Introduction

This file is provided as an overview of the many different TAD analyses that have gone into checking the robustness of the results presented in the paper. It includes many different types of analyses across a wide variety of different normalization and TAD calling parameters.

Juicer_Mega Analyses

These analyses represent those run on two “consensus” Hi-C maps–one for each species. These maps were built from the juicer .hic files from each individual, as described here: https://groups.google.com/forum/#!searchin/3d-genomics/mega|sort:date/3d-genomics/N95zVXHThSw/66Afq3NlBQAJ Note I had to tinker with some things in command line to get it to work for me on our cluster: https://groups.google.com/forum/#!searchin/3d-genomics/mega%7Csort:date/3d-genomics/dgqNM32cEmQ/0H0kZtd-CQAJ

#Since I only have one conact map and thus one set of TAD calls per species, this analyses precludes looking at intra-species variance. I'll get into individual level data in some of the next sections with other contact maps--here, I look at conservation of TAD boundaries and sizes within these mega maps exclusively.

#####DOMAIN ANALYSES#####
###NON-ORTHO TAD ANALYSES###
###First, with no eye to orthology whatsoever--just look at different metrics of the TADs, including number found, size, corner score distributions, genome coverage, and number of genes in the average TAD. Read-in and format of files here.
Hdomains.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.domains", header=FALSE, data.table=FALSE)
Hdomains.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.domains", header=FALSE, data.table=FALSE)
Hdomains.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.domains", header=FALSE, data.table=FALSE)
Hdomains.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.domains", header=FALSE, data.table=FALSE)
Hdomains.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.domains", header=FALSE, data.table=FALSE)
Hdomains.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.domains", header=FALSE, data.table=FALSE)

Cdomains.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.domains", header=FALSE, data.table=FALSE)
Cdomains.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.domains", header=FALSE, data.table=FALSE)
Cdomains.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.domains", header=FALSE, data.table=FALSE)
Cdomains.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.domains", header=FALSE, data.table=FALSE)
Cdomains.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.domains", header=FALSE, data.table=FALSE)
Cdomains.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.domains", header=FALSE, data.table=FALSE)

Hdomains.10$size <- Hdomains.10$V3-Hdomains.10$V2
Hdomains.10$res <- "10kb"
Hdomains.10$species <- "Human"
Hdomains.25$size <- Hdomains.25$V3-Hdomains.25$V2
Hdomains.25$res <- "25kb"
Hdomains.25$species <- "Human"
Hdomains.50$size <- Hdomains.50$V3-Hdomains.50$V2
Hdomains.50$res <- "50kb"
Hdomains.50$species <- "Human"
Hdomains.100$size <- Hdomains.100$V3-Hdomains.100$V2
Hdomains.100$res <- "100kb"
Hdomains.100$species <- "Human"
Hdomains.250$size <- Hdomains.250$V3-Hdomains.250$V2
Hdomains.250$res <- "250kb"
Hdomains.250$species <- "Human"
Hdomains.500$size <- Hdomains.500$V3-Hdomains.500$V2
Hdomains.500$res <- "500kb"
Hdomains.500$species <- "Human"

Cdomains.10$size <- Cdomains.10$V3-Cdomains.10$V2
Cdomains.10$res <- "10kb"
Cdomains.10$species <- "Chimpanzee"
Cdomains.25$size <- Cdomains.25$V3-Cdomains.25$V2
Cdomains.25$res <- "25kb"
Cdomains.25$species <- "Chimpanzee"
Cdomains.50$size <- Cdomains.50$V3-Cdomains.50$V2
Cdomains.50$res <- "50kb"
Cdomains.50$species <- "Chimpanzee"
Cdomains.100$size <- Cdomains.100$V3-Cdomains.100$V2
Cdomains.100$res <- "100kb"
Cdomains.100$species <- "Chimpanzee"
Cdomains.250$size <- Cdomains.250$V3-Cdomains.250$V2
Cdomains.250$res <- "250kb"
Cdomains.250$species <- "Chimpanzee"
Cdomains.500$size <- Cdomains.500$V3-Cdomains.500$V2
Cdomains.500$res <- "500kb"
Cdomains.500$species <- "Chimpanzee"

ggall <- rbind(Hdomains.10, Hdomains.25, Hdomains.50, Hdomains.100, Hdomains.250, Hdomains.500, Cdomains.10, Cdomains.25, Cdomains.50, Cdomains.100, Cdomains.250, Cdomains.500)
ggall$res <- factor(ggall$res, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))

###Size Analysis###
ggplot(data=ggall) + geom_boxplot(aes(x=res, y=size, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Size") + ggtitle("All Arrowhead TAD Size Distributions") + coord_cartesian(ylim=c(0, 20000000))

Past versions of “Juicer Mega Domain and Boundary Characteristics-1.png”

t.test(Hdomains.10$size, Cdomains.10$size)

    Welch Two Sample t-test

data:  Hdomains.10$size and Cdomains.10$size
t = 2.9269, df = 21794, p-value = 0.003428
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  4965.647 25100.642
sample estimates:
mean of x mean of y 
 443186.4  428153.3 

t.test(Hdomains.25$size, Cdomains.25$size)

    Welch Two Sample t-test

data:  Hdomains.25$size and Cdomains.25$size
t = 0.5607, df = 11349, p-value = 0.575
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -22531.5  40585.8
sample estimates:
mean of x mean of y 
 926906.2  917879.0 

t.test(Hdomains.50$size, Cdomains.50$size)

    Welch Two Sample t-test

data:  Hdomains.50$size and Cdomains.50$size
t = 0.36018, df = 5307.5, p-value = 0.7187
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -77942.9 113029.5
sample estimates:
mean of x mean of y 
  1776627   1759084 

t.test(Hdomains.100$size, Cdomains.100$size)

    Welch Two Sample t-test

data:  Hdomains.100$size and Cdomains.100$size
t = 1.9274, df = 2313.1, p-value = 0.05405
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  -5392.276 623834.083
sample estimates:
mean of x mean of y 
  3564390   3255169 

t.test(Hdomains.250$size, Cdomains.250$size)

    Welch Two Sample t-test

data:  Hdomains.250$size and Cdomains.250$size
t = 1.9112, df = 714.42, p-value = 0.05638
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  -17309.95 1287474.55
sample estimates:
mean of x mean of y 
  6842545   6207463 

t.test(Hdomains.500$size, Cdomains.500$size)

    Welch Two Sample t-test

data:  Hdomains.500$size and Cdomains.500$size
t = 0.41104, df = 167.88, p-value = 0.6816
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1629847  2486990
sample estimates:
mean of x mean of y 
 12535714  12107143 

###Confidence Analysis###
ggplot(data=ggall) + geom_boxplot(aes(x=res, y=V4, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Confidence Score") + ggtitle("All Arrowhead TAD Scores")

Past versions of “Juicer Mega Domain and Boundary Characteristics-2.png”

t.test(Hdomains.10$V4, Cdomains.10$V4)

    Welch Two Sample t-test

data:  Hdomains.10$V4 and Cdomains.10$V4
t = 5.6183, df = 21658, p-value = 1.952e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.01392119 0.02883894
sample estimates:
mean of x mean of y 
 1.148853  1.127473 

t.test(Hdomains.25$V4, Cdomains.25$V4)

    Welch Two Sample t-test

data:  Hdomains.25$V4 and Cdomains.25$V4
t = 1.2774, df = 11334, p-value = 0.2015
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.003574401  0.016949601
sample estimates:
mean of x mean of y 
 1.101157  1.094469 

t.test(Hdomains.50$V4, Cdomains.50$V4)

    Welch Two Sample t-test

data:  Hdomains.50$V4 and Cdomains.50$V4
t = 0.61539, df = 5296.7, p-value = 0.5383
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.0105161  0.0201389
sample estimates:
mean of x mean of y 
 1.103984  1.099172 

t.test(Hdomains.100$V4, Cdomains.100$V4)

    Welch Two Sample t-test

data:  Hdomains.100$V4 and Cdomains.100$V4
t = -2.2383, df = 2319.1, p-value = 0.0253
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.047531894 -0.003139009
sample estimates:
mean of x mean of y 
 1.095716  1.121052 

t.test(Hdomains.250$V4, Cdomains.250$V4)

    Welch Two Sample t-test

data:  Hdomains.250$V4 and Cdomains.250$V4
t = -0.98183, df = 699.11, p-value = 0.3265
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.05813996  0.01937602
sample estimates:
mean of x mean of y 
 1.190401  1.209783 

t.test(Hdomains.500$V4, Cdomains.500$V4)

    Welch Two Sample t-test

data:  Hdomains.500$V4 and Cdomains.500$V4
t = -2.5423, df = 174.03, p-value = 0.01188
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.16546639 -0.02083365
sample estimates:
mean of x mean of y 
 1.252993  1.346143 

###Total domains found analysis###
ggfound <- data.frame(species=c(rep("Human", 6), rep("Chimpanzee", 6)), resolution=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"), TADs.found=c(nrow(Hdomains.10), nrow(Hdomains.25), nrow(Hdomains.50), nrow(Hdomains.100), nrow(Hdomains.250), nrow(Hdomains.500), nrow(Cdomains.10), nrow(Cdomains.25), nrow(Cdomains.50), nrow(Cdomains.100), nrow(Cdomains.250), nrow(Cdomains.500)))
ggfound$resolution <- factor(ggfound$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=ggfound) + geom_line(aes(x=resolution, y=TADs.found, group=species, color=species)) + xlab("Resolution of Analysis") + ylab("Total # TADs Discovered") + ggtitle("All TAD Inferences Across Species and Resolutions")

Past versions of “Juicer Mega Domain and Boundary Characteristics-3.png”

###Genome coverage analysis
Hcov.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.genome.coverage", header=FALSE, data.table=FALSE)

Ccov.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.genome.coverage", header=FALSE, data.table=FALSE)

Hcov.10$species <- Hcov.25$species <- Hcov.50$species <- Hcov.100$species <- Hcov.250$species <-  Hcov.500$species <- "Human"
Ccov.10$species <- Ccov.25$species <- Ccov.50$species <- Ccov.100$species <- Ccov.250$species <- Ccov.500$species <- "Chimpanzee"

Hcov.10$resolution <- "10kb"
Hcov.25$resolution <- "25kb"
Hcov.50$resolution <- "50kb"
Hcov.100$resolution <- "100kb"
Hcov.250$resolution <- "250kb"
Hcov.500$resolution <- "500kb"

Ccov.10$resolution <- "10kb"
Ccov.25$resolution <- "25kb"
Ccov.50$resolution <- "50kb"
Ccov.100$resolution <- "100kb"
Ccov.250$resolution <- "250kb"
Ccov.500$resolution <- "500kb"

ggallcov <- rbind(Hcov.10, Hcov.25, Hcov.50, Hcov.100, Hcov.250, Hcov.500, Ccov.10, Ccov.25, Ccov.50, Ccov.100, Ccov.250, Ccov.500) %>% filter(.,V1=="genome")
ggallcov$resolution <- factor(ggallcov$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=ggallcov, aes(x=resolution, y=V5, color=factor(V2), shape=species, group=resolution)) + geom_jitter(size=3) + coord_cartesian(ylim=c(0, 0.75)) + xlab("Resolution of Analysis") + ylab("Proportion of Genome covered by X TADs") + ggtitle("All TAD Genome Coverage") + guides(color=guide_legend(title="X TADs"))

Past versions of “Juicer Mega Domain and Boundary Characteristics-4.png”

##Gene density analysis
###Now, look at gene density of the orthologous genes w/ the orthologously mappable TADs:
Hgene.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.10$resolution <- "10kb"
Hgene.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.25$resolution <- "25kb"
Hgene.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.50$resolution <- "50kb"
Hgene.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.100$resolution <- "100kb"
Hgene.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.250$resolution <- "250kb"
Hgene.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.500$resolution <- "500kb"
Hgene.10$species <- Hgene.25$species <- Hgene.50$species <- Hgene.100$species <- Hgene.250$species <- Hgene.500$species <-  "Human"

Cgene.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.10$resolution <- "10kb"
Cgene.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.25$resolution <- "25kb"
Cgene.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.50$resolution <- "50kb"
Cgene.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.100$resolution <- "100kb"
Cgene.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.250$resolution <- "250kb"
Cgene.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.500$resolution <- "500kb"
Cgene.10$species <- Cgene.25$species <- Cgene.50$species <- Cgene.100$species <- Cgene.250$species <- Cgene.500$species <-  "Chimpanzee"

gggenes <- rbind(Hgene.10, Hgene.25, Hgene.50, Hgene.100, Hgene.250, Hgene.500, Cgene.10, Cgene.25, Cgene.50, Cgene.100, Cgene.250, Cgene.500)
gggenes$resolution <- factor(gggenes$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=gggenes) + geom_boxplot(aes(x=resolution, y=V5, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Gene Density") + ggtitle("All TAD Gene Density Across Species & Resolutions")

Past versions of “Juicer Mega Domain and Boundary Characteristics-5.png”

t.test(Hgene.10$V5, Cgene.10$V5)

    Welch Two Sample t-test

data:  Hgene.10$V5 and Cgene.10$V5
t = 0.33905, df = 21801, p-value = 0.7346
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2080205  0.2950379
sample estimates:
mean of x mean of y 
 9.070543  9.027035 

t.test(Hgene.25$V5, Cgene.25$V5)

    Welch Two Sample t-test

data:  Hgene.25$V5 and Cgene.25$V5
t = -0.30499, df = 11306, p-value = 0.7604
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.8056076  0.5886698
sample estimates:
mean of x mean of y 
 16.92772  17.03619 

t.test(Hgene.50$V5, Cgene.50$V5)

    Welch Two Sample t-test

data:  Hgene.50$V5 and Cgene.50$V5
t = -0.092275, df = 5308.9, p-value = 0.9265
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.368426  2.155488
sample estimates:
mean of x mean of y 
 32.18713  32.29360 

t.test(Hgene.100$V5, Cgene.100$V5)

    Welch Two Sample t-test

data:  Hgene.100$V5 and Cgene.100$V5
t = 0.79971, df = 2350, p-value = 0.424
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.819600  9.080354
sample estimates:
mean of x mean of y 
 60.97886  58.34848 

t.test(Hgene.250$V5, Cgene.250$V5)

    Welch Two Sample t-test

data:  Hgene.250$V5 and Cgene.250$V5
t = 0.89838, df = 721.23, p-value = 0.3693
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -7.96252 21.39760
sample estimates:
mean of x mean of y 
 115.5116  108.7940 

t.test(Hgene.500$V5, Cgene.500$V5)

    Welch Two Sample t-test

data:  Hgene.500$V5 and Cgene.500$V5
t = -0.34041, df = 167.1, p-value = 0.734
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -54.27646  38.31218
sample estimates:
mean of x mean of y 
 200.1964  208.1786 

##########################ORTHO TAD ANALYSES###
##Repeat all above on only orthologously mappable TADs--just look at different metrics of the TADs, including genome coverage, size, corner score distributions, number of genes in the average TAD, etc. Read-in and format of files here.
Hdomains.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.domains.ortho.hg38", header=FALSE, data.table=FALSE)
Hdomains.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.domains.ortho.hg38", header=FALSE, data.table=FALSE)
Hdomains.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.domains.ortho.hg38", header=FALSE, data.table=FALSE)
Hdomains.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.domains.ortho.hg38", header=FALSE, data.table=FALSE)
Hdomains.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.domains.ortho.hg38", header=FALSE, data.table=FALSE)
Hdomains.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.domains.ortho.hg38", header=FALSE, data.table=FALSE)

Cdomains.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)
Cdomains.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)
Cdomains.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)
Cdomains.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)
Cdomains.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)
Cdomains.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.domains.ortho.panTro5", header=FALSE, data.table=FALSE)

Hdomains.10$size <- Hdomains.10$V3-Hdomains.10$V2
Hdomains.10$res <- "10kb"
Hdomains.10$species <- "Human"
Hdomains.25$size <- Hdomains.25$V3-Hdomains.25$V2
Hdomains.25$res <- "25kb"
Hdomains.25$species <- "Human"
Hdomains.50$size <- Hdomains.50$V3-Hdomains.50$V2
Hdomains.50$res <- "50kb"
Hdomains.50$species <- "Human"
Hdomains.100$size <- Hdomains.100$V3-Hdomains.100$V2
Hdomains.100$res <- "100kb"
Hdomains.100$species <- "Human"
Hdomains.250$size <- Hdomains.250$V3-Hdomains.250$V2
Hdomains.250$res <- "250kb"
Hdomains.250$species <- "Human"
Hdomains.500$size <- Hdomains.500$V3-Hdomains.500$V2
Hdomains.500$res <- "500kb"
Hdomains.500$species <- "Human"

Cdomains.10$size <- Cdomains.10$V3-Cdomains.10$V2
Cdomains.10$res <- "10kb"
Cdomains.10$species <- "Chimpanzee"
Cdomains.25$size <- Cdomains.25$V3-Cdomains.25$V2
Cdomains.25$res <- "25kb"
Cdomains.25$species <- "Chimpanzee"
Cdomains.50$size <- Cdomains.50$V3-Cdomains.50$V2
Cdomains.50$res <- "50kb"
Cdomains.50$species <- "Chimpanzee"
Cdomains.100$size <- Cdomains.100$V3-Cdomains.100$V2
Cdomains.100$res <- "100kb"
Cdomains.100$species <- "Chimpanzee"
Cdomains.250$size <- Cdomains.250$V3-Cdomains.250$V2
Cdomains.250$res <- "250kb"
Cdomains.250$species <- "Chimpanzee"
Cdomains.500$size <- Cdomains.500$V3-Cdomains.500$V2
Cdomains.500$res <- "500kb"
Cdomains.500$species <- "Chimpanzee"

ggall <- rbind(Hdomains.10, Hdomains.25, Hdomains.50, Hdomains.100, Hdomains.250, Hdomains.500, Cdomains.10, Cdomains.25, Cdomains.50, Cdomains.100, Cdomains.250, Cdomains.500)
ggall$res <- factor(ggall$res, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))

#Size analysis
ggplot(data=ggall) + geom_boxplot(aes(x=res, y=size, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Size") + ggtitle("Orthologous Mappable Arrowhead TAD Size Distributions") + coord_cartesian(ylim=c(0, 20000000))

Past versions of “Juicer Mega Domain and Boundary Characteristics-6.png”

t.test(Hdomains.10$size, Cdomains.10$size)

    Welch Two Sample t-test

data:  Hdomains.10$size and Cdomains.10$size
t = 2.3852, df = 19523, p-value = 0.01708
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  2373.69 24264.99
sample estimates:
mean of x mean of y 
 456409.3  443090.0 

t.test(Hdomains.25$size, Cdomains.25$size)

    Welch Two Sample t-test

data:  Hdomains.25$size and Cdomains.25$size
t = 0.37168, df = 10374, p-value = 0.7101
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -27072.18  39740.99
sample estimates:
mean of x mean of y 
 936329.2  929994.8 

t.test(Hdomains.50$size, Cdomains.50$size)

    Welch Two Sample t-test

data:  Hdomains.50$size and Cdomains.50$size
t = 0.54743, df = 4938, p-value = 0.5841
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -71654.55 127175.81
sample estimates:
mean of x mean of y 
  1788968   1761207 

t.test(Hdomains.100$size, Cdomains.100$size)

    Welch Two Sample t-test

data:  Hdomains.100$size and Cdomains.100$size
t = 1.8487, df = 2145.4, p-value = 0.06464
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -19143.53 649150.64
sample estimates:
mean of x mean of y 
  3607589   3292586 

t.test(Hdomains.250$size, Cdomains.250$size)

    Welch Two Sample t-test

data:  Hdomains.250$size and Cdomains.250$size
t = 1.9174, df = 671.71, p-value = 0.05561
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  -15279.26 1285650.92
sample estimates:
mean of x mean of y 
  6818324   6183138 

t.test(Hdomains.500$size, Cdomains.500$size)

    Welch Two Sample t-test

data:  Hdomains.500$size and Cdomains.500$size
t = 1.4412, df = 178.94, p-value = 0.1513
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -415646.5 2666910.7
sample estimates:
mean of x mean of y 
 12384532  11258899 

###Confidence Analysis###
ggplot(data=ggall) + geom_boxplot(aes(x=res, y=V4, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Confidence Score") + ggtitle("Orthologous Mappable Arrowhead TAD Scores")

Past versions of “Juicer Mega Domain and Boundary Characteristics-7.png”

t.test(Hdomains.10$V4, Cdomains.10$V4)

    Welch Two Sample t-test

data:  Hdomains.10$V4 and Cdomains.10$V4
t = 3.8348, df = 19421, p-value = 0.0001261
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.007509233 0.023211621
sample estimates:
mean of x mean of y 
 1.157399  1.142039 

t.test(Hdomains.25$V4, Cdomains.25$V4)

    Welch Two Sample t-test

data:  Hdomains.25$V4 and Cdomains.25$V4
t = 1.8998, df = 10369, p-value = 0.05749
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.0003285956  0.0210074123
sample estimates:
mean of x mean of y 
 1.105458  1.095119 

t.test(Hdomains.50$V4, Cdomains.50$V4)

    Welch Two Sample t-test

data:  Hdomains.50$V4 and Cdomains.50$V4
t = 1.2736, df = 4928.2, p-value = 0.2029
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.005530931  0.026040737
sample estimates:
mean of x mean of y 
 1.106650  1.096395 

t.test(Hdomains.100$V4, Cdomains.100$V4)

    Welch Two Sample t-test

data:  Hdomains.100$V4 and Cdomains.100$V4
t = -1.6217, df = 2170.3, p-value = 0.105
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.04174975  0.00395475
sample estimates:
mean of x mean of y 
 1.098881  1.117778 

t.test(Hdomains.250$V4, Cdomains.250$V4)

    Welch Two Sample t-test

data:  Hdomains.250$V4 and Cdomains.250$V4
t = -0.91065, df = 663.59, p-value = 0.3628
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.05740881  0.02103036
sample estimates:
mean of x mean of y 
 1.192789  1.210978 

t.test(Hdomains.500$V4, Cdomains.500$V4)

    Welch Two Sample t-test

data:  Hdomains.500$V4 and Cdomains.500$V4
t = -2.5524, df = 163.54, p-value = 0.01161
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.16580263 -0.02116414
sample estimates:
mean of x mean of y 
 1.256474  1.349958 

