20190821_eGeneTissueCount
Ben Fair
8/21/2019
Last updated: 2019-08-21
Checks: 6 1
Knit directory: Comparative_eQTL/analysis/
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library(tidyverse)
library(knitr)Are eGenes that are shared between humans and chimps more likely to be eGenes in many tissues?
From a table of eGene qvalues across all GTEx tissues (GTEx v7 release), first get distribution of how many tissues each gene has a qval under some threshold
GTEx <- read.table("../data/AllGTExTissues.egenes.txt", header=T, sep='\t')
Threshold=0.1
TissueEgeneCount <- data.frame(TissueCount=rowSums(GTEx[,-1]<=Threshold, na.rm=T), Gene.stable.ID=gsub("\\.\\d+", "", GTEx$gene_id, perl=T))
hist(TissueEgeneCount$TissueCount, breaks=50)
From my data, plot this distribution (as a cumulative dist) after stratifying into human-specific eGenes, versus shared eGenes.
eQTLs <- read.table(gzfile("../data/PastAnalysesDataToKeep/20190521_eQTLs_250kB_10MAF.txt.gz"), header=T)
kable(head(eQTLs))| snps | gene | beta | statistic | pvalue | FDR | qvalue |
|---|---|---|---|---|---|---|
| ID.1.126459696.ACCCTAGTAAG.A | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
| ID.1.126465687.TTGT.A | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
| ID.1.126465750.TG.CT | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
| ID.1.126465756.T.C | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
| ID.1.126465766.C.A | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
| ID.1.126465774.G.A | ENSPTRG00000001061 | 3.570039 | 12.50828 | 0 | 3.5e-06 | 3.5e-06 |
# 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$Chimpanzee.homology.type)
ortholog_many2many ortholog_one2many ortholog_one2one
2278 19917 140351 OneToOneMap <- ChimpToHumanGeneMap %>%
filter(Chimpanzee.homology.type=="ortholog_one2one") %>%
distinct(Chimpanzee.gene.stable.ID, .keep_all = TRUE) %>%
left_join(TissueEgeneCount, by="Gene.stable.ID")
# Read gtex heart egene list
# Only consider those that were tested in both species and are one2one orthologs
GtexHeartEgenes <- read.table("../data/Heart_Left_Ventricle.v7.egenes.txt.gz", header=T, sep='\t', stringsAsFactors = F) %>%
mutate(gene_id_stable = gsub(".\\d+$","",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$Gene.stable.ID, warn_missing = F)
)}
HumanFDR <- 0.1
ChimpFDR <- 0.1
#Get chimp eQTLs
Chimp_eQTLs <- eQTLs %>%
filter(qvalue<ChimpFDR)
# Count chimp eGenes
length(unique(Chimp_eQTLs$gene))[1] 336# Count human eGenes
length(GtexHeartEgenes %>% filter(qval< HumanFDR) %>% pull(chimp_id))[1] 5410# Count number genes tested in both species (already filtered for 1to1 orthologs)
length(GtexHeartEgenes$gene_id_stable)[1] 11586#Change FDR thresholds or take top N eGenes by qvalue
HumanTopN <- 600
HumanFDR <- 0.1
ChimpFDR <- 0.1
# Filter human eGenes by qval threshold
HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% pull(chimp_id)
# Filter human eGenes by topN qval
# HumanSigGenes <- GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% pull(chimp_id)
# Filter human eGeness by qval threshold then topN betas
# HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% top_n(1000, abs(slope)) %>% pull(chimp_id)
HumanNonSigGenes <- GtexHeartEgenes %>%
filter(!chimp_id %in% HumanSigGenes) %>%
pull(chimp_id)
ChimpSigGenes <- GtexHeartEgenes %>%
filter(chimp_id %in% Chimp_eQTLs$gene) %>%
pull(chimp_id)
ChimpNonSigGenes <- GtexHeartEgenes %>%
filter(! chimp_id %in% Chimp_eQTLs$gene) %>%
pull(chimp_id)
ContigencyTable <- matrix( c( length(intersect(ChimpSigGenes,HumanSigGenes)),
length(intersect(HumanSigGenes,ChimpNonSigGenes)),
length(intersect(ChimpSigGenes,HumanNonSigGenes)),
length(intersect(ChimpNonSigGenes,HumanNonSigGenes))),
nrow = 2)
rownames(ContigencyTable) <- c("Chimp eGene", "Not Chimp eGene")
