Last updated: 2019-07-11

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Knit directory: Comparative_eQTL/analysis/

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library(tidyverse)
library(knitr)
library("edgeR")
library(corrplot)
library(gplots)
library(pROC)
library(qvalue)
library(reshape2)
library(gridExtra)

As an initial pass at testing the hypothesis that Chimp-specific cis-eGenes have more DNA contacts at those cis-windows than humans in those same cis-windows, I looked at chimp/human differential contact windows from Eres et al and intersected in with my list of Chimp eGene locations (+/- 100kb). Then I looked at the sum of effect sizes within each of those windows.

DataIn <- read.table("../data/DCContactsInEgenes.bed", col.names = c("chr", "start", "stop", "gene", "blank", "strand", "chrContact", "startContact", "beta", "nameContact", "beta", "strandContact"), sep='\t', row.names = NULL)

GroupedData <- DataIn %>%
  group_by(stop) %>%
  summarise(BetaSum = sum(beta))

ggplot(GroupedData, aes(x=BetaSum)) +
  stat_ecdf(geom = "step") +
  xlab("Differential contact effect size sum over Chimp eGenes cis-window\n(Negative means more contact in chimp)") +
  ylab("Cumulative frequency") +
  theme_bw()

wilcox.test(GroupedData$BetaSum, mu = 0, alternative = "less")

    Wilcoxon signed rank test

data:  GroupedData$BetaSum
V = 135, p-value = 0.02245
alternative hypothesis: true location is less than 0

Ok it seems there is a subtle but significant shift in that chimp eGenes have more differential contacts a way that slightly favors high connectivity in chimps.

To explore this more carefully, Ittai gave me a list of Homer-normalized contact data (not just significant differential contacts and effect sizes) for all his 8 samples (4 human, 4 chimp) for the cis-window surrounding each eGene (+/- 250kb).

From this I can estimate connectivity within a cis window as the sum of all Homer normalized contact scores within a species. Then I will look at the difference between that sum between species, and ask if it is correlated with some species measure of eGene character.

First, read in Ittai’s Homer normalized contact data for each individual in both chimp and human…

SampleA<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_A-21792_10kb_norm.gz"), sep='\t')
SampleB<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_B-28126_10kb_norm.gz"), sep='\t')
SampleC<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_C-3649_10kb_norm.gz"), sep='\t')
SampleD<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_D-40300_10kb_norm.gz"), sep='\t')
SampleE<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_E-28815_10kb_norm.gz"), sep='\t')
SampleF<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_F-28834_10kb_norm.gz"), sep='\t')
SampleG<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_G-3624_10kb_norm.gz"), sep='\t')
SampleH<- read.csv(gzfile("../data/IttaiHomerInteractionScoresInCisWindows/adj_bins_25_H-3651_10kb_norm.gz"), sep='\t')

HumanInteractions <- data.frame(H.Score = rowSums(cbind(SampleA, SampleB, SampleE, SampleF))) %>%
  rownames_to_column() %>%
  mutate(HumanID = gsub("(.+?)\\..+?", "\\1", rowname, perl=T))


ChimpInteractions <- data.frame(C.Score = rowSums(cbind(SampleC, SampleD, SampleG, SampleH))) %>%
  rownames_to_column("ChimpID")

Ok now read in eQTL data…

eQTLs <- read.table(gzfile("../data/PastAnalysesDataToKeep/20190521_eQTLs_250kB_10MAF.txt.gz"), header=T)

# List of chimp tested genes
ChimpTestedGenes <- rownames(read.table('../output/ExpressionMatrix.un-normalized.txt.gz', header=T, check.names=FALSE, row.names = 1))