###Total domains found analysis###
ggfound <- data.frame(species=c(rep("Human", 6), rep("Chimpanzee", 6)), resolution=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"), TADs.found=c(nrow(Hdomains.10), nrow(Hdomains.25), nrow(Hdomains.50), nrow(Hdomains.100), nrow(Hdomains.250), nrow(Hdomains.500), nrow(Cdomains.10), nrow(Cdomains.25), nrow(Cdomains.50), nrow(Cdomains.100), nrow(Cdomains.250), nrow(Cdomains.500)))
ggfound$resolution <- factor(ggfound$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=ggfound) + geom_line(aes(x=resolution, y=TADs.found, group=species, color=species)) + xlab("Resolution of Analysis") + ylab("Total # TADs Discovered") + ggtitle("Ortho. TAD Inferences Across Species and Resolutions")

Past versions of “Juicer Mega Domain and Boundary Characteristics-8.png”

###Genome coverage analysis
Hcov.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Hcov.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.ortho.genome.coverage", header=FALSE, data.table=FALSE)

Ccov.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.ortho.genome.coverage", header=FALSE, data.table=FALSE)
Ccov.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.ortho.genome.coverage", header=FALSE, data.table=FALSE)

Hcov.10$species <- Hcov.25$species <- Hcov.50$species <- Hcov.100$species <- Hcov.250$species <- Hcov.500$species <- "Human"
Ccov.10$species <- Ccov.25$species <- Ccov.50$species <- Ccov.100$species <- Ccov.250$species <- Ccov.500$species <- "Chimpanzee"

Hcov.10$resolution <- "10kb"
Hcov.25$resolution <- "25kb"
Hcov.50$resolution <- "50kb"
Hcov.100$resolution <- "100kb"
Hcov.250$resolution <- "250kb"
Hcov.500$resolution <- "500kb"

Ccov.10$resolution <- "10kb"
Ccov.25$resolution <- "25kb"
Ccov.50$resolution <- "50kb"
Ccov.100$resolution <- "100kb"
Ccov.250$resolution <- "250kb"
Ccov.500$resolution <- "500kb"

ggallcov <- rbind(Hcov.10, Hcov.25, Hcov.50, Hcov.100, Hcov.250, Hcov.500, Ccov.10, Ccov.25, Ccov.50, Ccov.100, Ccov.250, Ccov.500) %>% filter(.,V1=="genome")
ggallcov$resolution <- factor(ggallcov$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=ggallcov, aes(x=resolution, y=V5, color=factor(V2), shape=species, group=resolution)) + geom_jitter(size=3) + xlab("Resolution of Analysis") + ylab("Proportion of Genome covered by X TADs") + ggtitle("Orthologous Mappable TAD Genome Coverage") + guides(color=guide_legend(title="X TADs")) + coord_cartesian(ylim=c(0, 0.75))

Past versions of “Juicer Mega Domain and Boundary Characteristics-9.png”

###Now, look at gene density of the orthologous genes w/ the orthologously mappable TADs:
Hgene.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.10$resolution <- "10kb"
Hgene.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.25$resolution <- "25kb"
Hgene.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.50$resolution <- "50kb"
Hgene.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.100$resolution <- "100kb"
Hgene.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.250$resolution <- "250kb"
Hgene.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Hgene.500$resolution <- "500kb"
Hgene.10$species <- Hgene.25$species <- Hgene.50$species <- Hgene.100$species <- Hgene.250$species <- Hgene.500$species <-  "Human"

Cgene.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.10$resolution <- "10kb"
Cgene.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.25$resolution <- "25kb"
Cgene.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.50$resolution <- "50kb"
Cgene.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.100$resolution <- "100kb"
Cgene.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.250$resolution <- "250kb"
Cgene.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.ortho.gene.overlap", header=FALSE, data.table=FALSE)
Cgene.500$resolution <- "500kb"
Cgene.10$species <- Cgene.25$species <- Cgene.50$species <- Cgene.100$species <- Cgene.250$species <- Cgene.500$species <-  "Chimpanzee"

gggenes <- rbind(Hgene.10, Hgene.25, Hgene.50, Hgene.100, Hgene.250, Hgene.500, Cgene.10, Cgene.25, Cgene.50, Cgene.100, Cgene.250, Cgene.500)
gggenes$resolution <- factor(gggenes$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
ggplot(data=gggenes) + geom_boxplot(aes(x=resolution, y=V5, fill=species)) + xlab("Resolution of Analysis") + ylab("TAD Gene Density") + ggtitle("Orth. TAD Gene Density Across Species & Resolutions")

Past versions of “Juicer Mega Domain and Boundary Characteristics-10.png”

t.test(Hgene.10$V5, Cgene.10$V5)

    Welch Two Sample t-test

data:  Hgene.10$V5 and Cgene.10$V5
t = -0.27382, df = 19512, p-value = 0.7842
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3008325  0.2270825
sample estimates:
mean of x mean of y 
 9.028783  9.065658 

t.test(Hgene.25$V5, Cgene.25$V5)

    Welch Two Sample t-test

data:  Hgene.25$V5 and Cgene.25$V5
t = -0.62176, df = 10372, p-value = 0.5341
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.9120094  0.4727694
sample estimates:
mean of x mean of y 
 16.47732  16.69694 

t.test(Hgene.50$V5, Cgene.50$V5)

    Welch Two Sample t-test

data:  Hgene.50$V5 and Cgene.50$V5
t = 0.11553, df = 4933.6, p-value = 0.908
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.154351  2.424158
sample estimates:
mean of x mean of y 
 31.37312  31.23821 

t.test(Hgene.100$V5, Cgene.100$V5)

    Welch Two Sample t-test

data:  Hgene.100$V5 and Cgene.100$V5
t = 0.91265, df = 2189.6, p-value = 0.3615
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.633063  9.958384
sample estimates:
mean of x mean of y 
 60.77826  57.61560 

t.test(Hgene.250$V5, Cgene.250$V5)

    Welch Two Sample t-test

data:  Hgene.250$V5 and Cgene.250$V5
t = 1.0679, df = 673.1, p-value = 0.2859
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -6.49774 21.99371
sample estimates:
mean of x mean of y 
 113.5348  105.7868 

t.test(Hgene.500$V5, Cgene.500$V5)

    Welch Two Sample t-test

data:  Hgene.500$V5 and Cgene.500$V5
t = 0.60127, df = 174.5, p-value = 0.5484
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -26.27007  49.28930
sample estimates:
mean of x mean of y 
 196.5096  185.0000 

#####BOUNDARY ANALYSES#####
###NON-ORTHO###
###No need to bother looking at number of boundaries found or their corner score distributions--these will just be doubled-down versions of what I already made above for the TADs themselves. So here, before moving to orthologous boundaries, I look at chromosomal distribution.
Hbounds.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.10$resolution <- "10kb"
Hbounds.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.25$resolution <- "25kb"
Hbounds.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.50$resolution <- "50kb"
Hbounds.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.100$resolution <- "100kb"
Hbounds.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.250$resolution <- "250kb"
Hbounds.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.boundaries", header=FALSE, data.table=FALSE)
Hbounds.500$resolution <- "500kb"
Hbounds.10$species <- Hbounds.25$species <- Hbounds.50$species <- Hbounds.100$species <- Hbounds.250$species <- Hbounds.500$species <- "Human"

Cbounds.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.10$resolution <- "10kb"
Cbounds.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.25$resolution <- "25kb"
Cbounds.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.50$resolution <- "50kb"
Cbounds.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.100$resolution <- "100kb"
Cbounds.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.250$resolution <- "250kb"
Cbounds.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.boundaries", header=FALSE, data.table=FALSE)
Cbounds.500$resolution <- "500kb"
Cbounds.10$species <- Cbounds.25$species <- Cbounds.50$species <- Cbounds.100$species <- Cbounds.250$species <- Cbounds.500$species <- "Chimpanzee"

bounds.chromos <- rbind(Hbounds.10, Hbounds.25, Hbounds.50, Hbounds.100, Hbounds.250, Hbounds.500, Cbounds.10, Cbounds.25, Cbounds.50, Cbounds.100, Cbounds.250, Cbounds.500)
bounds.chromos$V1 <- factor(bounds.chromos$V1, levels=c("chr1", "chr2", "chr2A", "chr2B", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10", "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", "chr18", "chr19", "chr20", "chr21", "chr22", "chrX", "chrY"))
ggplot(data=bounds.chromos, aes(x=V1, fill=species)) + geom_histogram(stat="count", position="dodge") + ggtitle("Chromosomal Distribution of All TAD Boundaries") + xlab("Chromosome") + ylab("Boundary Count")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Past versions of “Juicer Mega Domain and Boundary Characteristics-11.png”

###Repeat on orthologous boundaries, then look at orthologous boundaries' conservation.
Hbounds.10 <- fread("data/TADs/Human_inter_30_KR_contact_domains/10000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.10$resolution <- "10kb"
Hbounds.25 <- fread("data/TADs/Human_inter_30_KR_contact_domains/25000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.25$resolution <- "25kb"
Hbounds.50 <- fread("data/TADs/Human_inter_30_KR_contact_domains/50000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.50$resolution <- "50kb"
Hbounds.100 <- fread("data/TADs/Human_inter_30_KR_contact_domains/100000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.100$resolution <- "100kb"
Hbounds.250 <- fread("data/TADs/Human_inter_30_KR_contact_domains/250000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.250$resolution <- "250kb"
Hbounds.500 <- fread("data/TADs/Human_inter_30_KR_contact_domains/500000.boundaries.ortho.hg38", header=FALSE, data.table=FALSE)
Hbounds.500$resolution <- "500kb"
Hbounds.10$species <- Hbounds.25$species <- Hbounds.50$species <- Hbounds.100$species <- Hbounds.250$species <- Hbounds.500$species <- "Human"

Cbounds.10 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.10$resolution <- "10kb"
Cbounds.25 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/25000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.25$resolution <- "25kb"
Cbounds.50 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/50000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.50$resolution <- "50kb"
Cbounds.100 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/100000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.100$resolution <- "100kb"
Cbounds.250 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/250000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.250$resolution <- "250kb"
Cbounds.500 <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/500000.boundaries.ortho.panTro5", header=FALSE, data.table=FALSE)
Cbounds.500$resolution <- "500kb"
Cbounds.10$species <- Cbounds.25$species <- Cbounds.50$species <- Cbounds.100$species <- Cbounds.250$species <- Cbounds.500$species <- "Chimpanzee"

bounds.chromos <- rbind(Hbounds.10, Hbounds.25, Hbounds.50, Hbounds.100, Hbounds.250, Hbounds.500, Cbounds.10, Cbounds.25, Cbounds.50, Cbounds.100, Cbounds.250, Cbounds.500)
bounds.chromos$V1 <- factor(bounds.chromos$V1, levels=c("chr1", "chr2", "chr2A", "chr2B", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10", "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", "chr18", "chr19", "chr20", "chr21", "chr22", "chrX", "chrY"))
ggplot(data=bounds.chromos, aes(x=V1, fill=species)) + geom_histogram(stat="count", position="dodge") + ggtitle("Chromosomal Distribution of Ortho. TAD Boundaries") + xlab("Chromosome") + ylab("Boundary Count")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Past versions of “Juicer Mega Domain and Boundary Characteristics-12.png”

Interspecies TAD Boundary Conservation on Juicer Merged Data

###Interspecies TAD boundary overlap with bedtools merged files###

#A function to examine overlap of TAD boundaries. This is done by first merging boundary files within each species (using bedtools merge, and in order to subset down to the number of actual distinct boundaries). Since Arrowhead can call nested TADs, I merge here in essence to eliminate boundaries being identified repeatedly due to multiple domains having the same or overlapping boundaries. I then add a column to both species' merged boundary files indicating the species identifier, combine these two files, and run a bedtools merge again, collapsing the column with species identifiers to determine how many boundaries are actually shared between the species.
#This is for running on the output of boundary.mega.merger.intersect.sh
bounder <- function(resolution, species="H"){
  if(species=="H"){
  variable <- fread(paste("data/TADs/overlaps/", resolution, ".hg38.final.merged", sep=""), header=FALSE, data.table=FALSE)}
  if(species=="C"){
  variable <- fread(paste("data/TADs/overlaps/", resolution, ".panTro5.final.merged", sep=""), header=FALSE, data.table=FALSE)
  }
  h.only <- sum(variable$V4=="Human")
  c.only <- sum(variable$V4=="Chimp")
  shared <- nrow(variable) - h.only - c.only
  weird <- sum(variable$V4!="Human"&variable$V4!="Chimp"&variable$V4!="Human,Chimp"&variable$V4!="Chimp,Human") #Just checking to get a sense of how many of these cases there are, where boundaries overlapping each other will end up being extended due to merging and overlapping across multiple boundaries. These are still counted as conserved in this analysis though, since I calculated shared above merely by subtracting the number of human-only and chimp-only boundaries.
  print(weird) #Just print them out for edification.
  myvec <- c(shared, h.only, c.only, resolution, weird)
  return(myvec)
}
options(scipen=999)
bounds.5 <- bounder(5000)

[1] 1108

bounds.10 <- bounder(10000)

[1] 1424

bounds.25 <- bounder(25000)

[1] 372

bounds.50 <- bounder(50000)

[1] 1

bounds.100 <- bounder(100000)

[1] 0

bounds.250 <- bounder(250000)

[1] 0

bounds.500 <- bounder(500000)

[1] 0

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))

Past versions of “Juicer Mega Interspecies Boundary Conservation-1.png”

#FIGS13B

###Interspecies TAD boundary overlap with bedtools -c###

#Now, I also show an analysis where I do not do any merging of the boundaries at all, for the sake of robustness. Here, instead of merging boundary files, I reciprocally use bedtools intersect -c on each file. The resultant files will list all the boundaries found as orthologously mappable across species in the first several columns, with the number of boundaries it overlapped (by any amount) in the other file in the 5th column. This counts each individual TAD's boundaries as unique, even if they have overlap. In this case, the number of "shared" boundaries may be different between the files output from each species, since I am checking different sets' overlaps against each other and one set may contain many adjacent/overlapping boundaries that overlap one boundary in the other. Hence, I merely chose whichever "shared" number is larger between the two species, to try to be conservative towards calling conservation. This is done on the output of the mega.bounds.intersect.c.sh file.
#Function to assess the output properly.
bounder.c <- function(resolution, species="H"){
  if(species=="H"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.H2C.hg38", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.C2H.hg38", sep=""))
  }
  if(species=="C"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.H2C.panTro5", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.C2H.panTro5", sep=""))
  }
  h.only <- sum(dataframe.H$V5==0)
  c.only <- sum(dataframe.C$V5==0)
  shared <- max((nrow(dataframe.H)-h.only), (nrow(dataframe.C)-c.only))
  myvec <- c(shared, h.only, c.only, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

options(scipen=999)
bounds.5 <- bounder.c(5000)
bounds.10 <- bounder.c(10000)
bounds.25 <- bounder.c(25000)
bounds.50 <- bounder.c(50000)
bounds.100 <- bounder.c(100000)
bounds.250 <- bounder.c(250000)
bounds.500 <- bounder.c(500000)

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")

#For paper
theme_set(theme_cowplot(font_size=20))
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))

Past versions of “Juicer Mega Interspecies Boundary Conservation-2.png”

#FIG4B


###Interspecies TAD boundary Rao Style Overlaps###

#Now, for one last check on boundaries, do it with the Rao overlap style for assessment of domain conservation.
#Rao-style overlapper for boundaries instead of domains (50kb is too large). The boundary elements are set to 15kb in size, and 50kb was used for median domain sizes of 185kb, so an appropriate approximate similar leniency would be 4 kb here. We'll try rounding to 5 and include a parameter for changing it to see how it affects it. The reality is that this shows much lower conservation than my other boundary conservation metrics because it is built for domain conservation and requires a certain amount of overlap for the boundaries to be considered conserved (whereas my prior analyses called any overlap as conserved). This function works on the output of the mega.bounds.rao.sh processing file.
rao.bounds.overlapper <- function(resolution, leniency=5000, mega=TRUE, species="H"){
  if(species=="H"){
  df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.HC.closest.hg38", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.CH.closest.hg38", sep=""), data.table=F, header=F)
  }
  if(species=="C"){
    df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.HC.closest.panTro5", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.CH.closest.panTro5", sep=""), data.table=F, header=F)
  }
  df.h$size <- df.h$V3-df.h$V2
  df.c$size <- df.c$V3-df.c$V2
  df.h$dist_max <- ifelse((df.h$size*.5)<=leniency, df.h$size*.5, leniency)
  df.c$dist_max <- ifelse((df.c$size*.5)<=leniency, df.c$size*.5, leniency)
  if(mega==FALSE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V5)^2+(df.h$V3-df.h$V6)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V5)^2+(df.c$V3-df.c$V6)^2)^0.5)<=df.c$dist_max, "yes", "no")}
  if(mega==TRUE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V6)^2+(df.h$V3-df.h$V7)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V6)^2+(df.c$V3-df.c$V7)^2)^0.5)<=df.c$dist_max, "yes", "no")
  }
  df.h$ID <- paste(df.h$V1, df.h$V2, df.h$V3, sep="_")
  df.c$ID <- paste(df.c$V1, df.c$V2, df.c$V3, sep="_")
  cons.total.h <- length(unique(filter(df.h, conserved=="yes")$ID))
  cons.total.c <- length(unique(filter(df.c, conserved=="yes")$ID))
  ourcons <- max(as.numeric(cons.total.h), as.numeric(cons.total.c))
  myvec <- c(ourcons, length(unique(df.h$ID))-ourcons, length(unique(df.c$ID))-ourcons, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

options(scipen=999)
bounds.5 <- rao.bounds.overlapper(5000) #This is the only case where cons.H!=cons.C, just go with cons.H to inflate proportion conserved (it's more)
bounds.10 <- rao.bounds.overlapper(10000)
bounds.25 <- rao.bounds.overlapper(25000)
bounds.50 <- rao.bounds.overlapper(50000)
bounds.100 <- rao.bounds.overlapper(100000)
bounds.250 <- rao.bounds.overlapper(250000)
bounds.500 <- rao.bounds.overlapper(500000)

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))

Past versions of “Juicer Mega Interspecies Boundary Conservation-3.png”

Interspecies TAD Body Conservation on Juicer Merged Data

###Interspecies Domain Conservation using Rao et al. Method###
#First, define a function to call domain conservation as was performed in Rao et al. 2014.
#50kb is the leniency used by Rao et al for interspecies comparisons of domains, 0.5*|i-j| was also used for interspecies comparison (as opposed to 0.2*|i-j| for the cell types within human comparison), under the reasoning that we should be somewhat more permissive with flexibility of calling conservation allowing for errors in liftOver. This function works on the output of the files processed by mega.domains.rao.sh
rao.domain.overlapper <- function(resolution, mega=TRUE, species="H", leniency=50000, paper=FALSE){
  if(species=="H"){
  df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".HC.closest.hg38", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".CH.closest.hg38", sep=""), data.table=F, header=F)
  }
  if(species=="C"){
    df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".HC.closest.panTro5", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".CH.closest.panTro5", sep=""), data.table=F, header=F)
  }
  df.h$size <- df.h$V3-df.h$V2
  df.c$size <- df.c$V3-df.c$V2
  df.h$dist_max <- ifelse((df.h$size*.5)<=leniency, df.h$size*.5, leniency)
  df.c$dist_max <- ifelse((df.c$size*.5)<=leniency, df.c$size*.5, leniency)
  if(mega==FALSE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V5)^2+(df.h$V3-df.h$V6)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V5)^2+(df.c$V3-df.c$V6)^2)^0.5)<=df.c$dist_max, "yes", "no")}
  if(mega==TRUE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V6)^2+(df.h$V3-df.h$V7)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V6)^2+(df.c$V3-df.c$V7)^2)^0.5)<=df.c$dist_max, "yes", "no")
  }
  df.h$ID <- paste(df.h$V1, df.h$V2, df.h$V3, sep="_")
  df.c$ID <- paste(df.c$V1, df.c$V2, df.c$V3, sep="_")
  cons.total.h <- length(unique(filter(df.h, conserved=="yes")$ID))
  cons.total.c <- length(unique(filter(df.c, conserved=="yes")$ID))
  if(cons.total.h!=cons.total.c){print(paste("conservation estimates different b/t species, human=", cons.total.h, " chimp=", cons.total.c, sep=""))}
  ourcons <- max(as.numeric(cons.total.h), as.numeric(cons.total.c))
  myvec <- c(ourcons, length(unique(df.h$ID))-ourcons, length(unique(df.c$ID))-ourcons, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  if(paper==TRUE){
    return(list(df.h, df.c))
  }
  else{
  return(myvec)}
}

###For writing out table S13:
domains.10 <- rao.domain.overlapper(10000, paper=TRUE)
myH <- domains.10[[1]]
colnames(myH) <- c("Hchr", "Hstart", "Hend", "Hscore", "Cchr", "Cstart", "Cend", "Cscore", "size", "dist_max", "conserved", "unique_ID")
myH <- select(myH, Hchr, Hstart, Hend, Cchr, Cstart, Cend, conserved, unique_ID)
myH$species.file <- "Human"
myC <- domains.10[[2]]
colnames(myC) <- c("Cchr", "Cstart", "Cend", "Cscore", "Hchr", "Hstart", "Hend", "Hscore", "size", "dist_max", "conserved", "unique_ID")
myC <- select(myC, Hchr, Hstart, Hend, Cchr, Cstart, Cend, conserved, unique_ID)
myC$species.file <- "Chimpanzee"

test <- fread("~/Desktop/Hi-C/2019TAD/Human_inter_30_KR_contact_domains/10000.domains.ortho.hg38")
colnames(test) <- c("Hchr", "Hstart", "Hend", "score")
test2 <- fread("~/Desktop/Hi-C/2019TAD/Human_inter_30_KR_contact_domains/10000.domains.ortho.panTro5")
colnames(test2) <- c("Cchr", "Cstart", "Cend", "Cscore")
test <- test[,-4]
test2 <- test2[,-4]

S13 <- cbind(test, test2)
S13$disc_species <- "Human"

test <- fread("~/Desktop/Hi-C/2019TAD/Chimp_inter_30_KR_contact_domains/10000.domains.ortho.hg38")
colnames(test) <- c("Hchr", "Hstart", "Hend", "score")
test2 <- fread("~/Desktop/Hi-C/2019TAD/Chimp_inter_30_KR_contact_domains/10000.domains.ortho.panTro5")
colnames(test2) <- c("Cchr", "Cstart", "Cend", "Cscore")
test <- test[,-4]
test2 <- test2[,-4]
test2$disc_species <- "Chimp"
S13.sub <- cbind(test, test2)

S13.final <- rbind(S13, S13.sub)
#fwrite(S13.final, "~/Desktop/Paper Drafts/PLOS/Revision/Revision_2/Final?/FINAL/supptables/S13 Table.txt", quote=F, sep="\t")
#group_by(myH, ID) %>% summarise(., Hchr=unique(Hchr), Hstart=unique(Hstart), Hend=unique(Hend), Cchr=unique(Cchr), Cstart=unique(Cstart), Cend=unique(Cend), conserved=paste(conserved, collapse=","))
####


domains.5 <- rao.domain.overlapper(5000)

[1] "conservation estimates different b/t species, human=6673 chimp=6688"

domains.10 <- rao.domain.overlapper(10000)
domains.25 <- rao.domain.overlapper(25000)
domains.50 <- rao.domain.overlapper(50000)
domains.100 <- rao.domain.overlapper(100000)
domains.250 <- rao.domain.overlapper(250000)
domains.500 <- rao.domain.overlapper(500000)



mydomains <- as.data.frame(rbind(domains.10, domains.25, domains.50, domains.100, domains.250, domains.500))
colnames(mydomains) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mydomains$Resolution <- (mydomains$Resolution)/1000
mydomains$Resolution <- paste(mydomains$Resolution, "kb", sep="")
mydomains$totals <- mydomains$Shared + mydomains$Human + mydomains$Chimpanzee
mydomains$shared.perc <- mydomains$Shared/mydomains$totals
mydomains$human.perc <- mydomains$Human/mydomains$totals
mydomains$chimp.perc <- mydomains$Chimpanzee/mydomains$totals
ggdomains <- melt(mydomains[,1:4])

Using Resolution as id variables

ggdomains$Resolution <- factor(ggdomains$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggdomains) <- c("Resolution", "Species", "count")
ggplot(data=ggdomains) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Interspecies TAD Domain Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))

Past versions of “Juicer Mega Interspecies Domain Conservation-1.png”

#FIGS13A

#This method was likely the most robust way to define domain conservation, particularly with nested domains.

