Last updated: 2020-10-25

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Knit directory: Comparative_eQTL/analysis/

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See here for a more description.

Here, I will take the cell type specific dispersion point estimates described in the link above, and examine the bootstrapped standard errors (1000 replicates, resampled at the stage of different individuals after TED expression estimation) that were produced a part of the snakemake in the code section of this repo.

First, load libraries and data

library(tidyverse)
library(knitr)

Bootstrapped.SE.estimates <- read.delim("../output/CellTypeDispersion.SE.tsv.gz")
DispersionPointEstimates <- readRDS("../big_data/NormalizedExpressionPerCellType.rds") %>%
  group_by(gene, CellType, Species) %>%
  summarise(mu=mean(Log.CPM.Expression), log.var=log(var(Log.CPM.Expression))) %>%
  group_by(Species, CellType) %>% 
    do(data.frame(., resid = residuals(loess(log.var ~ mu, data=., degree=1, na.action="na.exclude"))))

CellTypeDispersion.df <- inner_join(Bootstrapped.SE.estimates, DispersionPointEstimates, by=c("CellType", "Species", "gene"))

head(CellTypeDispersion.df) %>% kable()
CellType Species gene mu.SE resid.SE mu log.var resid
cardiac muscle cell Chimp ENSG00000000003 0.0929669 0.1288482 1.3636554 -1.0070469 0.2349300
cardiac muscle cell Chimp ENSG00000000005 0.1713368 0.1838315 -0.9277903 0.2284514 1.0082161
cardiac muscle cell Chimp ENSG00000000457 0.0932767 0.1895129 1.2044743 -1.0083992 0.1977176
cardiac muscle cell Chimp ENSG00000001036 0.0530243 0.3162196 3.4908040 -2.1931103 -0.7386727
cardiac muscle cell Chimp ENSG00000001084 0.1275738 0.2238554 0.8604686 -0.3640991 0.7645701
cardiac muscle cell Chimp ENSG00000001167 0.0531067 0.1944670 1.6356742 -2.1726169 -0.8758627 
Dispersion <- read.delim('../output/Final/TableS3.tab')
head(Dispersion) %>% kable()
gene HGNC.symbol Chimp.Mean.Expression Human.Mean.Expression Chimp.Overdispersion Human.Overdispersion Chimp.Mean.Adjusted.Dispersion Human.Mean.Adjusted.Dispersion Chimp.SE Human.SE P q.value
ENSG00000186891 TNFRSF18 -20.04475 -22.72507 2.1055309 0.6147549 2.3121799 0.0805422 0.3799365 1.2023115 0.0054 0.0317946
ENSG00000186827 TNFRSF4 -18.77917 -19.33208 0.6203152 0.8582437 1.6405781 1.9529450 0.2157222 0.3020914 0.3439 0.3126680
ENSG00000078808 SDF4 -17.25701 -17.12363 0.0583115 0.0420739 -0.5386575 -0.8183559 0.2215974 0.2267717 0.3624 0.3210003
ENSG00000176022 B3GALT6 -18.54108 -18.71933 0.0921186 0.0543682 -0.2006514 -0.6565939 0.2894171 0.2657152 0.3434 0.3124612
ENSG00000184163 C1QTNF12 -20.22992 -20.96884 0.9431169 0.3705950 1.4085703 0.3109663 0.3138983 0.4147022 0.0662 0.1329230
ENSG00000160087 UBE2J2 -18.96377 -18.91956 0.0832274 0.0533650 -0.4256215 -0.7154168 0.2888279 0.3411127 0.4752 0.3657044

Let’s plot some things to explore… Is the distribution of gene expression and standard errors obviously different in different cell types?

CellTypeDispersion.df %>%
  ggplot(aes(x=mu, color=CellType)) +
  geom_density(aes(linetype=Species)) +
  facet_wrap(~CellType) +
  theme_bw()

CellTypeDispersion.df %>%
  ggplot(aes(x=mu.SE, color=CellType)) +
  geom_density(aes(linetype=Species)) +
  facet_wrap(~CellType) +
  theme_bw()

CellTypeDispersion.df %>%
  ggplot(aes(x=resid, color=CellType)) +
  geom_density(aes(linetype=Species)) +
  facet_wrap(~CellType) +
  theme_bw()

CellTypeDispersion.df %>%
  ggplot(aes(x=resid.SE, color=CellType)) +
  geom_density(aes(linetype=Species)) +
  facet_wrap(~CellType) +
  theme_bw()

Ok, write out the results

CellTypeDispersion.df %>%
  dplyr::select(Species, CellType, gene, MeanExpression=mu, MeanExpression.StandardError=mu.SE, Overdispersion=log.var, Mean.Adjusted.Dispersion=resid, Mean.Adjusted.Dispersion.StandardError=resid.SE) %>%
  write_delim("../figures/CellTypeDispersion.Reorganized.source.data.tsv", delim='\t')

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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_1.26      forcats_0.4.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.3     readr_1.3.1     tidyr_1.0.0     tibble_3.0.3   
 [9] ggplot2_3.3.2   tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.11        haven_2.2.0      colorspace_1.4-1
 [5] vctrs_0.3.4      generics_0.0.2   htmltools_0.4.0  yaml_2.2.0      
 [9] rlang_0.4.7      later_1.0.0      pillar_1.4.6     withr_2.1.2     
[13] glue_1.4.2       DBI_1.0.0        dbplyr_1.4.2     modelr_0.1.5    
[17] readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0    gtable_0.3.0    
[21] workflowr_1.5.0  cellranger_1.1.0 rvest_0.3.5      evaluate_0.14   
[25] labeling_0.3     httpuv_1.5.2     fansi_0.4.0      highr_0.8       
[29] broom_0.7.0      Rcpp_1.0.5       promises_1.1.0   backports_1.1.5 
[33] scales_1.1.1     jsonlite_1.6     farver_2.0.3     fs_1.3.1        
[37] hms_0.5.2        digest_0.6.23    stringi_1.4.3    grid_3.6.1      
[41] rprojroot_1.3-2  cli_2.0.0        tools_3.6.1      magrittr_1.5    
[45] crayon_1.3.4     pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.2.2      
[49] reprex_0.3.0     lubridate_1.7.9  assertthat_0.2.1 rmarkdown_1.18  
[53] httr_1.4.1       rstudioapi_0.10  R6_2.4.1         git2r_0.26.1    
[57] compiler_3.6.1