_TajD.txt')
mean(admix_posterior_Pi_S[,1])
##########################################################################################
# plot distribution of Pi, S, TajD for the 242 fragments simulated from the X chromosome #
##########################################################################################
setwd('/Users/nanditagarud/Documents/Jensen_response/analysis')
X_chr_noSingletons=read.table('Admixture_95CI_rho_5e-9_theta_0_selection_False_DuchenRhoTheta_exactLength_Xchr_Pi_S_TajD_noSingletons_DuchenRhoTheta.txt')
X_chr_Singletons=read.table('Admixture_95CI_rho_5e-9_theta_0_selection_False_DuchenRhoTheta_exactLength_Xchr_Pi_S_TajD_Singletons_DuchenRhoTheta.txt')
X_chr_fullPosterior_noSingletons=read.table('Admixture_fullPosterior_rho_5e-9_theta_0_selection_False_DuchenRhoTheta_exactLength_Xchr_Pi_S_TajD_noSingletons_DuchenRhoTheta.txt')
X_chr_fullPosterior_Singletons=read.table('Admixture_fullPosterior_rho_5e-9_theta_0_selection_False_DuchenRhoTheta_exactLength_Xchr_Pi_S_TajD_Singletons_DuchenRhoTheta.txt')
boxplot(X_chr_noSingletons, names=c('S_n','Pi_n', 'average pairwise distance','TajD'), main='X chr, 242 fragments, 95CI, no singletons')
boxplot(X_chr_fullPosterior_noSingletons, names=c('S_n','Pi_n', 'average pairwise distance','TajD'), main='X chr, 242 fragments, full posterior distribution, no singletons')
boxplot(X_chr_Singletons, names=c('S_n','Pi_n', 'average pairwise distance','TajD'), main='X chr, 242 fragments, 95CI, singletons')
boxplot(X_chr_fullPosterior_Singletons, names=c('S_n','Pi_n', 'average pairwise distance','TajD'), main='X chr, 242 fragments, full posterior distribution, singletons')
i= X_chr_Singletons[,1]<=791
boxplot(X_chr_Singletons[i,], names=c('S_n','Pi_n', 'TajD'))
#####################################################
# BF heat map #
# Red and Grey scale, truncated x axis, 3 panels #
# Panel 1: Original plot, SNP based #
# Panel 2: More comprehensive dem model, SNP based #
# Panel 3: More comprehensive dem model, bp based #
#####################################################
setwd("/Users/nanditagarud/Documents/Jensen_response/analysis")
peaks=read.table('peaks_082712_400_50_2_2_0_pan_509_top50_rhoForCalling.txt')
pdf("fig8_BFheatmap_panels.pdf",width=6.83, height=2.43, title = "Figure 2",paper="special")
#width=6.83,height=4.86
#width=4.86,height=2.43
s=as.vector(c(1,6))
split.screen(s)
change=0.06
mb1=0.1
ml1=0.4
mt1=0
mr1=0
mb2=0.1
ml2=0.35
mt2=0
mr2=0.05
mb3=0.1
ml3=0.3
mt3=0
mr3=0.1
mb4=0.1
ml4=0.25
mt4=0
mr4=0.15
mb5=0.1
ml5=0.2
mt5=0
mr5=0.2
mb6=0.1
ml6=0.15
mt6=0
mr6=0.25
tcl=0.2
pdjy=2.0
pdjx=-2.5
cex1=0.7
cex2=0.7
cex3=1
par(cex.axis=cex1) #The magnification to be used for axis annotation relative to the current setting of cex.
par(cex.lab=cex1) #The magnification to be used for x and y labels relative to the current setting of cex.
par(omi=c(0.35,0.1,0.35,0.1)) #A vector of the form c(bottom, left, top, right) giving the size of the outer margins in inches.
shift=0.02
screen(1)
par(mai=c(mb1,ml1,mt1,mr1)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
#d=read.table("Admixture_mode_fixedPopSize_NAm61659_EUr2000000_rho_5e-9_BFs_numSim_12000.txt")
d=read.table("Admixture_mode_fixedPopSize_NAm61659_EUr500000_conditionEndFreq_rho_5e-9_BFs_numSim_14000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13), ylim=c(0,41))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(H2~"/"~H1),side=2,cex=cex2,padj=-3)
mtext(expression(A),at=-0.5,padj=-0.5,cex=cex3)
screen(2)
par(mai=c(mb2,ml2,mt2,mr2)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_mode_fixedPopSize_NAm61659_EUr500000_rho_5e-9_BFs_numSim_140000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(B),at=-0.5,padj=-0.5,cex=cex3)
screen(3)
par(mai=c(mb1,ml1,mt1,mr1)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_mode_fixedPopSize_NAm1110000_EUr2000000_rho_5e-9_BFs_numSim_20000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13), ylim=c(0,41))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(H2~"/"~H1),side=2,cex=cex2,padj=-3)
mtext(expression(A),at=-0.5,padj=-0.5,cex=cex3)
screen(4)
par(mai=c(mb2,ml2,mt2,mr2)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_mode_fixedPopSize_NAm1110000_EUr500000_rho_5e-9_BFs_numSim_20000.txt")
d2=(d[,4])/(d[,3])
d3=matrix(d2,c(40,40))
d4=d3[,1:12]
colorMatrix=as.matrix(d4)
for (i in 1:length(d4[,1])){
for (j in 1:length(d4[1,])){
if (d3[i,j]=='NaN'){colorMatrix[i,j]="white"} else
{if (d3[i,j]=='Inf'){colorMatrix[i,j]="black"} else
{if (d3[i,j]>=0 && d3[i,j]<1){colorMatrix[i,j]="red"} else
{if (d3[i,j]>=1 && d3[i,j]<10){colorMatrix[i,j]="gray65"} else
{if (d3[i,j]>=10 && d3[i,j]<30){colorMatrix[i,j]="gray55"} else
{if (d3[i,j]>=30 && d3[i,j]<100){colorMatrix[i,j]="gray35"} else
{if (d3[i,j]>=100 && d3[i,j]<1000000){colorMatrix[i,j]="black"} else
{colorMatrix[i,j]=3}}}}}}}
}
}
plot(c(0, 12), c(0, 40), type= "n", xaxs="i",yaxs = "i",axes=F, main='', xlim=c(0,13))
h=0
w=0.35
for (i in 1:length(d4[,1])){
height=c(h-0.5, h+0.5)
for (j in 1:length(d4[1,])){
rect(j-0.5, height[1], j+0.5, height[2],density=-1,col=colorMatrix[i,j],lwd=0., border=colorMatrix[i,j])
}
h=h+1
}
axis(1,at=c(0,12),labels=F,tcl=0)
axis(1, at = c(0,6,12), labels=c("0","0.15","0.30"), padj=pdjx,tcl=-tcl)
axis(2,at=c(0,40),labels=F,tcl=0)
axis(2, at = c(0,10,20,30,40), labels=c("0","0.25","0.50", "0.75", "1" ),padj=pdjy,tcl=-tcl)
# overlay the observed peak's values
points(40*peaks[,28],40*peaks[,20],col='gold',pch=20, cex=0.6)
mtext(expression(B),at=-0.5,padj=-0.5,cex=cex3)
screen(5)
par(mai=c(mb1,ml1,mt1,mr1)) #A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches.
par(xaxs = "i")
par(yaxs = "i")
d=read.table("Admixture_mode_fixedPopSize_NAm15984500_EUr2000000_rho_5e-9_BFs_numSim_12000.txt")