20190617_ExpressionVarianceOfEgenes
Ben Fair
6/17/2019
Last updated: 2019-06-19
Checks: 6 1
Knit directory: Comparative_eQTL/analysis/
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For my own understanding of what it means to be an eGene, I wanted to plot from my chimp data the variance in expression levels of eGenes vs non eGenes. This is an important component to if it is reasonable to intepret eGene character as a proxy for stabilizing selection and/or low expression variance genes.
The way I will go about this is looking at the original count table of CPM for all tested genes, and plot the mean/variance trend with eGenes as a different color.
library(tidyverse)
eQTLs <- read.table("../output/MatrixEQTL_sig_eqtls.txt", header=T)
CountTable <- as.matrix(read.table("../output/ExpressionMatrix.un-normalized.txt.gz", header=T))
ToPlot <- as.data.frame(CountTable) %>%
rownames_to_column("gene") %>%
mutate(log.cpm.mean=apply(CountTable,1,mean),
log.cpm.var=apply(CountTable,1,var),
eGene=(gene %in% eQTLs$gene)) %>%
mutate(CV=log.cpm.mean/sqrt(log.cpm.var))
ToPlot %>%
ggplot(aes(x=log.cpm.mean, y=log.cpm.var, color=eGene)) +
geom_point(alpha=0.5)
Maybe a different way to look at this trend will be more clear
wilcox.test(data=ToPlot, CV ~ eGene)
Wilcoxon rank sum test with continuity correction
data: CV by eGene
W = 1964800, p-value = 0.0008523
alternative hypothesis: true location shift is not equal to 0ToPlot %>%
ggplot(aes(x=CV, color=eGene)) +
stat_ecdf(geom = "step")
eGenes have higher coefficient of variation
wilcox.test(data=ToPlot, log.cpm.mean ~ eGene)
Wilcoxon rank sum test with continuity correction
data: log.cpm.mean by eGene
W = 2470400, p-value = 0.0001234
alternative hypothesis: true location shift is not equal to 0ToPlot %>%
ggplot(aes(x=log.cpm.mean, color=eGene)) +
stat_ecdf(geom = "step")
but actually lower expression level
But all being said, the effect size is to the point where eGene character I think is only modest evidence for lack of stabilizing selection. Maybe the thing to do is to check the correlation between selection on coding regions (dN/dS or percent identity between two species) and the variance within a species to see if that signal is stronger than for eGene character.
ChimpToHumanGeneMap <- read.table("../data/Biomart_export.Hsap.Ptro.orthologs.txt.gz", header=T, sep='\t', stringsAsFactors = F)
# Of this ortholog list, how many genes are one2one
table(ChimpToHumanGeneMap$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)
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)
)}
ToPlotAdded <- ToPlot %>%
left_join(OneToOneMap, by=c("gene"="Chimpanzee.gene.stable.ID")) %>%
mutate(dN.dS = dN.with.Chimpanzee/dS.with.Chimpanzee,
rank = dense_rank(desc(CV))) %>%
mutate(TopN.Var.Genes=rank<300) %>%
ggplot(aes(x=100-X.id..query.gene.identical.to.target.Chimpanzee.gene, color=TopN.Var.Genes)) +
stat_ecdf(geom = "step") +
scale_x_continuous(trans='log1p')Surprising to me, chimp expression coefficient of variation (n=39) doesn’t separate dN/dS or percent identity better than chimp eGene classificiation. There are a couple things I should probably change about the above analysis to make it a more fair comparison to the eGene classificiation: I should be considering variation in TPM
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] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.1.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 cellranger_1.1.0 plyr_1.8.4 pillar_1.4.1
[5] compiler_3.5.1 git2r_0.25.2 workflowr_1.4.0 tools_3.5.1
[9] digest_0.6.19 lubridate_1.7.4 jsonlite_1.6 evaluate_0.14
[13] nlme_3.1-140 gtable_0.3.0 lattice_0.20-38 pkgconfig_2.0.2
[17] rlang_0.3.4 cli_1.1.0 rstudioapi_0.10 yaml_2.2.0
[21] haven_2.1.0 xfun_0.7 withr_2.1.2 xml2_1.2.0
[25] httr_1.4.0 knitr_1.23 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