ChimpToHumanGeneMap <- read.table("../data/Biomart_export.Hsap.Ptro.orthologs.txt.gz", header=T, sep='\t', stringsAsFactors = F)
kable(head(ChimpToHumanGeneMap))
Gene.stable.ID Transcript.stable.ID Chimpanzee.gene.stable.ID Chimpanzee.gene.name Chimpanzee.protein.or.transcript.stable.ID Chimpanzee.homology.type X.id..target.Chimpanzee.gene.identical.to.query.gene X.id..query.gene.identical.to.target.Chimpanzee.gene dN.with.Chimpanzee dS.with.Chimpanzee Chimpanzee.orthology.confidence..0.low..1.high.
ENSG00000198888 ENST00000361390 ENSPTRG00000042641 MT-ND1 ENSPTRP00000061407 ortholog_one2one 94.6541 94.6541 0.0267 0.5455 1
ENSG00000198763 ENST00000361453 ENSPTRG00000042626 MT-ND2 ENSPTRP00000061406 ortholog_one2one 96.2536 96.2536 0.0185 0.7225 1
ENSG00000210127 ENST00000387392 ENSPTRG00000042642 MT-TA ENSPTRT00000076396 ortholog_one2one 100.0000 100.0000 NA NA NA
ENSG00000198804 ENST00000361624 ENSPTRG00000042657 MT-CO1 ENSPTRP00000061408 ortholog_one2one 98.8304 98.8304 0.0065 0.5486 1
ENSG00000198712 ENST00000361739 ENSPTRG00000042660 MT-CO2 ENSPTRP00000061402 ortholog_one2one 97.7974 97.7974 0.0106 0.5943 1
ENSG00000228253 ENST00000361851 ENSPTRG00000042653 MT-ATP8 ENSPTRP00000061400 ortholog_one2one 94.1176 94.1176 0.0325 0.3331 1 
# Of this ortholog list, how many genes are one2one
table(ChimpToHumanGeneMap$Chimpanzee.homology.type)

ortholog_many2many  ortholog_one2many   ortholog_one2one 
              2278              19917             140351 
OneToOneMap <- ChimpToHumanGeneMap %>%
  filter(Chimpanzee.homology.type=="ortholog_one2one")

# Read gtex heart egene list
# Only consider those that were tested in both species and are one2one orthologs
GtexHeartEgenes <- read.table("../data/Heart_Left_Ventricle.v7.egenes.txt.gz", header=T, sep='\t', stringsAsFactors = F) %>%
  mutate(gene_id_stable = gsub(".\\d+$","",gene_id)) %>%
  filter(gene_id_stable %in% OneToOneMap$Gene.stable.ID) %>%
  mutate(chimp_id = plyr::mapvalues(gene_id_stable, OneToOneMap$Gene.stable.ID,  OneToOneMap$Chimpanzee.gene.stable.ID, warn_missing = F)) %>%
  filter(chimp_id %in% ChimpTestedGenes)

ChimpToHuman.ID <- function(Chimp.ID){
  #function to convert chimp ensembl to human ensembl gene ids
  return(
    plyr::mapvalues(Chimp.ID, OneToOneMap$Chimpanzee.gene.stable.ID, OneToOneMap$Gene.stable.ID, warn_missing = F)
  )}

First question: do the ~300 chimp eGenes have more contacts in their cis-window in chimp

Chimp_OrderedGenes <- eQTLs %>%
    group_by(gene) %>% 
    dplyr::slice(which.min(qvalue)) %>%
    filter(gene %in% GtexHeartEgenes$chimp_id) %>%
    left_join(GtexHeartEgenes, by=c("gene"="chimp_id")) %>%
    dplyr::select(gene, qvalue, qval) %>% as.data.frame() %>%
    mutate(ChimpRank = dense_rank(qvalue)) %>%
    mutate(HumanRank = dense_rank(qval)) %>%
    mutate(RankDifference = HumanRank-ChimpRank) %>%
    filter(qvalue <0.1) %>%
    mutate(HumanID=ChimpToHuman.ID(gene))

# OneToOneMap %>%
#   inner_join(HumanInteractions, by=c("Gene.stable.ID"="HumanId")) %>% dim()
#   inner_join(ChimpInteractions, by=c("Chimpanzee.gene.stable.ID"="ChimpID")) %>% dim()
#   right_join(Chimp_OrderedGenes, by=c("Chimpanzee.gene.stable.ID"="gene")) %>% dim()