###Interspecies Domain Conservation using bedtools -c###
#The nested nature means that a bedtools merge analytic paradigm like that used at some points for boundaries above would definitely not be appropriate, so here, I also test what happens when using a reciprocal bedtools -c approach of the domains. I also utilized -f 0.9 -r in the bedtools -c call, meaning that a domain will only be called as found in the other file if 90% of it is covered by a domain in the other file, and that 90% of that domain is also covered in the original file. This function works on the output of mega.domains.bedtoolsc.sh
domain.conserved.c <- function(resolution, species="H"){
  if(species=="H"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".HC.bedtoolsc.hg38", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".CH.bedtoolsc.hg38", sep=""))
  }
  if(species=="C"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".HC.bedtoolsc.panTro5", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".CH.bedtoolsc.panTro5", sep=""))
  }
  h.only <- sum(dataframe.H$V5==0)
  c.only <- sum(dataframe.C$V5==0)
  shared <- max((nrow(dataframe.H)-h.only), (nrow(dataframe.C)-c.only)) #Take the max to inflate conservation
  myvec <- c(shared, h.only, c.only, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

domains.5 <- domain.conserved.c(5000)
domains.10 <- domain.conserved.c(10000)
domains.25 <- domain.conserved.c(25000)
domains.50 <- domain.conserved.c(50000)
domains.100 <- domain.conserved.c(100000)
domains.250 <- domain.conserved.c(250000)
domains.500 <- domain.conserved.c(500000)

mydomains <- as.data.frame(rbind(domains.10, domains.25, domains.50, domains.100, domains.250, domains.500))
colnames(mydomains) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mydomains$Resolution <- (mydomains$Resolution)/1000
mydomains$Resolution <- paste(mydomains$Resolution, "kb", sep="")
mydomains$totals <- mydomains$Shared + mydomains$Human + mydomains$Chimpanzee
mydomains$shared.perc <- mydomains$Shared/mydomains$totals
mydomains$human.perc <- mydomains$Human/mydomains$totals
mydomains$chimp.perc <- mydomains$Chimpanzee/mydomains$totals
ggdomains <- melt(mydomains[,1:4])

Using Resolution as id variables

ggdomains$Resolution <- factor(ggdomains$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggdomains) <- c("Resolution", "Species", "count")

#For the sake of the paper:
theme_set(theme_cowplot(font_size=20))
ggplot(data=ggdomains) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Interspecies TAD Domain Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))#+ geom_text()#Need to add percentages here

Past versions of “Juicer Mega Interspecies Domain Conservation-2.png”

#FIG4A

Inter- and intra-species TAD Boundary Variance on Individual-level Arrowhead Inferences

###Intraspecies Variance in TAD Boundaries###
#First, I look at within-species variance of TAD boundaries, both on the full set of boundaries and on the set that could be orthologously lifted over between species. This is done by taking the boundary files from both these situations across all individuals within a species, adding a column to identify the individual it came from, appending these files onto one another, and then using bedtools merge and collapsing on the identifier column to assess how many unique boundaries are found and how many individuals each is found in.

#Define a function to give back how many boundaries are found in X# individuals within a species, for the orthologously mappable TADs' boundaries.
intra.ortho.assess <- function(resolution, species){
  if(species=="H"){
    df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/h.final.allmerge.ortho.hg38", sep=""), data.table=F, header=F)
    df$F <- df$E <- df$B <- df$A <- 0
    df$A[grep("A", df$V4)] <- 1
    df$B[grep("B", df$V4)] <- 1
    df$E[grep("E", df$V4)] <- 1
    df$F[grep("F", df$V4)] <- 1
  }
  if(species=="C"){
    df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/c.final.allmerge.ortho.hg38", sep=""), data.table=F, header=F)
    df$H <- df$G <- df$D <- df$C <- 0
    df$H[grep("H", df$V4)] <- 1
    df$G[grep("G", df$V4)] <- 1
    df$D[grep("D", df$V4)] <- 1
    df$C[grep("C", df$V4)] <- 1
  }
  df$indi.found <- rowSums(df[,5:8])
  mydf <- as.data.frame(melt(table(df$indi.found)))
  mydf$resolution <- paste(resolution/1000, "kb", sep="")
  colnames(mydf) <- c("Individuals", "Count", "Resolution")
  return(list(mydf, df))
}

#Same function as above, but for the boundaries inferred within species w/out orthology filtering:
intra.full.assess <- function(resolution, species){
  if(species=="H"){
    df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/h.final.allmerge.hg38", sep=""), data.table=F, header=F)
    df$F <- df$E <- df$B <- df$A <- 0
    df$A[grep("A", df$V4)] <- 1
    df$B[grep("B", df$V4)] <- 1
    df$E[grep("E", df$V4)] <- 1
    df$F[grep("F", df$V4)] <- 1
  }
  if(species=="C"){
    df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/c.final.allmerge.panTro5", sep=""), data.table=F, header=F)
    df$H <- df$G <- df$D <- df$C <- 0
    df$H[grep("H", df$V4)] <- 1
    df$G[grep("G", df$V4)] <- 1
    df$D[grep("D", df$V4)] <- 1
    df$C[grep("C", df$V4)] <- 1
  }
  df$indi.found <- rowSums(df[,5:8])
  mydf <- as.data.frame(melt(table(df$indi.found)))
  mydf$resolution <- paste(resolution/1000, "kb", sep="")
  colnames(mydf) <- c("Individuals", "Count", "Resolution")
  return(list(mydf, df))
}

#For plotting stats on orthologous boundaries.
intra.ortho.plotter <- function(species){
  bounds.10 <- intra.ortho.assess(10000, species)[[1]]
  bounds.25 <- intra.ortho.assess(25000, species)[[1]]
  bounds.50 <- intra.ortho.assess(50000, species)[[1]]
  bounds.100 <- intra.ortho.assess(100000, species)[[1]]
  bounds.250 <- intra.ortho.assess(250000, species)[[1]]
  bounds.500 <- intra.ortho.assess(500000, species)[[1]]
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  
  if(species=="H"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Humans") +xlab("Resolution of Analysis") + ylab("Boundary Count") + guides(fill=guide_legend(title="# Individuals"))
  }
   if(species=="C"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Chimps") +xlab("Resolution of Analysis") + ylab("Boundary Count")+ guides(fill=guide_legend(title="# Individuals"))
   }
  print(myplot)
}

#Same as above, but for the set of boundaries without orthology filtering.
intra.full.plotter <- function(species){
  bounds.10 <- intra.full.assess(10000, species)[[1]]
  bounds.25 <- intra.full.assess(25000, species)[[1]]
  bounds.50 <- intra.full.assess(50000, species)[[1]]
  bounds.100 <- intra.full.assess(100000, species)[[1]]
  bounds.250 <- intra.full.assess(250000, species)[[1]]
  bounds.500 <- intra.full.assess(500000, species)[[1]]
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  
  if(species=="H"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Humans") +xlab("Resolution of Analysis") + ylab("Boundary Count")+ guides(fill=guide_legend(title="# Individuals"))
  }
   if(species=="C"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Chimps") +xlab("Resolution of Analysis") + ylab("Boundary Count")+ guides(fill=guide_legend(title="# Individuals"))
   }
  print(myplot)
}
options(scipen=999)
intra.ortho.plotter("H")

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-1.png”

intra.ortho.plotter("C")

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-2.png”

intra.full.plotter("H")

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-3.png”

intra.full.plotter("C")

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-4.png”

##Secondary method for assessing intraspecies variance in TAD boundaries, Jaccard index.###
jac.intra.full <- function(resolution, species){
  if(species=="H"){
    AB <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AB.full", sep=""))[1,3])
    AE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AE.full", sep=""))[1,3])
    AF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AF.full", sep=""))[1,3])
    BE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BE.full", sep=""))[1,3])
    BF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BF.full", sep=""))[1,3])
    EF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EF.full", sep=""))[1,3])
    jaccard <- data.frame(A=c(1, AB, AE, AF), B=c(AB, 1, BE, BF), E=c(AE, BE, 1, EF), F=c(AF, BF, EF, 1))
    rownames(jaccard) <- colnames(jaccard)
  }
  if(species=="C"){
    CD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CD.full", sep=""))[1,3])
    CG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CG.full", sep=""))[1,3])
    CH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CH.full", sep=""))[1,3])
    DG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DG.full", sep=""))[1,3])
    DH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DH.full", sep=""))[1,3])
    GH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.GH.full", sep=""))[1,3])
    jaccard <- data.frame(C=c(1, CD, CG, CH), D=c(CD, 1, DG, DH), G=c(CG, DG, 1, GH), H=c(CH, DH, GH, 1))
    rownames(jaccard) <- colnames(jaccard) 
  }
  return(jaccard)
}

#Same as the above function, but only on orthologous TAD boundaries:
jac.intra.ortho <- function(resolution, species){
  if(species=="H"){
    AB <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AB.ortho", sep=""))[1,3])
    AE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AE.ortho", sep=""))[1,3])
    AF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AF.ortho", sep=""))[1,3])
    BE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BE.ortho", sep=""))[1,3])
    BF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BF.ortho", sep=""))[1,3])
    EF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EF.ortho", sep=""))[1,3])
    jaccard <- data.frame(A=c(1, AB, AE, AF), B=c(AB, 1, BE, BF), E=c(AE, BE, 1, EF), F=c(AF, BF, EF, 1))
    rownames(jaccard) <- colnames(jaccard)
  }
  if(species=="C"){
    CD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CD.ortho", sep=""))[1,3])
    CG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CG.ortho", sep=""))[1,3])
    CH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CH.ortho", sep=""))[1,3])
    DG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DG.ortho", sep=""))[1,3])
    DH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DH.ortho", sep=""))[1,3])
    GH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.GH.ortho", sep=""))[1,3])
    jaccard <- data.frame(C=c(1, CD, CG, CH), D=c(CD, 1, DG, DH), G=c(CG, DG, 1, GH), H=c(CH, DH, GH, 1))
    rownames(jaccard) <- colnames(jaccard) 
  }
  return(jaccard)
}

#Can be clustered upon later.
jac.intra.full(10000, "H")

         A        B        E        F
A 1.000000 0.397850 0.373858 0.383146
B 0.397850 1.000000 0.364933 0.400585
E 0.373858 0.364933 1.000000 0.378692
F 0.383146 0.400585 0.378692 1.000000

jac.intra.ortho(10000, "H")

         A        B        E        F
A 1.000000 0.398166 0.373086 0.382912
B 0.398166 1.000000 0.364743 0.401024
E 0.373086 0.364743 1.000000 0.379549
F 0.382912 0.401024 0.379549 1.000000

jac.intra.full(10000, "C")

         C        D        G        H
C 1.000000 0.387792 0.406708 0.391079
D 0.387792 1.000000 0.380699 0.391395
G 0.406708 0.380699 1.000000 0.400648
H 0.391079 0.391395 0.400648 1.000000

jac.intra.ortho(10000, "C")

         C        D        G        H
C 1.000000 0.392809 0.414110 0.397362
D 0.392809 1.000000 0.386979 0.395501
G 0.414110 0.386979 1.000000 0.406590
H 0.397362 0.395501 0.406590 1.000000

###Interspecies Conservation of TAD Boundaries###
#This is performed in much the same manner as above, but this time, combining the file across species.
#Once again, checked, and merging before/after individual file merging makes no difference.
inter.bound.cons <- function(resolution, clust=F){
  df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/final.merged.combined.each.merge.ortho.hg38", sep=""), data.table=F, header=F)
  df$H <- df$G <- df$F <- df$E <- df$D <- df$C <- df$B <- df$A <- 0
  df$A[grep("A", df$V4)] <- 1
  df$B[grep("B", df$V4)] <- 1
  df$C[grep("C", df$V4)] <- 1
  df$D[grep("D", df$V4)] <- 1
  df$E[grep("E", df$V4)] <- 1
  df$F[grep("F", df$V4)] <- 1
  df$G[grep("G", df$V4)] <- 1
  df$H[grep("H", df$V4)] <- 1
  df$found_in_H <- rowSums(df[,c(5:6, 9:10)])
  df$found_in_C <- rowSums(df[,c(7:8, 11:12)])
  df.2 <- filter(df, found_in_H>=2|found_in_C>=2)
  df.3 <- filter(df, found_in_H>=3|found_in_C>=3)
  df.4 <- filter(df, found_in_H>=4|found_in_C>=4)
  cons.1 <- ifelse(df$found_in_H>=1&df$found_in_C>=1, "Shared", ifelse(df$found_in_H>=1, "Human", "Chimpanzee"))
  cons.2 <- ifelse(df.2$found_in_H>=1&df.2$found_in_C>=1, "Shared", ifelse(df.2$found_in_H>=1, "Human", "Chimpanzee"))
  cons.3 <- ifelse(df.3$found_in_H>=1&df.3$found_in_C>=1, "Shared", ifelse(df.3$found_in_H>=1, "Human", "Chimpanzee"))
  cons.4 <- ifelse(df.4$found_in_H>=1&df.4$found_in_C>=1, "Shared", ifelse(df.4$found_in_H>=1, "Human", "Chimpanzee"))
  cons.table <- as.data.frame(rbind(table(factor(cons.1, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.2, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.3, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.4, levels=c("Shared", "Human", "Chimpanzee")))))
  cons.table$stringency <- 1:4
  cons.table$resolution <- paste(resolution/1000, "kb", sep="")
  if(clust==FALSE){
  return(cons.table)}
  if(clust==TRUE){
    return(df)
  }
}

#######Interspecies clustering on individual-level boundary element inferences######
#Function to calculate percentages:
percentage.table.calc <- function(df){
  colnames(df) <- c("A", "B", "C", "D", "E", "F", "G", "H")
  A.vec <- c(sum(df$A==1&df$A==1), sum(df$A==1&df$B==1), sum(df$A==1&df$C==1), sum(df$A==1&df$D==1), sum(df$A==1&df$E==1), sum(df$A==1&df$F==1), sum(df$A==1&df$G==1),  sum(df$A==1&df$H==1))
  B.vec <- c(sum(df$B==1&df$A==1), sum(df$B==1&df$B==1), sum(df$B==1&df$C==1), sum(df$B==1&df$D==1), sum(df$B==1&df$E==1), sum(df$B==1&df$F==1), sum(df$B==1&df$G==1),  sum(df$B==1&df$H==1))
  C.vec <- c(sum(df$C==1&df$A==1), sum(df$C==1&df$B==1), sum(df$C==1&df$C==1), sum(df$C==1&df$D==1), sum(df$C==1&df$E==1), sum(df$C==1&df$F==1), sum(df$C==1&df$G==1),  sum(df$C==1&df$H==1))
  D.vec <- c(sum(df$D==1&df$A==1), sum(df$D==1&df$B==1), sum(df$D==1&df$C==1), sum(df$D==1&df$D==1), sum(df$D==1&df$E==1), sum(df$D==1&df$F==1), sum(df$D==1&df$G==1),  sum(df$D==1&df$H==1))
  E.vec <- c(sum(df$E==1&df$A==1), sum(df$E==1&df$B==1), sum(df$E==1&df$C==1), sum(df$E==1&df$D==1), sum(df$E==1&df$E==1), sum(df$E==1&df$F==1), sum(df$E==1&df$G==1),  sum(df$E==1&df$H==1))
  F.vec <- c(sum(df$F==1&df$A==1), sum(df$F==1&df$B==1), sum(df$F==1&df$C==1), sum(df$F==1&df$D==1), sum(df$F==1&df$E==1), sum(df$F==1&df$F==1), sum(df$F==1&df$G==1),  sum(df$F==1&df$H==1))
  G.vec <- c(sum(df$G==1&df$A==1), sum(df$G==1&df$B==1), sum(df$G==1&df$C==1), sum(df$G==1&df$D==1), sum(df$G==1&df$E==1), sum(df$G==1&df$F==1), sum(df$G==1&df$G==1),  sum(df$G==1&df$H==1))
  H.vec <- c(sum(df$H==1&df$A==1), sum(df$H==1&df$B==1), sum(df$H==1&df$C==1), sum(df$H==1&df$D==1), sum(df$H==1&df$E==1), sum(df$H==1&df$F==1), sum(df$H==1&df$G==1),  sum(df$H==1&df$H==1))
  A.vec <- A.vec/A.vec[1]
  B.vec <- B.vec/B.vec[2]
  C.vec <- C.vec/C.vec[3]
  D.vec <- D.vec/D.vec[4]
  E.vec <- E.vec/E.vec[5]
  F.vec <- F.vec/F.vec[6]
  G.vec <- G.vec/G.vec[7]
  H.vec <- H.vec/H.vec[8]
  
  mydata <- cbind(A.vec, B.vec, C.vec, D.vec, E.vec, F.vec, G.vec, H.vec)
  rownames(mydata) <- colnames(mydata) <- c("A", "B", "C", "D", "E", "F", "G", "H")
  return(mydata)
}

cor.table.calc <- function(df){
  colnames(df) <- c("A", "B", "C", "D", "E", "F", "G", "H")
  A.vec <- c(cor(df$A, df$A), cor(df$A, df$B), cor(df$A, df$C), cor(df$A, df$D), cor(df$A, df$E), cor(df$A, df$F), cor(df$A, df$G),  cor(df$A, df$H))
  B.vec <- c(cor(df$B, df$A), cor(df$B, df$B), cor(df$B, df$C), cor(df$B, df$D), cor(df$B, df$E), cor(df$B, df$F), cor(df$B, df$G),  cor(df$B, df$H))
  C.vec <- c(cor(df$C, df$A), cor(df$C, df$B), cor(df$C, df$C), cor(df$C, df$D), cor(df$C, df$E), cor(df$C, df$F), cor(df$C, df$G),  cor(df$C, df$H))
  D.vec <- c(cor(df$D, df$A), cor(df$D, df$B), cor(df$D, df$C), cor(df$D, df$D), cor(df$D, df$E), cor(df$D, df$F), cor(df$D, df$G),  cor(df$D, df$H))
  E.vec <- c(cor(df$E, df$A), cor(df$E, df$B), cor(df$E, df$C), cor(df$E, df$D), cor(df$E, df$E), cor(df$E, df$F), cor(df$E, df$G),  cor(df$E, df$H))
  F.vec <- c(cor(df$F, df$A), cor(df$F, df$B), cor(df$F, df$C), cor(df$F, df$D), cor(df$F, df$E), cor(df$F, df$F), cor(df$F, df$G),  cor(df$F, df$H))
  G.vec <- c(cor(df$G, df$A), cor(df$G, df$B), cor(df$G, df$C), cor(df$G, df$D), cor(df$G, df$E), cor(df$G, df$F), cor(df$G, df$G),  cor(df$G, df$H))
  H.vec <- c(cor(df$H, df$A), cor(df$H, df$B), cor(df$H, df$C), cor(df$H, df$D), cor(df$H, df$E), cor(df$H, df$F), cor(df$H, df$G),  cor(df$H, df$H))
  
  mydata <- cbind(A.vec, B.vec, C.vec, D.vec, E.vec, F.vec, G.vec, H.vec)
  rownames(mydata) <- colnames(mydata) <- c("A", "B", "C", "D", "E", "F", "G", "H")
  return(mydata)
}

bounds.10 <- inter.bound.cons(10000, clust=TRUE)

test <- percentage.table.calc(bounds.10[,5:12])
test2 <- cor.table.calc(bounds.10[,5:12])

colnames(test) <- colnames(test2) <-  c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2") #Better for presentation
rownames(test) <- rownames(test2) <-  colnames(test)

#Similar to figure 1B, but done on the whole set of data, without subsetting down to hits found significant in at least 4 individuals (regardless of species).
heatmaply(test, main="Pairwise Proportions of Shared TAD Boundaries @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))

#heatmaply(test2, main="TAD Boundary Pairwise Pearson Correlations @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))