colnames(ContigencyTable) <- c("Human eGene", "Not human eGene")
#what is qval threshold for human eGene classification in this contigency table
print(GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% top_n(1, qval) %>% pull(qval))[1] 5.83223e-12#Contigency table of one to one orthologs tested in both chimps and humans of whether significant in humans, or chimps, or both, or neither
ContigencyTable Human eGene Not human eGene
Chimp eGene 145 135
Not Chimp eGene 5265 6041#One-sided Fisher test for greater overlap than expected by chance
fisher.test(ContigencyTable, alternative="greater")
Fisher's Exact Test for Count Data
data: ContigencyTable
p-value = 0.04779
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
1.002643 Inf
sample estimates:
odds ratio
1.232354 #Chimp eGenes vs non chimp eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% ChimpSigGenes ~ "chimp.eGene",
!chimp_id %in% ChimpSigGenes ~ "not.chimp.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Chimp.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="greater")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Human eGenes vs non human eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% HumanSigGenes ~ "human.eGene",
!chimp_id %in% HumanSigGenes ~ "not.human.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Human.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="greater")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Shared eGenes vs human-specific eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
chimp_id %in% setdiff(HumanSigGenes, ChimpSigGenes) ~ "human.specific.eGene")) %>%
filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(HumanSigGenes, ChimpSigGenes)))%>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Shared.human.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="less")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Shared eGenes vs chimp-specific eGenes
ToPlot <- GtexHeartEgenes %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee) %>%
mutate(group = case_when(
chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
chimp_id %in% setdiff(ChimpSigGenes, HumanSigGenes) ~ "chimp.specific.eGene")) %>%
dplyr::filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(ChimpSigGenes, HumanSigGenes)))
Shared.chimp.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="less")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
Chimp.tissue.plot
Human.tissue.plot
Shared.human.tissue.plot
Shared.chimp.tissue.plot
Make same plots, but using top 600 human eGenes to classify heart eGene for purposes of species sharing.
#Change FDR thresholds or take top N eGenes by qvalue
HumanTopN <- 600
HumanFDR <- 0.1
ChimpFDR <- 0.1
# Filter human eGenes by qval threshold
# HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% pull(chimp_id)
# Filter human eGenes by topN qval
HumanSigGenes <- GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% pull(chimp_id)
# Filter human eGeness by qval threshold then topN betas
# HumanSigGenes <- GtexHeartEgenes %>% filter(qval<HumanFDR) %>% top_n(1000, abs(slope)) %>% pull(chimp_id)
HumanNonSigGenes <- GtexHeartEgenes %>%
filter(!chimp_id %in% HumanSigGenes) %>%
pull(chimp_id)
ChimpSigGenes <- GtexHeartEgenes %>%
filter(chimp_id %in% Chimp_eQTLs$gene) %>%
pull(chimp_id)
ChimpNonSigGenes <- GtexHeartEgenes %>%
filter(! chimp_id %in% Chimp_eQTLs$gene) %>%
pull(chimp_id)
ContigencyTable <- matrix( c( length(intersect(ChimpSigGenes,HumanSigGenes)),
length(intersect(HumanSigGenes,ChimpNonSigGenes)),
length(intersect(ChimpSigGenes,HumanNonSigGenes)),
length(intersect(ChimpNonSigGenes,HumanNonSigGenes))),
nrow = 2)
rownames(ContigencyTable) <- c("Chimp eGene", "Not Chimp eGene")
colnames(ContigencyTable) <- c("Human eGene", "Not human eGene")