Chimp_OrderedGenes.WithContactInfo <- Chimp_OrderedGenes %>%
  left_join(HumanInteractions, by=c("HumanID")) %>%
  left_join(ChimpInteractions, by=c("gene"="ChimpID")) %>% 
  mutate(InteractionDifference=H.Score - C.Score)

  
ggplot(Chimp_OrderedGenes.WithContactInfo, aes(x=InteractionDifference)) +
  stat_ecdf(geom = "step") +
  xlab("Difference in contacts over chimp eGene cis-windows\n(Positive means more contact in chimp)") +
  ylab("Cumulative frequency") +
  theme_bw()

ggplot(Chimp_OrderedGenes.WithContactInfo, aes(x=RankDifference, y=InteractionDifference)) +
  geom_point() +
  theme_bw() +
  xlab("Rank Difference in eGene significance\nMore in human <--  --> More in chimp") +
  ylab("Differential contacts in cis window\nMore in human <--  --> More in chimp") +
  geom_smooth(method='lm',formula=y~x)

cor.test(x=Chimp_OrderedGenes.WithContactInfo$RankDifference, y=Chimp_OrderedGenes.WithContactInfo$InteractionDifference, method="spearman")

    Spearman's rank correlation rho

data:  Chimp_OrderedGenes.WithContactInfo$RankDifference and Chimp_OrderedGenes.WithContactInfo$InteractionDifference
S = 656450, p-value = 0.01665
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.1839659 
contacts.v.eGene.lm = lm(InteractionDifference ~ RankDifference, data=Chimp_OrderedGenes.WithContactInfo)
summary(contacts.v.eGene.lm)

Call:
lm(formula = InteractionDifference ~ RankDifference, data = Chimp_OrderedGenes.WithContactInfo)

Residuals:
     Min       1Q   Median       3Q      Max 
-127.372  -29.715   -2.727   33.539  222.680 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)  
(Intercept)    -5.776579   7.953704  -0.726   0.4687  
RankDifference  0.003498   0.001552   2.253   0.0255 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 55.08 on 167 degrees of freedom
  (111 observations deleted due to missingness)
Multiple R-squared:  0.02951,   Adjusted R-squared:  0.0237 
F-statistic: 5.078 on 1 and 167 DF,  p-value: 0.02553
plot(contacts.v.eGene.lm)

Ok yes, there is a slight correlation between chimp eGene character, and contacts in chimp. Now I am going to take a different approach to ask a similar question: Do lead chimp eQTLs have more contacts between a gene and its promoter window than human.


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gridExtra_2.3   reshape2_1.4.3  qvalue_2.14.1   pROC_1.15.0    
 [5] gplots_3.0.1.1  corrplot_0.84   edgeR_3.24.3    limma_3.38.3   
 [9] knitr_1.23      forcats_0.4.0   stringr_1.4.0   dplyr_0.8.1    
[13] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.3   
[17] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         locfit_1.5-9.1     lubridate_1.7.4   
 [4] lattice_0.20-38    gtools_3.8.1       assertthat_0.2.1  
 [7] rprojroot_1.3-2    digest_0.6.19      R6_2.4.0          
[10] cellranger_1.1.0   plyr_1.8.4         backports_1.1.4   
[13] evaluate_0.14      httr_1.4.0         highr_0.8         
[16] pillar_1.4.1       rlang_0.3.4        lazyeval_0.2.2    
[19] readxl_1.3.1       rstudioapi_0.10    gdata_2.18.0      
[22] rmarkdown_1.13     labeling_0.3       splines_3.5.1     
[25] munsell_0.5.0      broom_0.5.2        compiler_3.5.1    
[28] modelr_0.1.4       xfun_0.7           pkgconfig_2.0.2   
[31] htmltools_0.3.6    tidyselect_0.2.5   workflowr_1.4.0   
[34] crayon_1.3.4       withr_2.1.2        bitops_1.0-6      
[37] grid_3.5.1         nlme_3.1-140       jsonlite_1.6      
[40] gtable_0.3.0       git2r_0.25.2       magrittr_1.5      
[43] scales_1.0.0       KernSmooth_2.23-15 cli_1.1.0         
[46] stringi_1.4.3      fs_1.3.1           xml2_1.2.0        
[49] generics_0.0.2     tools_3.5.1        glue_1.3.1        
[52] hms_0.4.2          yaml_2.2.0         colorspace_1.4-1  
[55] caTools_1.17.1.2   rvest_0.3.4        haven_2.1.0