#FIG4D
#Change path to make sure orca can be found (in conda path); set mapbox token for orca export.
Sys.setenv(PATH="/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/opt/X11/bin:/Library/TeX/texbin:/opt/local/bin:/Users/ittaieres/miniconda3/bin/")
Sys.setenv('MAPBOX_TOKEN'='pk.eyJ1IjoiaXR0YWllcmVzIiwiYSI6ImNqeHVwanphejE3bjIzcHFmY2FvYXIxZXUifQ.rs3I8LoxJcqWBsIhVDiJCQ')
#FIG4C
dis <- heatmaply(test, main="Pairwise Proportions of Shared TAD Boundaries @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30), cellnote_size=10, fontsize_row=18, fontsize_col=18)
orca(dis, file="~/Desktop/Fig4D.pdf") #Note that this just created a ~ folder in the hicipsc directory, didn't redirect to home folder.
####

bounds.indi.clust <- function(resolution){
  bounds <- inter.bound.cons(resolution, clust=TRUE)
  heat <- percentage.table.calc(bounds[,5:12])
  colnames(heat) <- rownames(heat) <- c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2")
  heatmaply(heat, main=paste("Pairwise Proportions of Shared TAD Boundaries @ ", resolution/1000, "kb", sep=""), k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))
}

#FIGS12F, boundary clustering on individual basis with Arrowhead inferences.
bounds.indi.clust(10000)

bounds.indi.clust(25000)

bounds.indi.clust(50000)

bounds.indi.clust(100000)

bounds.indi.clust(250000)

bounds.indi.clust(500000)

boundary.inter.plot <- function(y.max=20000){
  bounds.10 <- inter.bound.cons(10000)
  bounds.25 <- inter.bound.cons(25000)
  bounds.50 <- inter.bound.cons(50000)
  bounds.100 <- inter.bound.cons(100000)
  bounds.250 <- inter.bound.cons(250000)
  bounds.500 <- inter.bound.cons(500000)
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$H.perc <- ggbounds$Human/rowSums(ggbounds[,1:3])
  ggbounds$C.perc <- ggbounds$Human/rowSums(ggbounds[,1:3])
  ggbounds$shared.perc <- ggbounds$Shared/rowSums(ggbounds[,1:3])
  ggbounds$resolution <- factor(ggbounds$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  ggbounds.1 <- filter(ggbounds, stringency==1) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.2 <- filter(ggbounds, stringency==2) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.3 <- filter(ggbounds, stringency==3) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.4 <- filter(ggbounds, stringency==4) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  plot.1 <- ggplot(data=ggbounds.1, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=1") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3))+ scale_fill_manual(name="Species of Discovery", values=c("#619CFF","#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared"))  + coord_cartesian(ylim=c(0, y.max))#+ scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))
  plot.2 <- ggplot(data=ggbounds.2, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=2") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3))+ scale_fill_manual(name="Species of Discovery", values=c("#619CFF","#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared")) + coord_cartesian(ylim=c(0, y.max))#+ scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))
  plot.3 <- ggplot(data=ggbounds.3, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=3") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Species of Discovery", values=c("#619CFF","#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared")) + coord_cartesian(ylim=c(0, y.max))#+ scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))
  plot.4 <- ggplot(data=ggbounds.4, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=4") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Species of Discovery", values=c("#619CFF","#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared")) + coord_cartesian(ylim=c(0, y.max))
  print(plot.1)
  print(plot.2)
  print(plot.3)
  print(plot.4)
  print(ggbounds.4)
}
boundary.inter.plot() #FIGS12C-D

Using resolution as id variables
Using resolution as id variables
Using resolution as id variables
Using resolution as id variables

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-12.png”

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-13.png”

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-14.png”

Past versions of “Boundary Conservation with Individual Level Arrowhead Inferences-15.png”

   resolution   variable value
1        10kb      Human   952
2        25kb      Human   634
3        50kb      Human   971
4       100kb      Human   597
5       250kb      Human   218
6       500kb      Human    47
7        10kb Chimpanzee   423
8        25kb Chimpanzee   595
9        50kb Chimpanzee  1086
10      100kb Chimpanzee   666
11      250kb Chimpanzee   208
12      500kb Chimpanzee    50
13       10kb     Shared  6930
14       25kb     Shared  4082
15       50kb     Shared  1281
16      100kb     Shared   310
17      250kb     Shared    38
18      500kb     Shared     5

#Now, Jaccard indices for interspecies variation in TAD boundaries#
jac.inter <- function(resolution){
  AB <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AB.ortho", sep=""))[1,3])
  AC <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AC.ortho", sep=""))[1,3])
  AD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AD.ortho", sep=""))[1,3])
  AE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AE.ortho", sep=""))[1,3])
  AF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AF.ortho", sep=""))[1,3])
  AG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AG.ortho", sep=""))[1,3])
  AH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.AH.ortho", sep=""))[1,3])
  BC <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BC.ortho", sep=""))[1,3])
  BD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BD.ortho", sep=""))[1,3])
  BE <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BE.ortho", sep=""))[1,3])
  BF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BF.ortho", sep=""))[1,3])
  BG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BG.ortho", sep=""))[1,3])
  BH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.BH.ortho", sep=""))[1,3])
  CD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CD.ortho", sep=""))[1,3])
  CG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CG.ortho", sep=""))[1,3])
  CH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.CH.ortho", sep=""))[1,3])
  DG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DG.ortho", sep=""))[1,3])
  DH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.DH.ortho", sep=""))[1,3])
  EC <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EC.ortho", sep=""))[1,3])
  ED <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.ED.ortho", sep=""))[1,3])
  EF <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EF.ortho", sep=""))[1,3])
  EG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EG.ortho", sep=""))[1,3])
  EH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.EH.ortho", sep=""))[1,3])
  FC <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.FC.ortho", sep=""))[1,3])
  FD <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.FD.ortho", sep=""))[1,3])
  FG <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.FG.ortho", sep=""))[1,3])
  FH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.FH.ortho", sep=""))[1,3])
  GH <- as.numeric(fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/jac.GH.ortho", sep=""))[1,3])
  jaccard <- data.frame(A=c(1, AB, AC, AD, AE, AF, AG, AH), B=c(AB, 1, BC, BD, BE, BF, BG, BH), C=c(AC, BC, 1, CD, EC, FC, CG, CH), D=c(AD, BD, CD, 1, ED, FD, DG, DH), E=c(AE, BE, EC, ED, 1, EF, EG, EH), F=c(AF, BF, FC, FD, EF, 1, FG, FH), G=c(AG, BG, CG, DG, EG, FG, 1, GH), H=c(AH, BH, CH, DH, EH, FH, GH, 1))
  rownames(jaccard) <- colnames(jaccard)
  return(jaccard)
}
jacs.10 <- jac.inter(10000) #Can be clustered upon later.

colnames(jacs.10) <- c("HF1", "HM1", "CM1", "CF1", "HM2", "HF2", "CM2", "CF2") #Better for presentation
rownames(jacs.10) <- colnames(jacs.10)

#Similar to figure 1B, but done on the whole set of data, without subsetting down to hits found significant in at least 4 individuals (regardless of species).
heatmaply(jacs.10, main="Pairwise Jaccard Index @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))

Inter- and intra-species TAD Body Variance on Individual-level Arrowhead Inferences

#Domains are considerably more complex to analyze, primarily due to the fact that the Arrowhead domain output can be nested. As a result, this analysis is a bit more coarse-grained. By this I mean to say that the nested TADs cannot simply be merged across all individuals to determine which are shared, so this must be done on an individual-by-individual basis to infer specific relationships while maintaining the number of discoveries of TADs for each individual. Thus, the average of many statistics across individuals is taken here.
#Function to get a given individual's statistics on TAD conservation, intra and inter species.
domain.compare <- function(resolution, letter, species, type, clust=F){
  df <- fread(paste("data/TADs/Arrowhead_individuals/", resolution, "_compare/inter.", letter, ".", type, sep=""))
  df$ID <- paste(df$V1, df$V2, df$V3, sep="-")
  if(type=="rao"){
  df$size <- df$V3-df$V2
  df$dist_max <- ifelse((df$size*.2)<=50000, df$size*.2, 50000) #Use 0.2, not 0.5, these are closely related species.
  df$conserved <- ifelse((((df$V2-df$V7)^2+(df$V3-df$V8)^2)^0.5)<df$dist_max, "yes", "no")
  merged.df <- as.data.frame(group_by(df, ID) %>% summarise(individuals=paste(V5[which(conserved=="yes")], collapse=",")))}
  if(type=="loj"){
    merged.df <- as.data.frame(group_by(df, ID) %>% summarise(individuals=paste(V5, collapse=",")))
  }
  merged.df$H <- merged.df$G <- merged.df$F <- merged.df$E <- merged.df$D <- merged.df$C <- merged.df$B <- merged.df$A <- 0
  merged.df$A[grep("A", merged.df$individuals)] <- 1
  merged.df$B[grep("B", merged.df$individuals)] <- 1
  merged.df$E[grep("E", merged.df$individuals)] <- 1
  merged.df$F[grep("F", merged.df$individuals)] <- 1
  merged.df$C[grep("C", merged.df$individuals)] <- 1
  merged.df$D[grep("D", merged.df$individuals)] <- 1
  merged.df$G[grep("G", merged.df$individuals)] <- 1
  merged.df$H[grep("H", merged.df$individuals)] <- 1
  merged.df$found_in_H <- rowSums(merged.df[,c(3, 4, 7, 8)])
  merged.df$found_in_C <- rowSums(merged.df[,c(5, 6, 9, 10)])
  merged.df$cons <- ifelse(merged.df$found_in_C>=1&merged.df$found_in_H>=1, "Shared", ifelse(merged.df$found_in_C==0, "Human", "Chimpanzee"))
  
  if(species=="H"){
    intra.stat <- table(merged.df$found_in_H)
    inter.1.stat <- table(factor(merged.df$cons, levels=c("Human", "Shared")))
    inter.2.stat <- table(factor(filter(merged.df, found_in_H>=2|found_in_C>=2)$cons, levels=c("Human", "Shared")))
    inter.3.stat <- table(factor(filter(merged.df, found_in_H>=3|found_in_C>=3)$cons, levels=c("Human", "Shared")))
    inter.4.stat <- table(factor(filter(merged.df, found_in_H>=4|found_in_C>=4)$cons, levels=c("Human", "Shared")))}
  if(species=="C"){
    intra.stat <- table(merged.df$found_in_C)
    inter.1.stat <- table(factor(merged.df$cons, levels=c("Chimpanzee", "Shared")))
    inter.2.stat <- table(factor(filter(merged.df, found_in_H>=2|found_in_C>=2)$cons, levels=c("Chimpanzee", "Shared")))
    inter.3.stat <- table(factor(filter(merged.df, found_in_H>=3|found_in_C>=3)$cons, levels=c("Chimpanzee", "Shared")))
    inter.4.stat <- table(factor(filter(merged.df, found_in_H>=4|found_in_C>=4)$cons, levels=c("Chimpanzee", "Shared")))}
  
  conservation <- as.data.frame(rbind(inter.1.stat, inter.2.stat, inter.3.stat, inter.4.stat))
  conservation$resolution <- paste(resolution/1000, "kb", sep="")
  conservation$stringency <- 1:4
  if(clust==F){
  return(list(intra.stat, conservation))}
  if(clust==TRUE){
    perc.table <- percentage.table.calc(merged.df[,3:10])
    return(perc.table[,letter])
  }
}

###Interspecies individual level TAD clustering###
A.domain <- domain.compare(10000, "A", "H", "loj", clust=TRUE)
B.domain <- domain.compare(10000, "B", "H", "loj", clust=TRUE)
C.domain <- domain.compare(10000, "C", "C", "loj", clust=TRUE)
D.domain <- domain.compare(10000, "D", "C", "loj", clust=TRUE)
E.domain <- domain.compare(10000, "E", "H", "loj", clust=TRUE)
F.domain <- domain.compare(10000, "F", "H", "loj", clust=TRUE)
G.domain <- domain.compare(10000, "G", "C", "loj", clust=TRUE)
H.domain <- domain.compare(10000, "H", "C", "loj", clust=TRUE)

#Taking the mean of each pairwise comparison. Kind of complex, prefer to just use the individual vectors as below. But this is for if we want to make sure the heatmap is symmetrical.
domainclust <- data.frame(A=c(1, mean(A.domain[2], B.domain[1]), mean(A.domain[3], C.domain[1]), mean(A.domain[4], D.domain[1]), mean(A.domain[5], E.domain[1]), mean(A.domain[6], F.domain[1]), mean(A.domain[7], G.domain["A"]), mean(A.domain["H"], H.domain["A"])), B=c(mean(B.domain["A"], A.domain["B"]), 1, mean(B.domain["C"], C.domain["B"]), mean(B.domain["D"], D.domain["B"]), mean(B.domain["E"], E.domain["B"]), mean(B.domain["F"], F.domain["B"]), mean(B.domain["G"], G.domain["B"]), mean(B.domain["H"], H.domain["B"])), C=c(mean(C.domain["A"], A.domain["C"]), mean(C.domain["B"], B.domain["C"]), 1, mean(C.domain["D"], D.domain["C"]), mean(C.domain["E"], E.domain["C"]), mean(C.domain["F"], F.domain["C"]), mean(C.domain["G"], G.domain["C"]), mean(C.domain["H"], H.domain["C"])), D=c(mean(D.domain["A"], A.domain["D"]), mean(D.domain["B"], B.domain["D"]), mean(D.domain["C"], C.domain["D"]), 1, mean(D.domain["E"], E.domain["D"]), mean(D.domain["F"], F.domain["D"]), mean(D.domain["G"], G.domain["D"]), mean(D.domain["H"], H.domain["D"])), E=c(mean(E.domain["A"], A.domain["E"]), mean(E.domain["B"], B.domain["E"]), mean(E.domain["C"], C.domain["E"]), mean(E.domain["D"], D.domain["E"]), 1, mean(E.domain["F"], F.domain["E"]), mean(E.domain["G"], G.domain["E"]), mean(E.domain["H"], H.domain["E"])), F=c(mean(F.domain["A"], A.domain["F"]), mean(F.domain["B"], B.domain["F"]), mean(F.domain["C"], C.domain["F"]), mean(F.domain["D"], D.domain["F"]), mean(F.domain["E"], E.domain["F"]), 1, mean(F.domain["G"], G.domain["F"]), mean(F.domain["H"], H.domain["F"])), G=c(mean(G.domain["A"], A.domain["G"]), mean(G.domain["B"], B.domain["G"]), mean(G.domain["C"], C.domain["G"]), mean(G.domain["D"], D.domain["G"]), mean(G.domain["E"], E.domain["G"]), mean(G.domain["F"], F.domain["G"]), 1, mean(G.domain["H"], H.domain["G"])), H=c(mean(H.domain["A"], A.domain["H"]), mean(H.domain["B"], B.domain["H"]), mean(H.domain["C"], C.domain["H"]), mean(H.domain["D"], D.domain["H"]), mean(H.domain["E"], E.domain["H"]), mean(H.domain["F"], F.domain["H"]), mean(H.domain["G"], G.domain["H"]), 1))

clust2 <- rbind(A.domain, B.domain, C.domain, D.domain, E.domain, F.domain, G.domain, H.domain)

colnames(domainclust) <- colnames(clust2) <-  c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2") #Better for presentation
rownames(domainclust) <- rownames(clust2) <- colnames(domainclust)

heatmaply(domainclust, main="Pairwise Proportions of Shared TADs @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))

heatmaply(clust2, main="Pairwise Proportions of Shared TADs @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))

#FIG4C:

#Change path to make sure orca can be found (in conda path); set mapbox token for orca export.
Sys.setenv(PATH="/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/opt/X11/bin:/Library/TeX/texbin:/opt/local/bin:/Users/ittaieres/miniconda3/bin/")
Sys.setenv('MAPBOX_TOKEN'='pk.eyJ1IjoiaXR0YWllcmVzIiwiYSI6ImNqeHVwanphejE3bjIzcHFmY2FvYXIxZXUifQ.rs3I8LoxJcqWBsIhVDiJCQ')
#FIG4C
dis <- heatmaply(clust2, main="Pairwise Proportions of Shared TADs @ 10kb", k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30), cellnote_size=10, fontsize_row=18, fontsize_col=18)
orca(dis, file="~/Desktop/Fig4C.pdf") #Note that this just created a ~ folder in the hicipsc directory, didn't redirect to home folder.



indi.TAD.clust <- function(type, resolution){
  A.domain <- domain.compare(resolution, "A", "H", type, clust=TRUE)
  B.domain <- domain.compare(resolution, "B", "H", type, clust=TRUE)
  C.domain <- domain.compare(resolution, "C", "C", type, clust=TRUE)
  D.domain <- domain.compare(resolution, "D", "C", type, clust=TRUE)
  E.domain <- domain.compare(resolution, "E", "H", type, clust=TRUE)
  F.domain <- domain.compare(resolution, "F", "H", type, clust=TRUE)
  G.domain <- domain.compare(resolution, "G", "C", type, clust=TRUE)
  H.domain <- domain.compare(resolution, "H", "C", type, clust=TRUE)
  indi.TAD.df <- rbind(A.domain, B.domain, C.domain, D.domain, E.domain, F.domain, G.domain, H.domain)
  colnames(indi.TAD.df) <- rownames(indi.TAD.df) <- c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2")
  heatmaply(indi.TAD.df, main=paste("Pairwise Proportions of Shared TADs @ ", resolution/1000, "kb", sep=""), k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))
}

#FIGS12E alternative, TAD clustering on individual basis, loj methodology (90% reciprocal overlap).
options(scipen=999)
indi.TAD.clust("loj", 10000)

indi.TAD.clust("loj", 25000)

indi.TAD.clust("loj", 50000)

indi.TAD.clust("loj", 100000)

indi.TAD.clust("loj", 250000)

indi.TAD.clust("loj", 500000)

#FIGS12E, TAD clustering on individual basis, Rao methodology. Use this as main b/c using Rao method for stringency analysis.
indi.TAD.clust("rao", 10000)

indi.TAD.clust("rao", 25000)

indi.TAD.clust("rao", 50000)

indi.TAD.clust("rao", 100000)

indi.TAD.clust("rao", 250000)

indi.TAD.clust("rao", 500000)

concatenator <- function(resolution, type) {
  A <- domain.compare(resolution, "A", "H", type)
  B <- domain.compare(resolution, "B", "H", type)
  E <- domain.compare(resolution, "E", "H", type)
  F <- domain.compare(resolution, "F", "H", type)
    intra.H <- data.frame(indi.found=1:4, count=c(sum(A[[1]][1], B[[1]][1], E[[1]][1], F[[1]][1]), mean(A[[1]][2], B[[1]][2], E[[1]][2], F[[1]][2]), mean(A[[1]][3], B[[1]][3], E[[1]][3], F[[1]][3]), mean(A[[1]][4], B[[1]][4], E[[1]][4], F[[1]][4])))
    intra.H$perc <- intra.H$count/sum(intra.H$count)
  
  C <- domain.compare(resolution, "C", "C", type)
  D <- domain.compare(resolution, "D", "C", type)
  G <- domain.compare(resolution, "G", "C", type)
  H <- domain.compare(resolution, "H", "C", type)
    intra.C <- data.frame(indi.found=1:4, count=c(sum(C[[1]][1], D[[1]][1], G[[1]][1], H[[1]][1]), mean(C[[1]][2], D[[1]][2], G[[1]][2], H[[1]][2]), mean(C[[1]][3], D[[1]][3], G[[1]][3], H[[1]][3]), mean(C[[1]][4], D[[1]][4], G[[1]][4], H[[1]][4])))
    intra.C$perc <- intra.C$count/sum(intra.C$count)
  intra.H$resolution <-intra.C$resolution <- paste(resolution/1000, "kb", sep="")
  print(C[[2]][,1:2])
  print(D[[2]][,1:2])
  print(G[[2]][,1:2])
  print(H[[2]][,1:2])
  print(A[[2]][,1:2])
  print(B[[2]][,1:2])
  print(E[[2]][,1:2])
  print(F[[2]][,1:2])
  inter.c <- ((C[[2]][,1:2] + D[[2]][,1:2] + G[[2]][,1:2] + H[[2]][,1:2])/4)
  inter.h <- ((A[[2]][,1:2] + B[[2]][,1:2] + E[[2]][,1:2] + F[[2]][,1:2])/4)
  inter <- as.data.frame(cbind(round(inter.c[,1]), round(inter.h[,1]), round((inter.c[,2]+inter.h[,2])/2)))
  colnames(inter) <- c("Chimpanzee", "Human", "Shared")
  inter$resoultion <- paste(resolution/1000, "kb", sep="")
  inter$stringency <- 1:4
  return(list(intra.H, intra.C, inter))
}

domain.plotter <- function(type, intra.ymax=8000, inter.ymax=8200){
    intra.10 <- concatenator(10000, type)
    intra.25 <- concatenator(25000, type)
    intra.50 <- concatenator(50000, type)
    intra.100 <- concatenator(100000, type)
    intra.250 <- concatenator(250000, type)
    intra.500 <- concatenator(500000, type)
    
    intra.h <- rbind(intra.10[[1]], intra.25[[1]], intra.50[[1]], intra.100[[1]], intra.250[[1]], intra.500[[1]])
    intra.h$resolution <- factor(intra.h$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    print(intra.h)
    intra.h.plot <- ggplot(data=intra.h, aes(x=resolution, group=resolution, y=count, fill=as.factor(indi.found))) + geom_bar(stat="identity") + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Intraspecies TAD Variance, Humans") + guides(fill=guide_legend(title="# Individuals w/ TAD")) + theme(plot.title=element_text(hjust=0.3)) + coord_cartesian(ylim=c(0, intra.ymax))
    
    intra.c <- rbind(intra.10[[2]], intra.25[[2]], intra.50[[2]], intra.100[[2]], intra.250[[2]], intra.500[[2]])
    intra.c$resolution <- factor(intra.c$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    print(intra.c)
    intra.c.plot <- ggplot(data=intra.c, aes(x=resolution, group=resolution, y=count, fill=as.factor(indi.found))) + geom_bar(stat="identity") + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Intraspecies TAD Variance, Chimpanzees") + guides(fill=guide_legend(title="# Individuals w/ TAD")) + theme(plot.title=element_text(hjust=0.3)) + coord_cartesian(ylim=c(0, intra.ymax))
    
