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

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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 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  
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── 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 )

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")
Warning: Ignoring unknown parameters: binwidth, bins, pad

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")
Warning: Ignoring unknown parameters: binwidth, bins, pad


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