#what is qval threshold for human eGene classification in this contigency table
print(GtexHeartEgenes %>% top_n(-HumanTopN, qval) %>% top_n(1, qval) %>% pull(qval))[1] 5.83223e-12#Contigency table of one to one orthologs tested in both chimps and humans of whether significant in humans, or chimps, or both, or neither
ContigencyTable Human eGene Not human eGene
Chimp eGene 28 252
Not Chimp eGene 572 10734#One-sided Fisher test for greater overlap than expected by chance
fisher.test(ContigencyTable, alternative="greater")
Fisher's Exact Test for Count Data
data: ContigencyTable
p-value = 0.0006395
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
1.444504 Inf
sample estimates:
odds ratio
2.084996 #Chimp eGenes vs non chimp eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% ChimpSigGenes ~ "chimp.eGene",
!chimp_id %in% ChimpSigGenes ~ "not.chimp.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Chimp.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="greater")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Human eGenes vs non human eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% HumanSigGenes ~ "human.eGene",
!chimp_id %in% HumanSigGenes ~ "not.human.eGene")) %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Human.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="greater")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Shared eGenes vs human-specific eGenes
ToPlot <- GtexHeartEgenes %>%
mutate(group = case_when(
chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
chimp_id %in% setdiff(HumanSigGenes, ChimpSigGenes) ~ "human.specific.eGene")) %>%
filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(HumanSigGenes, ChimpSigGenes)))%>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID"))
Shared.human.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="less")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
#Shared eGenes vs chimp-specific eGenes
ToPlot <- GtexHeartEgenes %>%
left_join(OneToOneMap, by=c("chimp_id"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee) %>%
mutate(group = case_when(
chimp_id %in% intersect(HumanSigGenes, ChimpSigGenes) ~ "shared.eGene",
chimp_id %in% setdiff(ChimpSigGenes, HumanSigGenes) ~ "chimp.specific.eGene")) %>%
dplyr::filter(chimp_id %in% union(intersect(HumanSigGenes, ChimpSigGenes), setdiff(ChimpSigGenes, HumanSigGenes)))
Shared.chimp.tissue.plot <- ggplot(ToPlot, aes(color=group,x=TissueCount)) +
stat_ecdf(geom = "step") +
ylab("Cumulative frequency") +
xlab("TissueCount") +
annotate("text", x = 40, y = 0.4, label = paste("Mann-Whitney\none-sided P =", signif(wilcox.test(data=ToPlot, TissueCount ~ group, alternative="less")$p.value, 2) )) +
theme_bw() +
theme(legend.position = c(.80, .2), legend.title=element_blank())
Chimp.tissue.plot
Human.tissue.plot
Shared.human.tissue.plot
Shared.chimp.tissue.plot
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] knitr_1.23 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
[9] ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 highr_0.8 cellranger_1.1.0 plyr_1.8.4
[5] pillar_1.4.1 compiler_3.5.1 git2r_0.25.2 workflowr_1.4.0
[9] tools_3.5.1 digest_0.6.19 lubridate_1.7.4 jsonlite_1.6
[13] evaluate_0.14 nlme_3.1-140 gtable_0.3.0 lattice_0.20-38
[17] pkgconfig_2.0.2 rlang_0.3.4 cli_1.1.0 rstudioapi_0.10
[21] yaml_2.2.0 haven_2.1.0 xfun_0.7 withr_2.1.2
[25] xml2_1.2.0 httr_1.4.0 hms_0.4.2 generics_0.0.2
[29] fs_1.3.1 rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[33] glue_1.3.1 R6_2.4.0 readxl_1.3.1 rmarkdown_1.13
[37] modelr_0.1.4 magrittr_1.5 backports_1.1.4 scales_1.0.0
[41] htmltools_0.3.6 rvest_0.3.4 assertthat_0.2.1 colorspace_1.4-1
[45] labeling_0.3 stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0
[49] broom_0.5.2 crayon_1.3.4