    inter <- rbind(intra.10[[3]], intra.25[[3]], intra.50[[3]], intra.100[[3]], intra.250[[3]], intra.500[[3]])
    inter$perc.shared <- inter$Shared/rowSums(inter[,1:3])
    inter$perc.h <- inter$Human/rowSums(inter[,1:3])
    inter$perc.c <- inter$Chimpanzee/rowSums(inter[,1:3])
    print(inter)
    inter <- inter[,-6:-8]
    inter.1 <- filter(inter, stringency==1)[,-5] %>% melt(., by="resolution")
    inter.1$resolution <- factor(inter.1$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.2 <- filter(inter, stringency==2)[,-5] %>% melt(., by="resolution")
    inter.2$resolution <- factor(inter.2$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.3 <- filter(inter, stringency==3)[,-5] %>% melt(., by="resolution")
    inter.3$resolution <- factor(inter.3$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.4 <- filter(inter, stringency==4)[,-5] %>% melt(., by="resolution")
    inter.4$resolution <- factor(inter.4$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    
    plot.1 <- ggplot(data=inter.1, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=1") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.2 <- ggplot(data=inter.2, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=2") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.3 <- ggplot(data=inter.3, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=3") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.4 <- ggplot(data=inter.4, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=4") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    print(intra.h.plot)
    print(intra.c.plot)
    print(plot.1)
    print(plot.2)
    print(plot.3)
    print(plot.4)
}

domain.plotter("rao", intra.ymax = 10000, inter.ymax = 8200) #FigS12A&B

             Chimpanzee Shared
inter.1.stat       1336   5174
inter.2.stat        883   5067
inter.3.stat        605   4715
inter.4.stat        373   4093
             Chimpanzee Shared
inter.1.stat       1030   4725
inter.2.stat        763   4643
inter.3.stat        554   4417
inter.4.stat        377   3933
             Chimpanzee Shared
inter.1.stat       1145   4763
inter.2.stat        870   4694
inter.3.stat        610   4460
inter.4.stat        378   3949
             Chimpanzee Shared
inter.1.stat       1064   4793
inter.2.stat        805   4734
inter.3.stat        593   4499
inter.4.stat        369   4000
             Human Shared
inter.1.stat  2111   4899
inter.2.stat  1602   4816
inter.3.stat  1197   4535
inter.4.stat   773   3969
             Human Shared
inter.1.stat  2177   4795
inter.2.stat  1664   4715
inter.3.stat  1225   4440
inter.4.stat   779   3886
             Human Shared
inter.1.stat  1809   4964
inter.2.stat  1341   4872
inter.3.stat  1067   4602
inter.4.stat   778   4065
             Human Shared
inter.1.stat  2063   4898
inter.2.stat  1623   4836
inter.3.stat  1222   4585
inter.4.stat   787   4001
             Chimpanzee Shared
inter.1.stat       1179   3390
inter.2.stat        908   3313
inter.3.stat        666   3110
inter.4.stat        437   2695
             Chimpanzee Shared
inter.1.stat       1126   3249
inter.2.stat        802   3163
inter.3.stat        609   3023
inter.4.stat        433   2632
             Chimpanzee Shared
inter.1.stat       1137   3340
inter.2.stat        905   3278
inter.3.stat        676   3095
inter.4.stat        447   2705
             Chimpanzee Shared
inter.1.stat       1122   3257
inter.2.stat        881   3204
inter.3.stat        645   3042
inter.4.stat        428   2668
             Human Shared
inter.1.stat  1474   3192
inter.2.stat  1037   3098
inter.3.stat   742   2886
inter.4.stat   437   2496
             Human Shared
inter.1.stat  1550   3131
inter.2.stat  1112   3069
inter.3.stat   741   2874
inter.4.stat   437   2465
             Human Shared
inter.1.stat  1360   3300
inter.2.stat   919   3190
inter.3.stat   674   2976
inter.4.stat   426   2548
             Human Shared
inter.1.stat  1520   3195
inter.2.stat  1105   3135
inter.3.stat   784   2941
inter.4.stat   433   2520
             Chimpanzee Shared
inter.1.stat        924   1299
inter.2.stat        644   1244
inter.3.stat        433   1084
inter.4.stat        226    848
             Chimpanzee Shared
inter.1.stat        950   1332
inter.2.stat        594   1259
inter.3.stat        401   1110
inter.4.stat        224    866
             Chimpanzee Shared
inter.1.stat        924   1300
inter.2.stat        676   1247
inter.3.stat        444   1114
inter.4.stat        227    853
             Chimpanzee Shared
inter.1.stat        937   1287
inter.2.stat        661   1228
inter.3.stat        451   1097
inter.4.stat        228    855
             Human Shared
inter.1.stat  1116   1195
inter.2.stat   563   1097
inter.3.stat   277    932
inter.4.stat   117    695
             Human Shared
inter.1.stat  1123   1193
inter.2.stat   645   1132
inter.3.stat   315    948
inter.4.stat   117    696
             Human Shared
inter.1.stat  1003   1260
inter.2.stat   478   1153
inter.3.stat   269    972
inter.4.stat   118    728
             Human Shared
inter.1.stat  1061   1237
inter.2.stat   590   1152
inter.3.stat   299    972
inter.4.stat   117    708
             Chimpanzee Shared
inter.1.stat        676    286
inter.2.stat        458    266
inter.3.stat        286    224
inter.4.stat        143    158
             Chimpanzee Shared
inter.1.stat        727    297
inter.2.stat        437    274
inter.3.stat        279    226
inter.4.stat        143    152
             Chimpanzee Shared
inter.1.stat        666    306
inter.2.stat        456    289
inter.3.stat        286    236
inter.4.stat        143    164
             Chimpanzee Shared
inter.1.stat        675    297
inter.2.stat        444    273
inter.3.stat        300    228
inter.4.stat        143    161
             Human Shared
inter.1.stat   807    258
inter.2.stat   430    246
inter.3.stat   245    206
inter.4.stat   125    142
             Human Shared
inter.1.stat   749    296
inter.2.stat   473    279
inter.3.stat   263    230
inter.4.stat   125    154
             Human Shared
inter.1.stat   729    303
inter.2.stat   386    268
inter.3.stat   229    214
inter.4.stat   125    148
             Human Shared
inter.1.stat   752    297
inter.2.stat   438    277
inter.3.stat   258    228
inter.4.stat   125    145
             Chimpanzee Shared
inter.1.stat        284     10
inter.2.stat        188      9
inter.3.stat        126      6
inter.4.stat         61      4
             Chimpanzee Shared
inter.1.stat        299     12
inter.2.stat        186      9
inter.3.stat        118      8
inter.4.stat         61      4
             Chimpanzee Shared
inter.1.stat        297      8
inter.2.stat        189      7
inter.3.stat        124      7
inter.4.stat         61      4
             Chimpanzee Shared
inter.1.stat        281     11
inter.2.stat        181     11
inter.3.stat        116     10
inter.4.stat         61      5
             Human Shared
inter.1.stat   326     10
inter.2.stat   174      8
inter.3.stat   107      8
inter.4.stat    54      4
             Human Shared
inter.1.stat   331     10
inter.2.stat   213      9
inter.3.stat   110      7
inter.4.stat    54      4
             Human Shared
inter.1.stat   321     10
inter.2.stat   178     10
inter.3.stat   103      8
inter.4.stat    54      4
             Human Shared
inter.1.stat   332      9
inter.2.stat   196      7
inter.3.stat   115      6
inter.4.stat    54      3
             Chimpanzee Shared
inter.1.stat         76      0
inter.2.stat         49      0
inter.3.stat         25      0
inter.4.stat         12      0
             Chimpanzee Shared
inter.1.stat         84      0
inter.2.stat         44      0
inter.3.stat         26      0
inter.4.stat         12      0
             Chimpanzee Shared
inter.1.stat         72      0
inter.2.stat         52      0
inter.3.stat         27      0
inter.4.stat         12      0
             Chimpanzee Shared
inter.1.stat         78      0
inter.2.stat         46      0
inter.3.stat         27      0
inter.4.stat         12      0
             Human Shared
inter.1.stat    92      0
inter.2.stat    43      0
inter.3.stat    23      0
inter.4.stat    15      0
             Human Shared
inter.1.stat    93      0
inter.2.stat    52      0
inter.3.stat    29      0
inter.4.stat    15      0
             Human Shared
inter.1.stat    91      0
inter.2.stat    38      0
inter.3.stat    23      0
inter.4.stat    15      0
             Human Shared
inter.1.stat    99      0
inter.2.stat    54      0
inter.3.stat    30      0
inter.4.stat    15      0
   indi.found count       perc resolution
1           1  2792 0.30755673       10kb
2           2   800 0.08812514       10kb
3           3  1233 0.13582287       10kb
4           4  4253 0.46849526       10kb
5           1  2569 0.38947847       25kb
6           2   653 0.09899939       25kb
7           3   886 0.13432383       25kb
8           4  2488 0.37719830       25kb
9           1  3140 0.68127576       50kb
10          2   539 0.11694511       50kb
11          3   431 0.09351269       50kb
12          4   499 0.10826644       50kb
13          1  1487 0.69162791      100kb
14          2   251 0.11674419      100kb
15          3   189 0.08790698      100kb
16          4   223 0.10372093      100kb
17          1   557 0.75372124      250kb
18          2    68 0.09201624      250kb
19          3    58 0.07848444      250kb
20          4    56 0.07577808      250kb
21          1   188 0.81385281      500kb
22          2    20 0.08658009      500kb
23          3     8 0.03463203      500kb
24          4    15 0.06493506      500kb
   indi.found count       perc resolution
1           1  2125 0.27045946       10kb
2           2   787 0.10016546       10kb
3           3  1009 0.12842052       10kb
4           4  3936 0.50095456       10kb
5           1  1701 0.29236851       25kb
6           2   543 0.09333104       25kb
7           3   704 0.12100378       25kb
8           4  2870 0.49329667       25kb
9           1  1720 0.48697622       50kb
10          2   395 0.11183465       50kb
11          3   486 0.13759909       50kb
12          4   931 0.26359003       50kb
13          1  1136 0.61907357      100kb
14          2   216 0.11771117      100kb
15          3   212 0.11553134      100kb
16          4   271 0.14768392      100kb
17          1   425 0.68548387      250kb
18          2    65 0.10483871      250kb
19          3    65 0.10483871      250kb
20          4    65 0.10483871      250kb
21          1   119 0.70833333      500kb
22          2    24 0.14285714      500kb
23          3    13 0.07738095      500kb
24          4    12 0.07142857      500kb
   Chimpanzee Human Shared resoultion stringency perc.shared    perc.h
1        1144  2040   4876       10kb          1  0.60496278 0.2531017
2         830  1558   4797       10kb          2  0.66764092 0.2168406
3         590  1178   4532       10kb          3  0.71936508 0.1869841
4         374   779   3987       10kb          4  0.77568093 0.1515564
5        1141  1476   3257       25kb          1  0.55447736 0.2512768
6         874  1043   3181       25kb          2  0.62397018 0.2045900
7         649   735   2993       25kb          3  0.68380169 0.1679232
8         436   433   2591       25kb          4  0.74884393 0.1251445
9         934  1076   1263       50kb          1  0.38588451 0.3287504
10        644   569   1189       50kb          2  0.49500416 0.2368859
11        432   290   1029       50kb          3  0.58766419 0.1656196
12        226   117    781       50kb          4  0.69483986 0.1040925
13        686   759    292      100kb          1  0.16810593 0.4369603
14        449   432    272      100kb          2  0.23590633 0.3746748
15        288   249    224      100kb          3  0.29434954 0.3272011
16        143   125    153      100kb          4  0.36342043 0.2969121
17        290   328     10      250kb          1  0.01592357 0.5222930
18        186   190      9      250kb          2  0.02337662 0.4935065
19        121   109      8      250kb          3  0.03361345 0.4579832
20         61    54      4      250kb          4  0.03361345 0.4537815
21         78    94      0      500kb          1  0.00000000 0.5465116
22         48    47      0      500kb          2  0.00000000 0.4947368
23         26    26      0      500kb          3  0.00000000 0.5000000
24         12    15      0      500kb          4  0.00000000 0.5555556
       perc.c
1  0.14193548
2  0.11551844
3  0.09365079
4  0.07276265
5  0.19424583
6  0.17143978
7  0.14827507
8  0.12601156
9  0.28536511
10 0.26810991
11 0.24671616
12 0.20106762
13 0.39493379
14 0.38941891
15 0.37844941
16 0.33966746
17 0.46178344
18 0.48311688
19 0.50840336
20 0.51260504
21 0.45348837
22 0.50526316
23 0.50000000
24 0.44444444

Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-15.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-16.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-17.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-18.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-19.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-20.png”

domain.plotter("loj", intra.ymax= 11000, inter.ymax=8200) #Alternative, highly similar, slightly less conserved.

             Chimpanzee Shared
inter.1.stat       1712   4798
inter.2.stat       1062   4642
inter.3.stat        709   4198
inter.4.stat        396   3361
             Chimpanzee Shared
inter.1.stat       1359   4396
inter.2.stat        921   4292
inter.3.stat        636   3941
inter.4.stat        389   3286
             Chimpanzee Shared
inter.1.stat       1486   4422
inter.2.stat       1055   4320
inter.3.stat        699   3955
inter.4.stat        387   3292
             Chimpanzee Shared
inter.1.stat       1395   4462
inter.2.stat        976   4350
inter.3.stat        667   4020
inter.4.stat        374   3318
             Human Shared
inter.1.stat  2484   4526
inter.2.stat  1794   4397
inter.3.stat  1279   4033
inter.4.stat   763   3310
             Human Shared
inter.1.stat  2537   4435
inter.2.stat  1850   4314
inter.3.stat  1286   3972
inter.4.stat   762   3263
             Human Shared
inter.1.stat  2128   4645
inter.2.stat  1495   4513
inter.3.stat  1138   4121
inter.4.stat   757   3395
             Human Shared
inter.1.stat  2418   4543
inter.2.stat  1827   4443
inter.3.stat  1297   4101
inter.4.stat   778   3307
             Chimpanzee Shared
inter.1.stat       1090   3479
inter.2.stat        858   3404
inter.3.stat        631   3203
inter.4.stat        409   2813
             Chimpanzee Shared
inter.1.stat       1022   3353
inter.2.stat        757   3273
inter.3.stat        589   3112
inter.4.stat        407   2741
             Chimpanzee Shared
inter.1.stat       1039   3438
inter.2.stat        823   3369
inter.3.stat        630   3193
inter.4.stat        406   2819
             Chimpanzee Shared
inter.1.stat       1037   3342
inter.2.stat        829   3292
inter.3.stat        631   3120
inter.4.stat        408   2759
             Human Shared
inter.1.stat  1366   3300
inter.2.stat  1055   3233
inter.3.stat   785   3049
inter.4.stat   507   2671
             Human Shared
inter.1.stat  1471   3210
inter.2.stat  1131   3153
inter.3.stat   798   2981
inter.4.stat   500   2609
             Human Shared
inter.1.stat  1273   3387
inter.2.stat   933   3296
inter.3.stat   709   3112
inter.4.stat   487   2724
             Human Shared
inter.1.stat  1436   3279
inter.2.stat  1122   3236
inter.3.stat   824   3072
inter.4.stat   494   2672
             Chimpanzee Shared
inter.1.stat        578   1645
inter.2.stat        469   1621
inter.3.stat        366   1531
inter.4.stat        240   1342
             Chimpanzee Shared
inter.1.stat        584   1698
inter.2.stat        425   1653
inter.3.stat        325   1551
inter.4.stat        232   1355
             Chimpanzee Shared
inter.1.stat        589   1635
inter.2.stat        489   1612
inter.3.stat        381   1529
inter.4.stat        244   1344
             Chimpanzee Shared
inter.1.stat        574   1650
inter.2.stat        474   1619
inter.3.stat        371   1531
inter.4.stat        235   1338
             Human Shared
inter.1.stat   721   1590
inter.2.stat   551   1557
inter.3.stat   412   1455
inter.4.stat   257   1276
             Human Shared
inter.1.stat   743   1573
inter.2.stat   587   1554
inter.3.stat   423   1454
inter.4.stat   258   1278
             Human Shared
inter.1.stat   656   1607
inter.2.stat   469   1555
inter.3.stat   367   1459
inter.4.stat   265   1277
             Human Shared
inter.1.stat   718   1580
inter.2.stat   571   1558
inter.3.stat   422   1479
inter.4.stat   252   1287
             Chimpanzee Shared
inter.1.stat        245    717
inter.2.stat        210    691
inter.3.stat        160    657
inter.4.stat        115    565
             Chimpanzee Shared
inter.1.stat        276    748
inter.2.stat        201    716
inter.3.stat        154    673
inter.4.stat        110    566
             Chimpanzee Shared
inter.1.stat        250    722
inter.2.stat        198    709
inter.3.stat        160    669
inter.4.stat        116    565
             Chimpanzee Shared
inter.1.stat        250    722
inter.2.stat        209    710
inter.3.stat        170    668
inter.4.stat        115    561
             Human Shared
inter.1.stat   379    686
inter.2.stat   276    670
inter.3.stat   192    632
inter.4.stat   108    535
             Human Shared
inter.1.stat   357    688
inter.2.stat   281    672
inter.3.stat   197    626
inter.4.stat   109    527
             Human Shared
inter.1.stat   310    722
inter.2.stat   228    696
inter.3.stat   165    655
inter.4.stat   102    550
             Human Shared
inter.1.stat   351    698
inter.2.stat   273    684
inter.3.stat   182    648
inter.4.stat   100    541
             Chimpanzee Shared
inter.1.stat         93    201
inter.2.stat         73    195
inter.3.stat         53    182
inter.4.stat         34    147
             Chimpanzee Shared
inter.1.stat         95    216
inter.2.stat         64    208
inter.3.stat         45    191
inter.4.stat         34    152
             Chimpanzee Shared
inter.1.stat         90    215
inter.2.stat         72    207
inter.3.stat         50    188
inter.4.stat         33    152
             Chimpanzee Shared
inter.1.stat         83    209
inter.2.stat         60    207
inter.3.stat         48    196
inter.4.stat         32    158
             Human Shared
inter.1.stat   127    209
inter.2.stat    94    203
inter.3.stat    67    187
inter.4.stat    40    156
             Human Shared
inter.1.stat   134    207
inter.2.stat   107    203
inter.3.stat    65    194
inter.4.stat    37    154
             Human Shared
inter.1.stat   128    203
inter.2.stat    88    196
inter.3.stat    59    184
inter.4.stat    39    148
             Human Shared
inter.1.stat   136    205
inter.2.stat   109    198
inter.3.stat    67    187
inter.4.stat    40    155
             Chimpanzee Shared
inter.1.stat         24     52
inter.2.stat         18     49
inter.3.stat         12     43
inter.4.stat          6     31
             Chimpanzee Shared
inter.1.stat         29     55
inter.2.stat         14     48
inter.3.stat         11     42
inter.4.stat          7     34
             Chimpanzee Shared
inter.1.stat         21     51
inter.2.stat         19     51
inter.3.stat         13     42
inter.4.stat          6     30
             Chimpanzee Shared
inter.1.stat         28     50
inter.2.stat         19     48
inter.3.stat         15     41
inter.4.stat          6     31
             Human Shared
inter.1.stat    41     51
inter.2.stat    25     44
inter.3.stat    17     40
inter.4.stat     6     31
             Human Shared
inter.1.stat    40     53
inter.2.stat    31     50
inter.3.stat    20     44
inter.4.stat     6     31
             Human Shared
inter.1.stat    43     48
inter.2.stat    24     44
inter.3.stat    15     39
inter.4.stat     7     32
             Human Shared
inter.1.stat    49     50
inter.2.stat    29     48
inter.3.stat    21     45
inter.4.stat     6     34
   indi.found count       perc resolution
1           1  3730 0.38174189       10kb
2           2   989 0.10121789       10kb
3           3  1417 0.14502098       10kb
4           4  3635 0.37201924       10kb
5           1  2003 0.32342968       25kb
6           2   582 0.09397707       25kb
7           3   831 0.13418376       25kb
8           4  2777 0.44840949       25kb
9           1  1016 0.33105246       50kb
10          2   298 0.09710003       50kb
11          3   400 0.13033561       50kb
12          4  1355 0.44151189       50kb
13          1   510 0.35465925      100kb
14          2   156 0.10848401      100kb
15          3   217 0.15090403      100kb
16          4   555 0.38595271      100kb
17          1   186 0.38993711      250kb
18          2    54 0.11320755      250kb
19          3    64 0.13417191      250kb
20          4   173 0.36268344      250kb
21          1    94 0.58385093      500kb
22          2    14 0.08695652      500kb
23          3    19 0.11801242      500kb
24          4    34 0.21118012      500kb
   indi.found count       perc resolution
1           1  3097 0.36133473       10kb
2           2   978 0.11410571       10kb
3           3  1216 0.14187376       10kb
4           4  3280 0.38268580       10kb
5           1  1523 0.26761553       25kb
6           2   534 0.09383237       25kb
7           3   675 0.11860833       25kb
8           4  2959 0.51994377       25kb
9           1   759 0.27010676       50kb
10          2   236 0.08398577       50kb
11          3   346 0.12313167       50kb
12          4  1469 0.52277580       50kb
13          1   359 0.28812199      100kb
14          2    98 0.07865169      100kb
15          3   167 0.13402889      100kb
16          4   622 0.49919743      100kb
17          1   152 0.36893204      250kb
18          2    37 0.08980583      250kb
19          3    57 0.13834951      250kb
20          4   166 0.40291262      250kb
21          1    57 0.46721311      500kb
22          2    14 0.11475410      500kb
23          3    16 0.13114754      500kb
24          4    35 0.28688525      500kb
   Chimpanzee Human Shared resoultion stringency perc.shared    perc.h
1        1488  2392   4528       10kb          1   0.5385347 0.2844910
2        1004  1742   4409       10kb          2   0.6162124 0.2434661
3         678  1250   4043       10kb          3   0.6771060 0.2093452
4         386   765   3316       10kb          4   0.7423327 0.1712559
5        1047  1386   3348       25kb          1   0.5791386 0.2397509
6         817  1060   3282       25kb          2   0.6361698 0.2054662
7         620   779   3105       25kb          3   0.6893872 0.1729574
8         408   497   2726       25kb          4   0.7507574 0.1368769
9         581   710   1622       50kb          1   0.5568143 0.2437350
10        464   544   1591       50kb          2   0.6121585 0.2093113
11        361   406   1499       50kb          3   0.6615181 0.1791703
12        238   258   1312       50kb          4   0.7256637 0.1426991
13        255   349    713      100kb          1   0.5413819 0.2649962
14        204   264    694      100kb          2   0.5972461 0.2271945
15        161   184    654      100kb          3   0.6546547 0.1841842
16        114   105    551      100kb          4   0.7155844 0.1363636
17         90   131    208      250kb          1   0.4848485 0.3053613
18         67   100    202      250kb          2   0.5474255 0.2710027
19         49    64    189      250kb          3   0.6258278 0.2119205
20         33    39    153      250kb          4   0.6800000 0.1733333
21         26    43     51      500kb          1   0.4250000 0.3583333
22         18    27     48      500kb          2   0.5161290 0.2903226
23         13    18     42      500kb          3   0.5753425 0.2465753
24          6     6     32      500kb          4   0.7272727 0.1363636
       perc.c
1  0.17697431
2  0.14032145
3  0.11354882
4  0.08641146
5  0.18111053
6  0.15836402
7  0.13765542
8  0.11236574
9  0.19945074
10 0.17853020
11 0.15931156
12 0.13163717
13 0.19362187
14 0.17555938
15 0.16116116
16 0.14805195
17 0.20979021
18 0.18157182
19 0.16225166
20 0.14666667
21 0.21666667
22 0.19354839
23 0.17808219
24 0.13636364

Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-21.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-22.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-23.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-24.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-25.png”

Past versions of “Domain Conservation with Individual Level Arrowhead Inferences-26.png”

Inter- and intra-species TAD Boundary Variance on Individual-level TopDom Inferences

###Intraspecies Variance in TAD Boundaries###
#First, I look at within-sepcies variance of TAD boundaries, both on the full set of boundaries and on the set that could be orthologously lifted over between species. This is done by taking the boundary files from both these situations across all individuals within a species, adding a column to identify the individual it came from, appending these files onto one another, and then using bedtools merge and collapsing on the identifier column to assess how many unique boundaries are found and how many individuals each is found in.

