Last updated: 2020-01-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/Nuclear_HvC.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 3bc05f3 brimittleman 2020-01-06 add empirical pvalues
html e5dff29 brimittleman 2020-01-06 Build site.
Rmd 767ca26 brimittleman 2020-01-06 add eQTL enrichment for eQTLs
html c633e63 brimittleman 2020-01-06 Build site.
Rmd 5e85704 brimittleman 2020-01-06 add overlap with eQTLs 
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started

In this analysis I will look at the overlap between DE genes and eQTLs identified in Li et al 2016. This is similar to the anaylsis I did for dAPA and I will use this as a way to benchmark those results.

explained=read.table("../data/Li_eqtls/explainedEgenes.txt",col.names = c("Gene.name"),stringsAsFactors = F)
unexplained=read.table("../data/Li_eqtls/UnexplainedEgenes.txt",col.names = c("Gene.name"),stringsAsFactors = F)
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)


DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
DE_explained=DEgenes %>% inner_join(explained, by="Gene.name")
DE_unexplained=DEgenes %>% inner_join(unexplained, by="Gene.name")
neither=DEgenes %>% anti_join(explained, by="Gene.name") %>% anti_join(unexplained, by="Gene.name")


cat=c("Explained", "Unexaplained", "Neither")
num=c(nrow(DE_explained), nrow(DE_unexplained), nrow(neither))

DEqtl=as.data.frame(cbind(cat,num))

DEqtl$cat=factor(DEqtl$cat, levels=c("Explained","Unexaplained", "Neither"), ordered=T)
DEqtl$num=as.numeric(as.character(DEqtl$num ))

ggplot(DEqtl,aes(x=cat, y=num)) +geom_bar(stat="identity") + geom_text(aes(label=num), vjust=1.6, color="white", size=3.5) + labs(title="Differential Expression and human eQTL", y="Number of genes", x="Overlap")

Version Author Date
c633e63 brimittleman 2020-01-06

Proportion:

DEqtlProp=DEqtl %>% mutate(Prop=num/nrow(DEgenes))

ggplot(DEqtlProp,aes(x=cat, y=Prop)) +geom_bar(stat="identity") + geom_text(aes(label=round(Prop,digits = 3)), vjust=1.6, color="white", size=3.5) + labs(title="Differential Expression and human eQTL", y="Proportion of DE genes", x="Overlap")

Version Author Date
c633e63 brimittleman 2020-01-06

Look for enrichment:

permuteGenes <- function(InputGenes, nGenes, nTests){
  #InputGenes=NuclearAPAtested_genes
  #nGenes=nrow(sigNuclear_genes)
  #nTests=100
  explained=read.table("../data/Li_eqtls/explainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)
  unexplained=read.table("../data/Li_eqtls/UnexplainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)
  explainedOverlap=c()
  unexplainedOverlap=c()
  neitherOverlap=c()
  for (n in 1:nTests){
    genesTest=sample(InputGenes, nGenes)
    overlapE=intersect(genesTest, explained$genes) 
    overlapUN=intersect(genesTest, unexplained$genes) 
    diffs <- Reduce(setdiff,list(A = genesTest, B = explained$genes,C = unexplained$genes))
    explainedOverlap= c(explainedOverlap, length(overlapE))
    unexplainedOverlap=c(unexplainedOverlap,length(overlapUN))
    neitherOverlap=c(neitherOverlap, length(diffs))
  }
  DF=as.data.frame(cbind(explainedOverlap,unexplainedOverlap,neitherOverlap))
  return(DF)
}

I need to have all of the genes tested for eGenes.

DEtestedgenes=read.table("../data/DiffExpression/DE_Testedgenes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)

Perform permutations 1000 times

Expression_1000tests=permuteGenes(DEtestedgenes$Gene.name, nrow(DEgenes),1000 )

EEx=Expression_1000tests %>% filter(explainedOverlap>=nrow(DE_explained)) %>% nrow() / 1000
EUnEx=Expression_1000tests %>% filter(unexplainedOverlap>=nrow(DE_unexplained)) %>% nrow() / 1000

Plot as histograms

Explained

ggplot(Expression_1000tests,aes(x=explainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(DE_explained), col="red" )+labs(x="Number of Overlaps", title="DE overlap with explained eGenes") + annotate("text", x = 275, y = 30, label = paste("EmpPvalue=", EEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
e5dff29 brimittleman 2020-01-06

Unexplained

ggplot(Expression_1000tests,aes(x=unexplainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(DE_unexplained), col="red" ) +labs(x="Number of Overlaps", title="DE overlap with unexplained eGenes")+ annotate("text", x = 200, y = 30, label = paste("EmpPvalue=", EUnEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
e5dff29 brimittleman 2020-01-06 

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] workflowr_1.5.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4