Last updated: 2019-07-11

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

HumanLeadSnps <- read.table(gzfile('../data/Heart_Left_Ventricle.v7.250kB.leadsnps.txt.gz'), col.names = c("gene", "snp", "tss.dist", "ma_samples", "ma_count", "maf", "p", "slope", "slope_se"))


# 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) %>% 
  left_join(HumanLeadSnps, by=c("gene_id"="gene")) %>%
  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, p) %>% as.data.frame() %>%
    mutate(ChimpRank = dense_rank(qvalue)) %>%
    mutate(HumanRank = dense_rank(p)) %>%
    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 = 704440, p-value = 0.1074
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.1243046 
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 
-131.606  -28.074   -3.088   32.980  223.540 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)
(Intercept)    0.567449   8.529620   0.067    0.947
RankDifference 0.002238   0.001871   1.196    0.233

Residual standard error: 55.68 on 167 degrees of freedom
  (111 observations deleted due to missingness)
Multiple R-squared:  0.008496,  Adjusted R-squared:  0.002559 
F-statistic: 1.431 on 1 and 167 DF,  p-value: 0.2333
plot(contacts.v.eGene.lm)

With this procedure, the correlation was weaker. The earlier observation, that chimp eGenes have more dna contacts in their cis-windows in chimp, is not robustly detected.

The perhaps more sensitive way to ask a slightly different question, is this: do chimp eQTL snps (or conversely human eQTL snps), have more contacts between TSS and SNP in its respective species?


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   knitr_1.23      forcats_0.4.0  
 [9] stringr_1.4.0   dplyr_0.8.1     purrr_0.3.2     readr_1.3.1    
[13] tidyr_0.8.3     tibble_2.1.3    ggplot2_3.1.1   tidyverse_1.2.1

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