#Define a function to give back how many boundaries are found in X# individuals within a species, for the orthologously mappable TADs' boundaries.
intra.ortho.assess <- function(resolution, species){
  if(species=="H"){
    df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/h.final.allmerge.ortho.hg38", sep=""), data.table=F)
    df$F <- df$E <- df$B <- df$A <- 0
    df$A[grep("A", df$V4)] <- 1
    df$B[grep("B", df$V4)] <- 1
    df$E[grep("E", df$V4)] <- 1
    df$F[grep("F", df$V4)] <- 1
  }
  if(species=="C"){
    df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/c.final.allmerge.ortho.hg38", sep=""), data.table=F)
    df$H <- df$G <- df$D <- df$C <- 0
    df$H[grep("H", df$V4)] <- 1
    df$G[grep("G", df$V4)] <- 1
    df$D[grep("D", df$V4)] <- 1
    df$C[grep("C", df$V4)] <- 1
  }
  df$indi.found <- rowSums(df[,5:8])
  mydf <- as.data.frame(melt(table(df$indi.found)))
  mydf$resolution <- paste(resolution/1000, "kb", sep="")
  colnames(mydf) <- c("Individuals", "Count", "Resolution")
  return(list(mydf, df))
}

#Same function as above, but for the boundaries inferred within species w/out orthology filtering:
intra.full.assess <- function(resolution, species){
  if(species=="H"){
    df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/h.final.allmerge.hg38", sep=""), data.table=F)
    df$F <- df$E <- df$B <- df$A <- 0
    df$A[grep("A", df$V4)] <- 1
    df$B[grep("B", df$V4)] <- 1
    df$E[grep("E", df$V4)] <- 1
    df$F[grep("F", df$V4)] <- 1
  }
  if(species=="C"){
    df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/c.final.allmerge.panTro5", sep=""), data.table=F)
    df$H <- df$G <- df$D <- df$C <- 0
    df$H[grep("H", df$V4)] <- 1
    df$G[grep("G", df$V4)] <- 1
    df$D[grep("D", df$V4)] <- 1
    df$C[grep("C", df$V4)] <- 1
  }
  df$indi.found <- rowSums(df[,5:8])
  mydf <- as.data.frame(melt(table(df$indi.found)))
  mydf$resolution <- paste(resolution/1000, "kb", sep="")
  colnames(mydf) <- c("Individuals", "Count", "Resolution")
  return(list(mydf, df))
}

#For plotting stats on orthologous boundaries.
intra.ortho.plotter <- function(species, y.max){
  bounds.10 <- intra.ortho.assess(10000, species)[[1]]
  bounds.25 <- intra.ortho.assess(25000, species)[[1]]
  bounds.50 <- intra.ortho.assess(50000, species)[[1]]
  bounds.100 <- intra.ortho.assess(100000, species)[[1]]
  bounds.250 <- intra.ortho.assess(250000, species)[[1]]
  bounds.500 <- intra.ortho.assess(500000, species)[[1]]
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  print(ggbounds)
  if(species=="H"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Humans") +xlab("Resolution of Analysis") + ylab("Boundary Count") + guides(fill=guide_legend(title="# Individuals")) + coord_cartesian(ylim=c(0, y.max)) + guides(fill=guide_legend(title="# Individuals"))
  }
   if(species=="C"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Chimpanzees") +xlab("Resolution of Analysis") + ylab("Boundary Count") + guides(fill=guide_legend(title="# Individuals")) + coord_cartesian(ylim=c(0, y.max)) + guides(fill=guide_legend(title="# Individuals"))
   }
  print(myplot)
}

#Same as above, but for the set of boundaries without orthology filtering.
intra.full.plotter <- function(species){
  bounds.10 <- intra.full.assess(10000, species)[[1]]
  bounds.25 <- intra.full.assess(25000, species)[[1]]
  bounds.50 <- intra.full.assess(50000, species)[[1]]
  bounds.100 <- intra.full.assess(100000, species)[[1]]
  bounds.250 <- intra.full.assess(250000, species)[[1]]
  bounds.500 <- intra.full.assess(500000, species)[[1]]
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  
  if(species=="H"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Humans") +xlab("Resolution of Analysis") + ylab("Boundary Count")+ guides(fill=guide_legend(title="# Individuals"))
  }
   if(species=="C"){
    myplot <- ggplot(data=ggbounds, aes(x=Resolution, group=Resolution, y=Count, fill=as.factor(Individuals))) + geom_bar(stat="identity") + ggtitle("Intraspecies TAD Boundary Variance, Chimps") +xlab("Resolution of Analysis") + ylab("Boundary Count")+ guides(fill=guide_legend(title="# Individuals"))
   }
  print(myplot)
}

intra.ortho.plotter("H", 2100)

   Individuals Count Resolution
1            1  1069       10kb
2            2   404       10kb
3            3   203       10kb
4            4   185       10kb
5            1   266       25kb
6            2   157       25kb
7            3   114       25kb
8            4   214       25kb
9            1   202       50kb
10           2   104       50kb
11           3    86       50kb
12           4   160       50kb
13           1   125      100kb
14           2    63      100kb
15           3    54      100kb
16           4    77      100kb
17           1    42      250kb
18           2    26      250kb
19           3    15      250kb
20           4    20      250kb
21           1    23      500kb
22           2    10      500kb
23           3     4      500kb
24           4     7      500kb

Past versions of “Boundary Inferences using Individual TopDom Data-1.png”

intra.ortho.plotter("C", 2100)

   Individuals Count Resolution
1            1  1064       10kb
2            2   400       10kb
3            3   255       10kb
4            4   356       10kb
5            1   233       25kb
6            2   146       25kb
7            3   122       25kb
8            4   265       25kb
9            1   159       50kb
10           2    84       50kb
11           3    68       50kb
12           4   195       50kb
13           1    86      100kb
14           2    62      100kb
15           3    54      100kb
16           4    88      100kb
17           1    50      250kb
18           2    23      250kb
19           3    16      250kb
20           4    19      250kb
21           1    20      500kb
22           2    10      500kb
23           3    11      500kb
24           4    10      500kb

Past versions of “Boundary Inferences using Individual TopDom Data-2.png”

#intra.full.plotter("H")
#intra.full.plotter("C")

##Secondary method for assessing intraspecies variance in TAD boundaries, Jaccard index.###
jac.intra.full <- function(resolution, species){
  if(species=="H"){
    AB <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AB.full", sep=""))[1,3])
    AE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AE.full", sep=""))[1,3])
    AF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AF.full", sep=""))[1,3])
    BE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BE.full", sep=""))[1,3])
    BF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BF.full", sep=""))[1,3])
    EF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EF.full", sep=""))[1,3])
    jaccard <- data.frame(A=c(1, AB, AE, AF), B=c(AB, 1, BE, BF), E=c(AE, BE, 1, EF), F=c(AF, BF, EF, 1))
    rownames(jaccard) <- colnames(jaccard)
  }
  if(species=="C"){
    CD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CD.full", sep=""))[1,3])
    CG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CG.full", sep=""))[1,3])
    CH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CH.full", sep=""))[1,3])
    DG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DG.full", sep=""))[1,3])
    DH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DH.full", sep=""))[1,3])
    GH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.GH.full", sep=""))[1,3])
    jaccard <- data.frame(C=c(1, CD, CG, CH), D=c(CD, 1, DG, DH), G=c(CG, DG, 1, GH), H=c(CH, DH, GH, 1))
    rownames(jaccard) <- colnames(jaccard) 
  }
  return(jaccard)
}

#Same as the above function, but only on orthologous TAD boundaries:
jac.intra.ortho <- function(resolution, species){
  if(species=="H"){
    AB <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AB.ortho", sep=""))[1,3])
    AE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AE.ortho", sep=""))[1,3])
    AF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AF.ortho", sep=""))[1,3])
    BE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BE.ortho", sep=""))[1,3])
    BF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BF.ortho", sep=""))[1,3])
    EF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EF.ortho", sep=""))[1,3])
    jaccard <- data.frame(A=c(1, AB, AE, AF), B=c(AB, 1, BE, BF), E=c(AE, BE, 1, EF), F=c(AF, BF, EF, 1))
    rownames(jaccard) <- colnames(jaccard)
  }
  if(species=="C"){
    CD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CD.ortho", sep=""))[1,3])
    CG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CG.ortho", sep=""))[1,3])
    CH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CH.ortho", sep=""))[1,3])
    DG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DG.ortho", sep=""))[1,3])
    DH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DH.ortho", sep=""))[1,3])
    GH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.GH.ortho", sep=""))[1,3])
    jaccard <- data.frame(C=c(1, CD, CG, CH), D=c(CD, 1, DG, DH), G=c(CG, DG, 1, GH), H=c(CH, DH, GH, 1))
    rownames(jaccard) <- colnames(jaccard) 
  }
  return(jaccard)
}

#Can be clustered upon later.
jac.intra.full(10000, "H")

         A        B        E        F
A 1.000000 0.379610 0.303561 0.329188
B 0.379610 1.000000 0.287836 0.342339
E 0.303561 0.287836 1.000000 0.400728
F 0.329188 0.342339 0.400728 1.000000

jac.intra.ortho(10000, "H")

         A        B        E        F
A 1.000000 0.368436 0.282066 0.297989
B 0.368436 1.000000 0.273265 0.320928
E 0.282066 0.273265 1.000000 0.389707
F 0.297989 0.320928 0.389707 1.000000

jac.intra.full(10000, "C")

         C        D        G        H
C 1.000000 0.374851 0.445536 0.418546
D 0.374851 1.000000 0.380949 0.387779
G 0.445536 0.380949 1.000000 0.438128
H 0.418546 0.387779 0.438128 1.000000

jac.intra.ortho(10000, "C")

         C        D        G        H
C 1.000000 0.368436 0.437756 0.412817
D 0.368436 1.000000 0.374234 0.384674
G 0.437756 0.374234 1.000000 0.436316
H 0.412817 0.384674 0.436316 1.000000

###Interspecies Conservation of TAD Boundaries###
#This is performed in much the same manner as above, but this time, combining the file across species.
#Once again, checked, and merging before/after individual file merging makes no difference.
inter.bound.cons <- function(resolution, clust=F){
  df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/final.merged.combined.each.merge.ortho.hg38", sep=""), data.table=F)
  df$H <- df$G <- df$F <- df$E <- df$D <- df$C <- df$B <- df$A <- 0
  df$A[grep("A", df$V4)] <- 1
  df$B[grep("B", df$V4)] <- 1
  df$C[grep("C", df$V4)] <- 1
  df$D[grep("D", df$V4)] <- 1
  df$E[grep("E", df$V4)] <- 1
  df$F[grep("F", df$V4)] <- 1
  df$G[grep("G", df$V4)] <- 1
  df$H[grep("H", df$V4)] <- 1
  df$found_in_H <- rowSums(df[,c(5:6, 9:10)])
  df$found_in_C <- rowSums(df[,c(7:8, 11:12)])
  df.2 <- filter(df, found_in_H>=2|found_in_C>=2)
  df.3 <- filter(df, found_in_H>=3|found_in_C>=3)
  df.4 <- filter(df, found_in_H>=4|found_in_C>=4)
  cons.1 <- ifelse(df$found_in_H>=1&df$found_in_C>=1, "Shared", ifelse(df$found_in_H>=1, "Human", "Chimpanzee"))
  cons.2 <- ifelse(df.2$found_in_H>=1&df.2$found_in_C>=1, "Shared", ifelse(df.2$found_in_H>=1, "Human", "Chimpanzee"))
  cons.3 <- ifelse(df.3$found_in_H>=1&df.3$found_in_C>=1, "Shared", ifelse(df.3$found_in_H>=1, "Human", "Chimpanzee"))
  cons.4 <- ifelse(df.4$found_in_H>=1&df.4$found_in_C>=1, "Shared", ifelse(df.4$found_in_H>=1, "Human", "Chimpanzee"))
  cons.table <- as.data.frame(rbind(table(factor(cons.1, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.2, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.3, levels=c("Shared", "Human", "Chimpanzee"))), table(factor(cons.4, levels=c("Shared", "Human", "Chimpanzee")))))
  cons.table$stringency <- 1:4
  cons.table$resolution <- paste(resolution/1000, "kb", sep="")
  if(clust==FALSE){
    return(cons.table)}
  if(clust==TRUE){
    return(df)
  }
}

boundary.inter.plot <- function(y.max){
  bounds.10 <- inter.bound.cons(10000)
  bounds.25 <- inter.bound.cons(25000)
  bounds.50 <- inter.bound.cons(50000)
  bounds.100 <- inter.bound.cons(100000)
  bounds.250 <- inter.bound.cons(250000)
  bounds.500 <- inter.bound.cons(500000)
  ggbounds <- rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500)
  ggbounds$H.perc <- ggbounds$Human/rowSums(ggbounds[,1:3])
  ggbounds$C.perc <- ggbounds$Human/rowSums(ggbounds[,1:3])
  ggbounds$shared.perc <- ggbounds$Shared/rowSums(ggbounds[,1:3])
  print(ggbounds)
  ggbounds$resolution <- factor(ggbounds$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
  ggbounds.1 <- filter(ggbounds, stringency==1) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.2 <- filter(ggbounds, stringency==2) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.3 <- filter(ggbounds, stringency==3) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  ggbounds.4 <- filter(ggbounds, stringency==4) %>% select(., Human, Chimpanzee, Shared, resolution) %>% melt(., by="resolution")
  plot.1 <- ggplot(data=ggbounds.1, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=1") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#619CFF", "#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared"))+ coord_cartesian(ylim=c(0, y.max))
  plot.2 <- ggplot(data=ggbounds.2, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=2") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#619CFF", "#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared"))+ coord_cartesian(ylim=c(0, y.max))
  plot.3 <- ggplot(data=ggbounds.3, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=3") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery"))+ theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#619CFF", "#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared"))+ coord_cartesian(ylim=c(0, y.max))
  plot.4 <- ggplot(data=ggbounds.4, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies TAD Boundary Conservation, Stringency=4") + xlab("Resolution of Analysis") + ylab("TAD Boundary Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#619CFF", "#00BA38", "#F8766D"), labels=c("Human", "Chimpanzee", "Shared")) + coord_cartesian(ylim=c(0, y.max))
  print(plot.1)
  print(plot.2)
  print(plot.3)
  print(plot.4)
}
boundary.inter.plot(3100) #FigS14C-D

   Shared Human Chimpanzee stringency resolution     H.perc     C.perc
1     841   998       1193          1       10kb 0.32915567 0.32915567
2     665   286        469          2       10kb 0.20140845 0.20140845
3     454    98        246          3       10kb 0.12280702 0.12280702
4     273    38        138          4       10kb 0.08463252 0.08463252
5     434   297        316          1       25kb 0.28366762 0.28366762
6     397   135        165          2       25kb 0.19368723 0.19368723
7     340    71         96          3       25kb 0.14003945 0.14003945
8     256    44         61          4       25kb 0.12188366 0.12188366
9     319   218        175          1       50kb 0.30617978 0.30617978
10    286   104         89          2       50kb 0.21711900 0.21711900
11    243    60         48          3       50kb 0.17094017 0.17094017
12    196    33         28          4       50kb 0.12840467 0.12840467
13    183   127        103          1      100kb 0.30750605 0.30750605
14    169    61         57          2      100kb 0.21254355 0.21254355
15    136    30         32          3      100kb 0.15151515 0.15151515
16     91    11         18          4      100kb 0.09166667 0.09166667
17     51    51         57          1      250kb 0.32075472 0.32075472
18     42    28         29          2      250kb 0.28282828 0.28282828
19     32    15         13          3      250kb 0.25000000 0.25000000
20     20     7          5          4      250kb 0.21875000 0.21875000
21     18    26         33          1      500kb 0.33766234 0.33766234
22     17     7         18          2      500kb 0.16666667 0.16666667
23     13     4          9          3      500kb 0.15384615 0.15384615
24      9     2          3          4      500kb 0.14285714 0.14285714
   shared.perc
1    0.2773747
2    0.4683099
3    0.5689223
4    0.6080178
5    0.4145177
6    0.5695839
7    0.6706114
8    0.7091413
9    0.4480337
10   0.5970772
11   0.6923077
12   0.7626459
13   0.4430993
14   0.5888502
15   0.6868687
16   0.7583333
17   0.3207547
18   0.4242424
19   0.5333333
20   0.6250000
21   0.2337662
22   0.4047619
23   0.5000000
24   0.6428571

Using resolution as id variables
Using resolution as id variables
Using resolution as id variables
Using resolution as id variables

Past versions of “Boundary Inferences using Individual TopDom Data-3.png”

Past versions of “Boundary Inferences using Individual TopDom Data-4.png”

Past versions of “Boundary Inferences using Individual TopDom Data-5.png”

Past versions of “Boundary Inferences using Individual TopDom Data-6.png”

#For boundary clustering
bounds.indi.clust <- function(resolution){
  bounds <- inter.bound.cons(resolution, clust=TRUE)
  heat <- percentage.table.calc(bounds[,5:12])
  colnames(heat) <- rownames(heat) <- c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2")
  heatmaply(heat, main=paste("Pairwise Proportions of Shared TAD Boundaries @ ", resolution/1000, "kb", sep=""), k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))
}

#FigS14F, boundary clustering on individual basis with TopDom inferences
bounds.indi.clust(10000)

bounds.indi.clust(25000)

bounds.indi.clust(50000)

bounds.indi.clust(100000)

bounds.indi.clust(250000)

bounds.indi.clust(500000)

#Now, Jaccard indices for interspecies variation in TAD boundaries#
jac.inter <- function(resolution){
  AB <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AB.ortho", sep=""))[1,3])
  AC <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AC.ortho", sep=""))[1,3])
  AD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AD.ortho", sep=""))[1,3])
  AE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AE.ortho", sep=""))[1,3])
  AF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AF.ortho", sep=""))[1,3])
  AG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AG.ortho", sep=""))[1,3])
  AH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.AH.ortho", sep=""))[1,3])
  BC <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BC.ortho", sep=""))[1,3])
  BD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BD.ortho", sep=""))[1,3])
  BE <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BE.ortho", sep=""))[1,3])
  BF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BF.ortho", sep=""))[1,3])
  BG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BG.ortho", sep=""))[1,3])
  BH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.BH.ortho", sep=""))[1,3])
  CD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CD.ortho", sep=""))[1,3])
  CG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CG.ortho", sep=""))[1,3])
  CH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.CH.ortho", sep=""))[1,3])
  DG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DG.ortho", sep=""))[1,3])
  DH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.DH.ortho", sep=""))[1,3])
  EC <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EC.ortho", sep=""))[1,3])
  ED <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.ED.ortho", sep=""))[1,3])
  EF <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EF.ortho", sep=""))[1,3])
  EG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EG.ortho", sep=""))[1,3])
  EH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.EH.ortho", sep=""))[1,3])
  FC <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.FC.ortho", sep=""))[1,3])
  FD <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.FD.ortho", sep=""))[1,3])
  FG <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.FG.ortho", sep=""))[1,3])
  FH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.FH.ortho", sep=""))[1,3])
  GH <- as.numeric(fread(paste("data/TADs/TopDom/", resolution, "_compare/jac.GH.ortho", sep=""))[1,3])
  jaccard <- data.frame(A=c(1, AB, AC, AD, AE, AF, AG, AH), B=c(AB, 1, BC, BD, BE, BF, BG, BH), C=c(AC, BC, 1, CD, EC, FC, CG, CH), D=c(AD, BD, CD, 1, ED, FD, DG, DH), E=c(AE, BE, EC, ED, 1, EF, EG, EH), F=c(AF, BF, FC, FD, EF, 1, FG, FH), G=c(AG, BG, CG, DG, EG, FG, 1, GH), H=c(AH, BH, CH, DH, EH, FH, GH, 1))
  rownames(jaccard) <- colnames(jaccard)
  return(jaccard)
}
jac.inter(10000) #Can be clustered upon later.

