Last updated: 2020-09-10

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Rmd 5f95cbc Benjmain Fair 2020-09-10 update site
html 5f95cbc Benjmain Fair 2020-09-10 update site

Original reviewer point:

I would like to see more discussion about the inter-relatedness of the chimpanzees in the analysis of gene expression. Is that contributing to the power of the DE analysis, which has really high numbers of DE genes. That may certainly be due to the large samples size, but should be addressed. Related to that, the support that the gene-wise dispersion estimates are well correlated in humans and chimpanzees overall (Fig1C, and S4) seems qualitative. It looks like the chimpanzees might have less dispersion overall?

I will address the first point (about relatedness in DE analysis) empricially by reporfming DE analysis with subsamples of chimps that have some relatively high degree of inter-relatedness, compared to subsamples that do not have such relatedness.

First load necessary libraries for analysis…

library(tidyverse)
library(gplots)
library(readxl)
library(scales)

Load relatedness matrix and RNA-seq sample batch info

#Relatedness matrix
SampleLabels <- read.table('../output/ForAssociationTesting.temp.fam', stringsAsFactors = F)$V2
GemmaMatrix <- as.matrix(read.table('../output/GRM.cXX.txt'))
colnames(GemmaMatrix) <- SampleLabels
row.names(GemmaMatrix) <- SampleLabels

#Metadata like RNA-seq batch
Metadata<-read_excel("../data/Metadata_SequencedChimps.xlsx")
Metadata$RNA.Extract_date %>% unique() %>% length()
[1] 5
Metadata$RNA.Library.prep.batch %>% unique() %>% length()
[1] 5
Colors <- data.frame(Numbers=1:5, Colors=hue_pal()(5))

BatchColor <- data.frame(IndividualID=as.character(colnames(GemmaMatrix))) %>% 
  left_join(Metadata, by=c("IndividualID"="IndividualID (as listed in vcf)")) %>%
  dplyr::select(IndividualID, RNA.Library.prep.batch) %>%
  left_join(Colors, by=c("RNA.Library.prep.batch"="Numbers")) %>%
  pull(Colors) %>% as.character()

diag(GemmaMatrix) <- NA #For plotting purposes, just don't show the diagonol so that the color scale is better for non-diagnol entries
heatmap.2(GemmaMatrix, trace="none", RowSideColors = BatchColor, ColSideColors = BatchColor)

Version Author Date
5f95cbc Benjmain Fair 2020-09-10

I may pick out some of those somewhat related sample blocks for DE analysis… First, I should also try to match batches for this analysis.


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] scales_1.1.0    readxl_1.3.1    gplots_3.0.1.1  forcats_0.4.0  
 [5] stringr_1.4.0   dplyr_0.8.3     purrr_0.3.3     readr_1.3.1    
 [9] tidyr_1.0.0     tibble_2.1.3    ggplot2_3.2.1   tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         lubridate_1.7.4    lattice_0.20-38    gtools_3.8.1      
 [5] assertthat_0.2.1   zeallot_0.1.0      rprojroot_1.3-2    digest_0.6.23     
 [9] R6_2.4.1           cellranger_1.1.0   backports_1.1.5    reprex_0.3.0      
[13] evaluate_0.14      httr_1.4.1         pillar_1.4.2       rlang_0.4.1       
[17] lazyeval_0.2.2     rstudioapi_0.10    gdata_2.18.0       whisker_0.4       
[21] rmarkdown_1.18     munsell_0.5.0      broom_0.5.2        compiler_3.6.1    
[25] httpuv_1.5.2       modelr_0.1.5       xfun_0.11          pkgconfig_2.0.3   
[29] htmltools_0.4.0    tidyselect_0.2.5   workflowr_1.5.0    fansi_0.4.0       
[33] crayon_1.3.4       dbplyr_1.4.2       withr_2.1.2        later_1.0.0       
[37] bitops_1.0-6       grid_3.6.1         nlme_3.1-143       jsonlite_1.6      
[41] gtable_0.3.0       lifecycle_0.1.0    DBI_1.0.0          git2r_0.26.1      
[45] magrittr_1.5       KernSmooth_2.23-16 cli_2.0.0          stringi_1.4.3     
[49] farver_2.0.1       fs_1.3.1           promises_1.1.0     xml2_1.2.2        
[53] generics_0.0.2     vctrs_0.2.0        tools_3.6.1        glue_1.3.1        
[57] hms_0.5.2          yaml_2.2.0         colorspace_1.4-1   caTools_1.17.1.3  
[61] rvest_0.3.5        knitr_1.26         haven_2.2.0