Last updated: 2019-08-06

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Rmd c3dbe5a Benjmain Fair 2019-06-19 update site
html c3dbe5a Benjmain Fair 2019-06-19 update site

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)

Version Author Date
c3dbe5a Benjmain Fair 2019-06-19

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 0
ToPlot %>%
  ggplot(aes(x=CV, color=eGene)) +
  stat_ecdf(geom = "step")

Version Author Date
c3dbe5a Benjmain Fair 2019-06-19

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 0
ToPlot %>%
  ggplot(aes(x=log.cpm.mean, color=eGene)) +
  stat_ecdf(geom = "step")

Version Author Date
c3dbe5a Benjmain Fair 2019-06-19

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     whisker_0.3-2    backports_1.1.4 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.4      assertthat_0.2.1
[45] colorspace_1.4-1 labeling_0.3     stringi_1.4.3    lazyeval_0.2.2  
[49] munsell_0.5.0    broom_0.5.2      crayon_1.3.4