         A        B        C        D        E        F        G        H
A 1.000000 0.368436 0.164530 0.176989 0.282066 0.297989 0.177578 0.180857
B 0.368436 1.000000 0.162524 0.175498 0.273265 0.320928 0.184697 0.190072
C 0.164530 0.162524 1.000000 0.368436 0.207875 0.203086 0.437756 0.412817
D 0.176989 0.175498 0.368436 1.000000 0.199992 0.194232 0.374234 0.384674
E 0.282066 0.273265 0.207875 0.199992 1.000000 0.389707 0.221492 0.232747
F 0.297989 0.320928 0.203086 0.194232 0.389707 1.000000 0.211308 0.220979
G 0.177578 0.184697 0.437756 0.374234 0.221492 0.211308 1.000000 0.436316
H 0.180857 0.190072 0.412817 0.384674 0.232747 0.220979 0.436316 1.000000

Inter- and intra-species TAD Body Variance on Individual-level TopDom Inferences

domain.compare <- function(resolution, letter, species, type, clust=F){
  df <- fread(paste("data/TADs/TopDom/", resolution, "_compare/inter.", letter, ".", type, sep=""))
  df$ID <- paste(df$V1, df$V2, df$V3, sep="-")
  if(type=="rao"){
  df$size <- df$V3-df$V2
  df$dist_max <- ifelse((df$size*.2)<=50000, df$size*.2, 50000) #Use 0.2, not 0.5, these are closely related species.
  df$conserved <- ifelse((((df$V2-df$V6)^2+(df$V3-df$V7)^2)^0.5)<df$dist_max, "yes", "no")
  merged.df <- as.data.frame(group_by(df, ID) %>% summarise(individuals=paste(V4[which(conserved=="yes")], collapse=",")))}
  if(type=="loj"){
    merged.df <- as.data.frame(group_by(df, ID) %>% summarise(individuals=paste(V4, collapse=",")))
  }
  merged.df$H <- merged.df$G <- merged.df$F <- merged.df$E <- merged.df$D <- merged.df$C <- merged.df$B <- merged.df$A <- 0
  merged.df$A[grep("A", merged.df$individuals)] <- 1
  merged.df$B[grep("B", merged.df$individuals)] <- 1
  merged.df$E[grep("E", merged.df$individuals)] <- 1
  merged.df$F[grep("F", merged.df$individuals)] <- 1
  merged.df$C[grep("C", merged.df$individuals)] <- 1
  merged.df$D[grep("D", merged.df$individuals)] <- 1
  merged.df$G[grep("G", merged.df$individuals)] <- 1
  merged.df$H[grep("H", merged.df$individuals)] <- 1
  merged.df$found_in_H <- rowSums(merged.df[,c(3, 4, 7, 8)])
  merged.df$found_in_C <- rowSums(merged.df[,c(5, 6, 9, 10)])
  merged.df$cons <- ifelse(merged.df$found_in_C>=1&merged.df$found_in_H>=1, "Shared", ifelse(merged.df$found_in_C==0, "Human", "Chimpanzee"))
  
  if(species=="H"){
    intra.stat <- table(merged.df$found_in_H)
    inter.1.stat <- table(factor(merged.df$cons, levels=c("Human", "Shared")))
    inter.2.stat <- table(factor(filter(merged.df, found_in_H>=2|found_in_C>=2)$cons, levels=c("Human", "Shared")))
    inter.3.stat <- table(factor(filter(merged.df, found_in_H>=3|found_in_C>=3)$cons, levels=c("Human", "Shared")))
    inter.4.stat <- table(factor(filter(merged.df, found_in_H>=4|found_in_C>=4)$cons, levels=c("Human", "Shared")))}
  if(species=="C"){
    intra.stat <- table(merged.df$found_in_C)
    inter.1.stat <- table(factor(merged.df$cons, levels=c("Chimpanzee", "Shared")))
    inter.2.stat <- table(factor(filter(merged.df, found_in_H>=2|found_in_C>=2)$cons, levels=c("Chimpanzee", "Shared")))
    inter.3.stat <- table(factor(filter(merged.df, found_in_H>=3|found_in_C>=3)$cons, levels=c("Chimpanzee", "Shared")))
    inter.4.stat <- table(factor(filter(merged.df, found_in_H>=4|found_in_C>=4)$cons, levels=c("Chimpanzee", "Shared")))}
  
  conservation <- as.data.frame(rbind(inter.1.stat, inter.2.stat, inter.3.stat, inter.4.stat))
  conservation$resolution <- paste(resolution/1000, "kb", sep="")
  conservation$stringency <- 1:4
  if(clust==F){
    return(list(intra.stat, conservation))}
  if(clust==TRUE){
    perc.table <- percentage.table.calc(merged.df[,3:10])
    return(perc.table[,letter])
  }
}

###Interspecies domain clustering:
indi.TAD.clust <- function(type, resolution){
  A.domain <- domain.compare(resolution, "A", "H", type, clust=TRUE)
  B.domain <- domain.compare(resolution, "B", "H", type, clust=TRUE)
  C.domain <- domain.compare(resolution, "C", "C", type, clust=TRUE)
  D.domain <- domain.compare(resolution, "D", "C", type, clust=TRUE)
  E.domain <- domain.compare(resolution, "E", "H", type, clust=TRUE)
  F.domain <- domain.compare(resolution, "F", "H", type, clust=TRUE)
  G.domain <- domain.compare(resolution, "G", "C", type, clust=TRUE)
  H.domain <- domain.compare(resolution, "H", "C", type, clust=TRUE)
  indi.TAD.df <- rbind(A.domain, B.domain, C.domain, D.domain, E.domain, F.domain, G.domain, H.domain)
  colnames(indi.TAD.df) <- rownames(indi.TAD.df) <- c("H_F1", "H_M1", "C_M1", "C_F1", "H_M2", "H_F2", "C_M2", "C_F2")
  heatmaply(indi.TAD.df, main=paste("Pairwise Proportions of Shared TADs @ ", resolution/1000, "kb", sep=""), k_row=2, k_col=2, symm=TRUE, margins=c(50, 50, 30, 30))
}

#FIgS14E alternative TAD clustering on individual basis, TopDom!
options(scipen=999)
indi.TAD.clust("loj", 10000)

indi.TAD.clust("loj", 25000)

indi.TAD.clust("loj", 50000)

indi.TAD.clust("loj", 100000)

indi.TAD.clust("loj", 250000)

indi.TAD.clust("loj", 500000)

#Used this for FIgS14E, since others shown are Rao methodology.
indi.TAD.clust("rao", 10000)

indi.TAD.clust("rao", 25000)

indi.TAD.clust("rao", 50000)

indi.TAD.clust("rao", 100000)

indi.TAD.clust("rao", 250000)

indi.TAD.clust("rao", 500000)

concatenator <- function(resolution, type) {
  A <- domain.compare(resolution, "A", "H", type)
  B <- domain.compare(resolution, "B", "H", type)
  E <- domain.compare(resolution, "E", "H", type)
  F <- domain.compare(resolution, "F", "H", type)
    intra.H <- data.frame(indi.found=1:4, count=c(sum(A[[1]][1], B[[1]][1], E[[1]][1], F[[1]][1]), mean(A[[1]][2], B[[1]][2], E[[1]][2], F[[1]][2]), mean(A[[1]][3], B[[1]][3], E[[1]][3], F[[1]][3]), mean(A[[1]][4], B[[1]][4], E[[1]][4], F[[1]][4])))
  
  C <- domain.compare(resolution, "C", "C", type)
  D <- domain.compare(resolution, "D", "C", type)
  G <- domain.compare(resolution, "G", "C", type)
  H <- domain.compare(resolution, "H", "C", type)
    intra.C <- data.frame(indi.found=1:4, count=c(sum(C[[1]][1], D[[1]][1], G[[1]][1], H[[1]][1]), mean(C[[1]][2], D[[1]][2], G[[1]][2], H[[1]][2]), mean(C[[1]][3], D[[1]][3], G[[1]][3], H[[1]][3]), mean(C[[1]][4], D[[1]][4], G[[1]][4], H[[1]][4])))
  intra.H$resolution <-intra.C$resolution <- paste(resolution/1000, "kb", sep="")
  inter.c <- ((C[[2]][,1:2] + D[[2]][,1:2] + G[[2]][,1:2] + H[[2]][,1:2])/4)
  inter.h <- ((A[[2]][,1:2] + B[[2]][,1:2] + E[[2]][,1:2] + F[[2]][,1:2])/4)
  inter <- as.data.frame(cbind(round(inter.c[,1]), round(inter.h[,1]), round((inter.c[,2]+inter.h[,2])/2)))
  colnames(inter) <- c("Chimpanzee", "Human", "Shared")
  inter$resoultion <- paste(resolution/1000, "kb", sep="")
  inter$stringency <- 1:4
  return(list(intra.H, intra.C, inter))
}

domain.plotter <- function(type, inter.ymax, intra.ymax){
    intra.10 <- concatenator(10000, type)
    intra.25 <- concatenator(25000, type)
    intra.50 <- concatenator(50000, type)
    intra.100 <- concatenator(100000, type)
    intra.250 <- concatenator(250000, type)
    intra.500 <- concatenator(500000, type)
    
    intra.h <- rbind(intra.10[[1]], intra.25[[1]], intra.50[[1]], intra.100[[1]], intra.250[[1]], intra.500[[1]])
    intra.h$resolution <- factor(intra.h$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    intra.h.plot <- ggplot(data=intra.h, aes(x=resolution, group=resolution, y=count, fill=as.factor(indi.found))) + geom_bar(stat="identity") + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Intraspecies TAD Variance, Humans") + guides(fill=guide_legend(title="# Individuals")) + theme(plot.title=element_text(hjust=0.3)) + coord_cartesian(ylim=c(0, intra.ymax))
    print(intra.h)
    
    intra.c <- rbind(intra.10[[2]], intra.25[[2]], intra.50[[2]], intra.100[[2]], intra.250[[2]], intra.500[[2]])
    intra.c$resolution <- factor(intra.c$resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    intra.c.plot <- ggplot(data=intra.c, aes(x=resolution, group=resolution, y=count, fill=as.factor(indi.found))) + geom_bar(stat="identity") + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Intraspecies TAD Variance, Chimpanzees") + guides(fill=guide_legend(title="# Individuals")) + theme(plot.title=element_text(hjust=0.3)) + coord_cartesian(ylim=c(0, intra.ymax))
    print(intra.c)
    
    inter <- rbind(intra.10[[3]], intra.25[[3]], intra.50[[3]], intra.100[[3]], intra.250[[3]], intra.500[[3]])
    inter$perc.shared <- inter$Shared/rowSums(inter[,1:3])
    inter$perc.h <- inter$Human/rowSums(inter[,1:3])
    inter$perc.c <- inter$Chimpanzee/rowSums(inter[,1:3])
    print(inter)
    inter <- inter[,-6:-8]
    inter.1 <- filter(inter, stringency==1)[,-5] %>% melt(., by="resolution")
    inter.1$resolution <- factor(inter.1$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.2 <- filter(inter, stringency==2)[,-5] %>% melt(., by="resolution")
    inter.2$resolution <- factor(inter.2$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.3 <- filter(inter, stringency==3)[,-5] %>% melt(., by="resolution")
    inter.3$resolution <- factor(inter.3$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    inter.4 <- filter(inter, stringency==4)[,-5] %>% melt(., by="resolution")
    inter.4$resolution <- factor(inter.4$resoultion, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
    
    plot.1 <- ggplot(data=inter.1, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=1") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.2 <- ggplot(data=inter.2, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=2") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.3 <- ggplot(data=inter.3, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=3") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    plot.4 <- ggplot(data=inter.4, aes(x=resolution, group=resolution, y=value, fill=variable)) + geom_bar(stat="identity") + ggtitle("Interspecies Domain Conservation, Stringency=4") + xlab("Resolution of Analysis") + ylab("Domain Count") + guides(fill=guide_legend(title="Species of Discovery")) + theme(plot.title=element_text(hjust=0.3)) + scale_fill_manual(name="Conservation", values=c("#00BA38",  "#619CFF", "#F8766D"), labels=c("Chimpanzee", "Human", "Shared")) + coord_cartesian(ylim=c(0, inter.ymax))
    print(intra.h.plot)
    print(intra.c.plot)
    print(plot.1)
    print(plot.2)
    print(plot.3)
    print(plot.4)
}

domain.plotter("rao", intra.ymax=12000, inter.ymax=12000) #FIgS14A-B

   indi.found count resolution
1           1  3368       10kb
2           2   954       10kb
3           3  1211       10kb
4           4  3335       10kb
5           1  1876       25kb
6           2   563       25kb
7           3   577       25kb
8           4  1214       25kb
9           1  2310       50kb
10          2   377       50kb
11          3   351       50kb
12          4   324       50kb
13          1  1270      100kb
14          2   239      100kb
15          3   186      100kb
16          4   208      100kb
17          1   441      250kb
18          2    86      250kb
19          3    64      250kb
20          4    84      250kb
21          1   222      500kb
22          2    35      500kb
23          3    36      500kb
24          4    36      500kb
   indi.found count resolution
1           1  3718       10kb
2           2  1025       10kb
3           3  1461       10kb
4           4  5170       10kb
5           1  1473       25kb
6           2   450       25kb
7           3   627       25kb
8           4  1721       25kb
9           1  1308       50kb
10          2   330       50kb
11          3   366       50kb
12          4   663       50kb
13          1  1020      100kb
14          2   194      100kb
15          3   211      100kb
16          4   276      100kb
17          1   443      250kb
18          2    82      250kb
19          3    62      250kb
20          4   118      250kb
21          1   187      500kb
22          2    46      500kb
23          3    38      500kb
24          4    48      500kb
   Chimpanzee Human Shared resoultion stringency perc.shared    perc.h
1        5319  3136   3141       10kb          1  0.27086927 0.2704381
2        4558  2498   3062       10kb          2  0.30262898 0.2468867
3        3849  1918   2862       10kb          3  0.33167227 0.2222737
4        2932  1281   2477       10kb          4  0.37025411 0.1914798
5        1717  1450   1374       25kb          1  0.30257652 0.3193129
6        1438  1098   1336       25kb          2  0.34504132 0.2835744
7        1157   776   1210       25kb          3  0.38498250 0.2468979
8         826   481    973       25kb          4  0.42675439 0.2109649
9         939   978    690       50kb          1  0.26467204 0.3751438
10        708   575    642       50kb          2  0.33350649 0.2987013
11        513   352    526       50kb          3  0.37814522 0.2530554
12        326   173    375       50kb          4  0.42906178 0.1979405
13        742   746    202      100kb          1  0.11952663 0.4414201
14        530   469    183      100kb          2  0.15482234 0.3967851
15        368   274    141      100kb          3  0.18007663 0.3499361
16        211   143     87      100kb          4  0.19727891 0.3242630
17        378   360     14      250kb          1  0.01861702 0.4787234
18        270   252     12      250kb          2  0.02247191 0.4719101
19        184   158      8      250kb          3  0.02285714 0.4514286
20        111    80      6      250kb          4  0.03045685 0.4060914
21        178   167      3      500kb          1  0.00862069 0.4798851
22        131   112      3      500kb          2  0.01219512 0.4552846
23         89    74      2      500kb          3  0.01212121 0.4484848
24         48    35      1      500kb          4  0.01190476 0.4166667
      perc.c
1  0.4586927
2  0.4504843
3  0.4460540
4  0.4382661
5  0.3781105
6  0.3713843
7  0.3681196
8  0.3622807
9  0.3601841
10 0.3677922
11 0.3687994
12 0.3729977
13 0.4390533
14 0.4483926
15 0.4699872
16 0.4784580
17 0.5026596
18 0.5056180
19 0.5257143
20 0.5634518
21 0.5114943
22 0.5325203
23 0.5393939
24 0.5714286

Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables

Past versions of “Domain Inferences Using Individual TopDom Data-13.png”

Past versions of “Domain Inferences Using Individual TopDom Data-14.png”

Past versions of “Domain Inferences Using Individual TopDom Data-15.png”

Past versions of “Domain Inferences Using Individual TopDom Data-16.png”

Past versions of “Domain Inferences Using Individual TopDom Data-17.png”

Past versions of “Domain Inferences Using Individual TopDom Data-18.png”

domain.plotter("loj", intra.ymax=12000, inter.ymax=12000) 

   indi.found count resolution
1           1  4023       10kb
2           2  1125       10kb
3           3  1245       10kb
4           4  2956       10kb
5           1  1495       25kb
6           2   489       25kb
7           3   524       25kb
8           4  1444       25kb
9           1   940       50kb
10          2   281       50kb
11          3   340       50kb
12          4   795       50kb
13          1   630      100kb
14          2   165      100kb
15          3   199      100kb
16          4   432      100kb
17          1   200      250kb
18          2    63      250kb
19          3    57      250kb
20          4   184      250kb
21          1   123      500kb
22          2    18      500kb
23          3    29      500kb
24          4    83      500kb
   indi.found count resolution
1           1  5059       10kb
2           2  1265       10kb
3           3  1664       10kb
4           4  4394       10kb
5           1  1411       25kb
6           2   423       25kb
7           3   560       25kb
8           4  1835       25kb
9           1   738       50kb
10          2   215       50kb
11          3   322       50kb
12          4   951       50kb
13          1   500      100kb
14          2   138      100kb
15          3   192      100kb
16          4   488      100kb
17          1   250      250kb
18          2    52      250kb
19          3    69      250kb
20          4   196      250kb
21          1   112      500kb
22          2    38      500kb
23          3    35      500kb
24          4    79      500kb
   Chimpanzee Human Shared resoultion stringency perc.shared    perc.h
1        5670  3467   2800       10kb          1   0.2345648 0.2904415
2        4583  2664   2713       10kb          2   0.2723896 0.2674699
3        3663  1962   2478       10kb          3   0.3058127 0.2421325
4        2590  1268   2052       10kb          4   0.3472081 0.2145516
5        1632  1354   1464       25kb          1   0.3289888 0.3042697
6        1360  1068   1431       25kb          2   0.3708215 0.2767556
7        1092   771   1337       25kb          3   0.4178125 0.2409375
8         783   509   1139       25kb          4   0.4685315 0.2093789
9         758   788    876       50kb          1   0.3616846 0.3253509
10        628   615    853       50kb          2   0.4069656 0.2934160
11        500   446    784       50kb          3   0.4531792 0.2578035
12        343   273    656       50kb          4   0.5157233 0.2146226
13        462   476    476      100kb          1   0.3366337 0.3366337
14        377   362    461      100kb          2   0.3841667 0.3016667
15        292   256    414      100kb          3   0.4303534 0.2661123
16        204   157    334      100kb          4   0.4805755 0.2258993
17        214   197    177      250kb          1   0.3010204 0.3350340
18        166   158    173      250kb          2   0.3480885 0.3179074
19        126   115    158      250kb          3   0.3959900 0.2882206
20         81    73    134      250kb          4   0.4652778 0.2534722
21        108    96     73      500kb          1   0.2635379 0.3465704
22         87    71     71      500kb          2   0.3100437 0.3100437
23         63    58     64      500kb          3   0.3459459 0.3135135
24         39    42     49      500kb          4   0.3769231 0.3230769
      perc.c
1  0.4749937
2  0.4601406
3  0.4520548
4  0.4382403
5  0.3667416
6  0.3524229
7  0.3412500
8  0.3220897
9  0.3129645
10 0.2996183
11 0.2890173
12 0.2696541
13 0.3267327
14 0.3141667
15 0.3035343
16 0.2935252
17 0.3639456
18 0.3340040
19 0.3157895
20 0.2812500
21 0.3898917
22 0.3799127
23 0.3405405
24 0.3000000

Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables
Using resoultion as id variables

Past versions of “Domain Inferences Using Individual TopDom Data-19.png”

Past versions of “Domain Inferences Using Individual TopDom Data-20.png”

Past versions of “Domain Inferences Using Individual TopDom Data-21.png”

Past versions of “Domain Inferences Using Individual TopDom Data-22.png”

Past versions of “Domain Inferences Using Individual TopDom Data-23.png”

Past versions of “Domain Inferences Using Individual TopDom Data-24.png”

#####First, a comparison using the hypergeometric distribution.
humans <- fread("data/TADs/Rao/GM12878.domains.bed", header=F, data.table=FALSE)
mice <- fread("data/TADs/Rao/mouse.domains.ortho.hg19.bed", header=F, data.table=FALSE)
mice.nonortho <- fread("data/TADs/Rao/Mouse.domains.bed")
humans$size <- humans$V3-humans$V2
mice$size <- mice$V3-mice$V2
mice.nonortho$size <- mice.nonortho$V3-mice.nonortho$V2
humans$species <- "human"
mice$species <- "mice.ortho"
mice.nonortho$species <- "mice"
median(humans$size)

[1] 185000

median(mice$size)

[1] 275521

median(mice.nonortho$size)

[1] 220000

sizes <- rbind(humans, mice, mice.nonortho)
ggplot(data=sizes) + geom_boxplot(aes(x=species, group=species, y=size)) + coord_cartesian(ylim=c(0, 2500000))

Past versions of “Comparisons of Rao data-1.png”

ggplot(data=sizes) + geom_density(aes(color=species, y=..scaled.., x=size)) + coord_cartesian(xlim=c(0, 2500000))

Past versions of “Comparisons of Rao data-2.png”

#Now, to get the actual probability of overlap given chance alone, assume a hypergeometric distribution. This is perhaps not appropriate, but here are the results regardless. First, need to calculate background as total possible TADs on human genome (assuming they're all 185kb):
#Parameter assignment, with human as the "population" (since more TADs discovered there) and mouse as the "sample".
m <- 9274 #Total number of TADs discovered in humans in the data
n <- round(3257347282/185000)-m #Possible numver of TADs found in population (human, total possible in genome based on genome length and median TAD size), minus the number actually found in humans. #Total human genome size from https://www.ncbi.nlm.nih.gov/grc/human/data; divided by median TAD size empirically from data
x <- 1309 #Actual observed overlap between humans and mice.
q <- x
k <- 2927#Number of TADs found in the sample being considered (here, mice).

phyper(q, m, n, k)

[1] 0.000000000000000000002476619

dhyper(1:1309, 9274, 17607-9274, 2927)

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plot(dhyper(1:2309, 9274, 17607-9274, 2927))

Past versions of “Comparisons of Rao data-3.png”

max(dhyper(1:1309, 9274, 17607-9274, 2927))

[1] 0.0000000000000000000007945096

which(grepl(max(dhyper(1:1800, m, n, k)), dhyper(1:1800, m, n, k))) #Indicates, based on hypergeometric, that expected overlap is actually higher than the observed! 1542 vs. 1309...

[1] 1542

####Now, something more reasonable that shuffles the locations of the TADs around the genome instead. This utilizes the method for domain conservation outlined in Rao et al 2014; define functions first:
#A function for calculating the overlap (finding the conserved domains) as per Rao et al 2014.
rao.overlapper <- function(filepath, mega=TRUE){
  df <- fread(filepath, data.table=F, header=F)
  df$mousesize <- df$V3-df$V2
  df$dist_max <- ifelse((df$mousesize*.5)<=50000, df$mousesize*.5, 50000)
  if(mega==FALSE){
    df$conserved <- ifelse((((df$V2-df$V5)^2+(df$V3-df$V6)^2)^0.5)<=df$dist_max, "yes", "no")}
  if(mega==TRUE){
    df$conserved <- ifelse((((df$V2-df$V6)^2+(df$V3-df$V7)^2)^0.5)<=df$dist_max, "yes", "no")
  }
  df$ID <- paste(df$V1, df$V2, df$V3, sep="_")
  cons.total <- length(unique(filter(df, conserved=="yes")$ID))
  return(as.numeric(cons.total))
}
rao.overlapper("data/TADs/HC.10kb.closest.hg38") #Of 10145 total

[1] 6558

rao.overlapper("data/TADs/CH.10kb.closest.panTro5") #Of 9382 total

[1] 6521

#Check how many of my domains are conserved.

####Subset my chimp domains to a smaller number and observe how many are conserved with rao overlapping stringency.
chimp.domains <- fread("data/TADs/Chimp_inter_30_KR_contact_domains/10000.domains.ortho.hg38", data.table=F, header=F)
chimp.domains.order <- chimp.domains[order(chimp.domains$V4, decreasing=TRUE),]
chimp.10 <- chimp.domains.order[1:938,]
chimp.20 <- chimp.domains.order[1:(938*2),]
chimp.30 <- chimp.domains.order[1:(938*3),]
fwrite(chimp.10, "data/TADs/Rao/chimps.10kb.10percent.domains.hg38", quote=FALSE, sep="\t", col.names=F)
fwrite(chimp.20, "data/TADs/Rao/chimps.10kb.20percent.domains.hg38", quote=FALSE, sep="\t", col.names = F)
fwrite(chimp.30, "data/TADs/Rao/chimps.10kb.30percent.domains.hg38", quote=FALSE, sep="\t", col.names=F)

#Check how many of the domains from these subsamples are conserved.
rao.overlapper("data/TADs/overlaps_rao_style/10kb.10percent.CH.closest.hg38") #~85%, 798/938

[1] 798

rao.overlapper("data/TADs/overlaps_rao_style/10kb.20percent.CH.closest.hg38") #~83%, 1556/1876

[1] 1556

rao.overlapper("data/TADs/overlaps_rao_style/10kb.30percent.CH.closest.hg38") #~81%, 2293/2814

[1] 2293

TAD Comparisons with data from Rao et al. 2014

#First, examine boundaries between hg38 and PT6 mega maps:
bounder <- function(resolution, species="H"){
  if(species=="H"){
  variable <- fread(paste("data/TADs/overlaps/", resolution, ".hg38.pt6.final.merged", sep=""), header=FALSE, data.table=FALSE)}
  if(species=="C"){
  variable <- fread(paste("data/TADs/overlaps/", resolution, ".panTro6.final.merged", sep=""), header=FALSE, data.table=FALSE)
  }
  h.only <- sum(variable$V4=="Human")
  c.only <- sum(variable$V4=="Chimp")
  shared <- nrow(variable) - h.only - c.only
  weird <- sum(variable$V4!="Human"&variable$V4!="Chimp"&variable$V4!="Human,Chimp"&variable$V4!="Chimp,Human") #Just checking to get a sense of how many of these cases there are, where boundaries overlapping each other will end up being extended due to merging and overlapping across multiple boundaries. These are still counted as conserved in this analysis though, since I calculated shared above merely by subtracting the number of human-only and chimp-only boundaries.
  print(weird) #Just print them out for edification.
  myvec <- c(shared, h.only, c.only, resolution, weird)
  return(myvec)
}
options(scipen=999)
bounds.5 <- bounder(5000)

[1] 1569

bounds.10 <- bounder(10000)

[1] 1756

bounds.25 <- bounder(25000)

[1] 380

bounds.50 <- bounder(50000)

[1] 1

bounds.100 <- bounder(100000)

[1] 0

bounds.250 <- bounder(250000)

[1] 0

bounds.500 <- bounder(500000)

[1] 0

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))

Past versions of “Juicer_mega hg38 v PT6 interspecies comparison-1.png”

#FIGS13D

###Interspecies TAD boundary overlap with bedtools -c###

#Now, I also show an analysis where I do not do any merging of the boundaries at all, for the sake of robustness. Here, instead of merging boundary files, I reciprocally use bedtools intersect -c on each file. The resultant files will list all the boundaries found as orthologously mappable across species in the first several columns, with the number of boundaries it overlapped (by any amount) in the other file in the 5th column. This counts each individual TAD's boundaries as unique, even if they have overlap. In this case, the number of "shared" boundaries may be different between the files output from each species, since I am checking different sets' overlaps against each other and one set may contain many adjacent/overlapping boundaries that overlap one boundary in the other. Hence, I merely chose whichever "shared" number is larger between the two species, to try to be conservative towards calling conservation. This is done on the output of the mega.bounds.intersect.c.sh file.
#Function to assess the output properly.
bounder.c <- function(resolution, species="H"){
  if(species=="H"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.H2C.hg38.pt6", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.C2H.hg38.pt6", sep=""))
  }
  if(species=="C"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.H2C.panTro6", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".boundaries.overlap.C2H.panTro6", sep=""))
  }
  h.only <- sum(dataframe.H$V5==0)
  c.only <- sum(dataframe.C$V5==0)
  shared <- max((nrow(dataframe.H)-h.only), (nrow(dataframe.C)-c.only))
  myvec <- c(shared, h.only, c.only, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

options(scipen=999)
bounds.5 <- bounder.c(5000)
bounds.10 <- bounder.c(10000)
bounds.25 <- bounder.c(25000)
bounds.50 <- bounder.c(50000)
bounds.100 <- bounder.c(100000)
bounds.250 <- bounder.c(250000)
bounds.500 <- bounder.c(500000)

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))#+ geom_text()#Need to add percentages here

Past versions of “Juicer_mega hg38 v PT6 interspecies comparison-2.png”

###Interspecies TAD boundary Rao Style Overlaps###

#Now, for one last check on boundaries, do it with the Rao overlap style for assessment of domain conservation.
#Rao-style overlapper for boundaries instead of domains (50kb is too large). The boundary elements are set to 15kb in size, and 50kb was used for median domain sizes of 185kb, so an appropriate approximate similar leniency would be 4 kb here. We'll try rounding to 5 and include a parameter for changing it to see how it affects it. The reality is that this shows much lower conservation than my other boundary conservation metrics because it is built for domain conservation and requires a certain amount of overlap for the boundaries to be considered conserved (whereas my prior analyses called any overlap as conserved). This function works on the output of the mega.bounds.rao.sh processing file.
rao.bounds.overlapper <- function(resolution, leniency=5000, mega=TRUE, species="H"){
  if(species=="H"){
  df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.HC.closest.hg38.pt6", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.CH.closest.hg38.pt6", sep=""), data.table=F, header=F)
  }
  if(species=="C"){
    df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.HC.closest.panTro6", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".boundaries.CH.closest.panTro6", sep=""), data.table=F, header=F)
  }
  df.h$size <- df.h$V3-df.h$V2
  df.c$size <- df.c$V3-df.c$V2
  df.h$dist_max <- ifelse((df.h$size*.5)<=leniency, df.h$size*.5, leniency)
  df.c$dist_max <- ifelse((df.c$size*.5)<=leniency, df.c$size*.5, leniency)
  if(mega==FALSE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V5)^2+(df.h$V3-df.h$V6)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V5)^2+(df.c$V3-df.c$V6)^2)^0.5)<=df.c$dist_max, "yes", "no")}
  if(mega==TRUE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V6)^2+(df.h$V3-df.h$V7)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V6)^2+(df.c$V3-df.c$V7)^2)^0.5)<=df.c$dist_max, "yes", "no")
  }
  df.h$ID <- paste(df.h$V1, df.h$V2, df.h$V3, sep="_")
  df.c$ID <- paste(df.c$V1, df.c$V2, df.c$V3, sep="_")
  cons.total.h <- length(unique(filter(df.h, conserved=="yes")$ID))
  cons.total.c <- length(unique(filter(df.c, conserved=="yes")$ID))
  ourcons <- max(as.numeric(cons.total.h), as.numeric(cons.total.c))
  myvec <- c(ourcons, length(unique(df.h$ID))-ourcons, length(unique(df.c$ID))-ourcons, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

options(scipen=999)
bounds.5 <- rao.bounds.overlapper(5000) #This is the only case where cons.H!=cons.C, just go with cons.H to inflate proportion conserved (it's more)
bounds.10 <- rao.bounds.overlapper(10000)
bounds.25 <- rao.bounds.overlapper(25000)
bounds.50 <- rao.bounds.overlapper(50000)
bounds.100 <- rao.bounds.overlapper(100000)
bounds.250 <- rao.bounds.overlapper(250000)
bounds.500 <- rao.bounds.overlapper(500000)
#Boundaries closest to failed for chimp coords on 100kb and 250kb...

mybounds <- as.data.frame(rbind(bounds.10, bounds.25, bounds.50, bounds.100, bounds.250, bounds.500))
colnames(mybounds) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mybounds$Resolution <- (mybounds$Resolution)/1000
mybounds$Resolution <- paste(mybounds$Resolution, "kb", sep="")
mybounds$totals <- mybounds$Shared + mybounds$Human + mybounds$Chimpanzee
mybounds$shared.perc <- mybounds$Shared/mybounds$totals
mybounds$human.perc <- mybounds$Human/mybounds$totals
mybounds$chimp.perc <- mybounds$Chimpanzee/mybounds$totals
ggbounds <- melt(mybounds[,1:4])

Using Resolution as id variables

ggbounds$Resolution <- factor(ggbounds$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggbounds) <- c("Resolution", "Species", "count")
ggplot(data=ggbounds) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Boundary Count") + ggtitle("Interspecies TAD Boundary Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))#+ geom_text()

Past versions of “Juicer_mega hg38 v PT6 interspecies comparison-3.png”

###Interspecies Domain Conservation using Rao et al. Method###
#First, define a function to call domain conservation as was performed in Rao et al. 2014.
#50kb is the leniency used by Rao et al for interspecies comparisons of domains, 0.5*|i-j| was also used for interspecies comparison (as opposed to 0.2*|i-j| for the cell types within human comparison), under the reasoning that we should be somewhat more permissive with flexibility of calling conservation allowing for errors in liftOver. This function works on the output of the files processed by mega.domains.rao.sh
rao.domain.overlapper <- function(resolution, mega=TRUE, species="H", leniency=50000){
  if(species=="H"){
  df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".HC.closest.hg38.pt6", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".CH.closest.hg38.pt6", sep=""), data.table=F, header=F)
  }
  if(species=="C"){
    df.h <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".HC.closest.panTro6", sep=""), data.table=F, header=F)
  df.c <- fread(paste("data/TADs/overlaps_rao_style/", resolution, ".CH.closest.panTro6", sep=""), data.table=F, header=F)
  }
  df.h$size <- df.h$V3-df.h$V2
  df.c$size <- df.c$V3-df.c$V2
  df.h$dist_max <- ifelse((df.h$size*.5)<=leniency, df.h$size*.5, leniency)
  df.c$dist_max <- ifelse((df.c$size*.5)<=leniency, df.c$size*.5, leniency)
  if(mega==FALSE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V5)^2+(df.h$V3-df.h$V6)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V5)^2+(df.c$V3-df.c$V6)^2)^0.5)<=df.c$dist_max, "yes", "no")}
  if(mega==TRUE){
    df.h$conserved <- ifelse((((df.h$V2-df.h$V6)^2+(df.h$V3-df.h$V7)^2)^0.5)<=df.h$dist_max, "yes", "no")
    df.c$conserved <- ifelse((((df.c$V2-df.c$V6)^2+(df.c$V3-df.c$V7)^2)^0.5)<=df.c$dist_max, "yes", "no")
  }
  df.h$ID <- paste(df.h$V1, df.h$V2, df.h$V3, sep="_")
  df.c$ID <- paste(df.c$V1, df.c$V2, df.c$V3, sep="_")
  cons.total.h <- length(unique(filter(df.h, conserved=="yes")$ID))
  cons.total.c <- length(unique(filter(df.c, conserved=="yes")$ID))
  if(cons.total.h!=cons.total.c){print(paste("conservation estimates different b/t species, human=", cons.total.h, " chimp=", cons.total.c, sep=""))}
  ourcons <- max(as.numeric(cons.total.h), as.numeric(cons.total.c))
  myvec <- c(ourcons, length(unique(df.h$ID))-ourcons, length(unique(df.c$ID))-ourcons, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

domains.5 <- rao.domain.overlapper(5000)

[1] "conservation estimates different b/t species, human=8762 chimp=8769"

domains.10 <- rao.domain.overlapper(10000)

[1] "conservation estimates different b/t species, human=7873 chimp=7874"

domains.25 <- rao.domain.overlapper(25000)
domains.50 <- rao.domain.overlapper(50000)
domains.100 <- rao.domain.overlapper(100000)
domains.250 <- rao.domain.overlapper(250000)
domains.500 <- rao.domain.overlapper(500000)

mydomains <- as.data.frame(rbind(domains.10, domains.25, domains.50, domains.100, domains.250, domains.500))
colnames(mydomains) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mydomains$Resolution <- (mydomains$Resolution)/1000
mydomains$Resolution <- paste(mydomains$Resolution, "kb", sep="")
mydomains$totals <- mydomains$Shared + mydomains$Human + mydomains$Chimpanzee
mydomains$shared.perc <- mydomains$Shared/mydomains$totals
mydomains$human.perc <- mydomains$Human/mydomains$totals
mydomains$chimp.perc <- mydomains$Chimpanzee/mydomains$totals
ggdomains <- melt(mydomains[,1:4])

Using Resolution as id variables

ggdomains$Resolution <- factor(ggdomains$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggdomains) <- c("Resolution", "Species", "count")
ggplot(data=ggdomains) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Interspecies TAD Domain Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))#+ geom_text()#Need to add percentages here

Past versions of “Juicer_mega hg38 v PT6 interspecies comparison-4.png”

#FIGS13C

#This method was likely the most robust way to define domain conservation, particularly with nested domains.

###Interspecies Domain Conservation using bedtools -c###
#The nested nature means that a bedtools merge analytic paradigm like that used at some points for boundaries above would definitely not be appropriate, so here, I also test what happens when using a reciprocal bedtools -c approach of the domains. I also utilized -f 0.9 -r in the bedtools -c call, meaning that a domain will only be called as found in the other file if 90% of it is covered by a domain in the other file, and that 90% of that domain is also covered in the original file. This function works on the output of mega.domains.bedtoolsc.sh
domain.conserved.c <- function(resolution, species="H"){
  if(species=="H"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".HC.bedtoolsc.hg38.pt6", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".CH.bedtoolsc.hg38.pt6", sep=""))
  }
  if(species=="C"){
    dataframe.H <- fread(paste("data/TADs/overlaps/", resolution, ".HC.bedtoolsc.panTro6", sep=""))
    dataframe.C <- fread(paste("data/TADs/overlaps/", resolution, ".CH.bedtoolsc.panTro6", sep=""))
  }
  h.only <- sum(dataframe.H$V5==0)
  c.only <- sum(dataframe.C$V5==0)
  shared <- max((nrow(dataframe.H)-h.only), (nrow(dataframe.C)-c.only)) #Take the max to inflate conservation
  myvec <- c(shared, h.only, c.only, resolution)
  names(myvec) <- c("Shared", "Human", "Chimpanzee", "Resolution")
  return(myvec)
}

domains.5 <- domain.conserved.c(5000)
domains.10 <- domain.conserved.c(10000)
domains.25 <- domain.conserved.c(25000)
domains.50 <- domain.conserved.c(50000)
domains.100 <- domain.conserved.c(100000)
domains.250 <- domain.conserved.c(250000)
domains.500 <- domain.conserved.c(500000)

mydomains <- as.data.frame(rbind(domains.10, domains.25, domains.50, domains.100, domains.250, domains.500))
colnames(mydomains) <- c("Shared", "Human", "Chimpanzee", "Resolution")
mydomains$Resolution <- (mydomains$Resolution)/1000
mydomains$Resolution <- paste(mydomains$Resolution, "kb", sep="")
mydomains$totals <- mydomains$Shared + mydomains$Human + mydomains$Chimpanzee
mydomains$shared.perc <- mydomains$Shared/mydomains$totals
mydomains$human.perc <- mydomains$Human/mydomains$totals
mydomains$chimp.perc <- mydomains$Chimpanzee/mydomains$totals
ggdomains <- melt(mydomains[,1:4])

Using Resolution as id variables

ggdomains$Resolution <- factor(ggdomains$Resolution, levels=c("10kb", "25kb", "50kb", "100kb", "250kb", "500kb"))
colnames(ggdomains) <- c("Resolution", "Species", "count")
ggplot(data=ggdomains) + geom_col(aes(x=Resolution, y=count, fill=Species)) + xlab("Resolution of Analysis") + ylab("Domain Count") + ggtitle("Interspecies TAD domain Conservation") + scale_fill_manual(name="Conservation", values=c("#F8766D", "#619CFF","#00BA38"), labels=c("Shared", "Human", "Chimpanzee"))#

Past versions of “Juicer_mega hg38 v PT6 interspecies comparison-5.png”

Human-Chimp large-scale linear interval synteny analysis

#Obtained these data from running the reciprocal best hits liftOver method on genome-wide windows of varying sizes created with bedtools makewindows. I then ran wc -l to look at line counts of the resultant files to see how many intervals were retained.
#FigS11A
syn.df <- data.frame(window=c(100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000), total=c(32351, 13100, 6743, 4638, 3583, 2949, 2533, 2229, 2007), retained=c(23025, 9491, 4862, 3264, 2442, 1952, 1624, 1381, 1209))#, species=c(rep("Human", 9), rep("Chimp", 9)))
ggplot(data=syn.df, aes(x=window, y=total)) + geom_col(aes(fill="Total"), position="identity") + geom_col(aes(y=retained, fill="Syntenic"), position="identity") + xlab("Interval Window (kb)") + ylab("Total Intervals") + ggtitle("Genome-Wide Synteny, Human to Chimpanzee") + scale_fill_manual(name="Intervals", values=c(Total="red", Syntenic="blue"))

Past versions of unnamed-chunk-1-1.png

#FigS11B
syn.df.pt5 <- data.frame(window=rep(c(100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000), 2), total=c(29687, 11881, 5947, 3967, 2979, 2387, 1990, 1708, 1494), retained=c(23252, 9607, 4907, 3301, 2499, 1971, 1650, 1409, 1229))#, species=c(rep("Human", 9), rep("Chimp", 9)))
ggplot(data=syn.df.pt5, aes(x=window, y=total)) + geom_col(aes(fill="Total"), position="identity") + geom_col(aes(y=retained, fill="Syntenic"), position="identity") + xlab("Interval Window (kb)") + ylab("Total Intervals") + ggtitle("Genome-Wide Synteny, Chimpanzee to Human") + scale_fill_manual(name="Intervals", values=c(Total="red", Syntenic="blue"))

Past versions of unnamed-chunk-1-2.png

Session information

sessionInfo()

R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] compiler  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] UpSetR_1.4.0       bedr_1.0.7         forcats_0.4.0     
 [4] purrr_0.3.2        readr_1.3.1        tibble_2.1.3      
 [7] tidyverse_1.2.1    edgeR_3.20.9       RColorBrewer_1.1-2
[10] heatmaply_0.16.0   viridis_0.5.1      viridisLite_0.3.0 
[13] stringr_1.4.0      gplots_3.0.1.1     Hmisc_4.2-0       
[16] Formula_1.2-3      survival_2.44-1.1  lattice_0.20-38   
[19] dplyr_0.8.3        plotly_4.9.0       cowplot_0.9.4     
[22] ggplot2_3.2.1      reshape2_1.4.3     data.table_1.12.0 
[25] tidyr_1.0.0        plyr_1.8.4         limma_3.34.9      

loaded via a namespace (and not attached):
  [1] colorspace_1.4-1     rprojroot_1.3-2      htmlTable_1.13.2    
  [4] futile.logger_1.4.3  base64enc_0.1-3      fs_1.3.1            
  [7] rstudioapi_0.10      bit64_0.9-7          lubridate_1.7.4     
 [10] xml2_1.2.2           codetools_0.2-16     splines_3.4.0       
 [13] R.methodsS3_1.7.1    knitr_1.22           zeallot_0.1.0       
 [16] jsonlite_1.6         workflowr_1.4.0      broom_0.5.2         
 [19] cluster_2.0.7-1      R.oo_1.22.0          shiny_1.3.2         
 [22] httr_1.4.1           backports_1.1.4      assertthat_0.2.1    
 [25] Matrix_1.2-17        lazyeval_0.2.2       cli_1.1.0           
 [28] later_0.8.0          formatR_1.7          acepack_1.4.1       
 [31] htmltools_0.3.6      tools_3.4.0          gtable_0.3.0        
 [34] glue_1.3.1           Rcpp_1.0.1           cellranger_1.1.0    
 [37] vctrs_0.2.0          gdata_2.18.0         nlme_3.1-137        
 [40] crosstalk_1.0.0      iterators_1.0.12     xfun_0.5            
 [43] ps_1.3.0             testthat_2.2.1       rvest_0.3.4         
 [46] mime_0.7             lifecycle_0.1.0      gtools_3.8.1        
 [49] dendextend_1.12.0    MASS_7.3-51.4        scales_1.0.0        
 [52] TSP_1.1-7            promises_1.0.1       hms_0.5.1           
 [55] parallel_3.4.0       lambda.r_1.2.4       yaml_2.2.0          
 [58] gridExtra_2.3        rpart_4.1-15         latticeExtra_0.6-28 
 [61] stringi_1.4.3        gclus_1.3.2          foreach_1.4.7       
 [64] checkmate_1.9.4      seriation_1.2-3      caTools_1.17.1.2    
 [67] rlang_0.4.0          pkgconfig_2.0.3      bitops_1.0-6        
 [70] evaluate_0.13        labeling_0.3         htmlwidgets_1.3     
 [73] processx_3.4.1       bit_1.1-14           tidyselect_0.2.5    
 [76] magrittr_1.5         R6_2.4.0             generics_0.0.2      
 [79] pillar_1.4.2         haven_2.1.1          whisker_0.4         
 [82] foreign_0.8-72       withr_2.1.2          nnet_7.3-12         
 [85] modelr_0.1.5         crayon_1.3.4         futile.options_1.0.1
 [88] KernSmooth_2.23-15   rmarkdown_1.12       locfit_1.5-9.1      
 [91] grid_3.4.0           readxl_1.3.1         git2r_0.26.1        
 [94] digest_0.6.18        webshot_0.5.1        xtable_1.8-4        
 [97] VennDiagram_1.6.20   httpuv_1.5.2         R.utils_2.9.0       
[100] munsell_0.5.0        registry_0.5-1