Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
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    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
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    Modified:   code/apaQTLsnake.err
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    Modified:   docs/figure/propeQTLs_explained.Rmd/figure3B-1.pdf
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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 1.4.0
Run ?workflowr for help getting started

This analysis will be similar to the explained and unexplained eQTL analysis available here. I downloaded the protein and expression specific QTLs from Battle et al. 2015. This information is in supplimentary table s2.

First I need to convert the ENSG ids.

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F, header = T)

psQTL=read.table("../data/Battle_pQTL/psQTLGenes.txt", header = T, stringsAsFactors = F, col.names="gene_id") %>% inner_join(geneNames, by="gene_id") %>% select(GeneName)

write.table(psQTL, file="../data/Battle_pQTL/psQTLGeneNames.txt", row.names = F, col.names = F, quote = F, sep="\t")

esQTL=read.table("../data/Battle_pQTL/esQTLGenes.txt", header = T, stringsAsFactors = F, col.names="gene_id") %>% inner_join(geneNames, by="gene_id") %>% select(GeneName)
write.table(esQTL, file="../data/Battle_pQTL/esQTLGeneNames.txt", row.names = F, col.names = F, quote = F, sep="\t")

Now I cat use the subsetpermAPAwithGenelist.py code to subset my results:

mkdir ../data/ApaByPgene
python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/psQTLGeneNames.txt Total ../data/ApaByPgene/TotalApaPSGenes.txt

python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/esQTLGenes.txt Total ../data/ApaByPgene/TotalApaESGenes.txt

python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/psQTLGeneNames.txt Nuclear ../data/ApaByPgene/NuclearApaPSGenes.txt

python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/esQTLGenes.txt Nuclear ../data/ApaByPgene/NuclearApaESGenes.txt

I also need those not in eQTL or pQTL:

python subsetAPAnotEorPgene.py Total ../data/ApaByPgene/TotalApaNOTPorEGenes.txt
python subsetAPAnotEorPgene.py Nuclear ../data/ApaByPgene/NuclearApaNOTPorEGenes.txt

Total:

tot.notEorP=read.table("../data/ApaByPgene/TotalApaNOTPorEGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.PS=read.table("../data/ApaByPgene/TotalApaPSGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.ES=read.table("../data/ApaByPgene/TotalApaESGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
tot.ex=read.table("../data/ApaByEgene/TotalApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.un=read.table("../data/ApaByEgene/TotalApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )

tot_allE=as.data.frame(rbind(tot.ex,tot.un))
tot.PS=na.omit(tot.PS)
tot.notEorP=na.omit(tot.notEorP)
tot.ES=na.omit(tot.ES)
tot.un=na.omit(tot.un)
qqplot(-log10(runif(nrow(tot.notEorP))), -log10(tot.notEorP$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Total Apa")
points(sort(-log10(runif(nrow(tot.PS)))), sort(-log10(tot.PS$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(tot_allE)))), sort(-log10(tot_allE$bpval)),col= alpha("blue"))
abline(0,1)

legend("topleft", legend=c("Neither eGenes nor pGenes", "pGenes", "eGenes"),col=c("black", "red","blue"), pch=16,bty = 'n')

Version Author Date
3db496b brimittleman 2019-06-21
71ae708 brimittleman 2019-06-14
15962c8 brimittleman 2019-06-14 
 wilcox.test(tot.PS$bpval,tot.notEorP$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  tot.PS$bpval and tot.notEorP$bpval
W = 3608700, p-value = 0.0015
alternative hypothesis: true location shift is less than 0
 wilcox.test(tot_allE$bpval,tot.notEorP$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  tot_allE$bpval and tot.notEorP$bpval
W = 44345000, p-value = 6.026e-11
alternative hypothesis: true location shift is less than 0
wilcox.test(tot.PS$bpval,tot_allE$bpval, alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  tot.PS$bpval and tot_allE$bpval
W = 584110, p-value = 0.1636
alternative hypothesis: true location shift is less than 0

Nuclear:

nuc.notEorP=read.table("../data/ApaByPgene/NuclearApaNOTPorEGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.PS=read.table("../data/ApaByPgene/NuclearApaPSGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.ES=read.table("../data/ApaByPgene/NuclearApaESGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
nuc.ex=read.table("../data/ApaByEgene/NuclearApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.un=read.table("../data/ApaByEgene/NuclearApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )

nuc.un=na.omit(nuc.un)
nuc.PS=na.omit(nuc.PS)
nuc.notEorP=na.omit(nuc.notEorP)
nuc.ES=na.omit(nuc.ES)
nuc_allE=as.data.frame(rbind(nuc.ex,nuc.un))
qqplot(-log10(runif(nrow(nuc.notEorP))), -log10(nuc.notEorP$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(nuc.PS)))), sort(-log10(nuc.PS$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(nuc_allE)))), sort(-log10(nuc_allE$bpval)),col= alpha("blue"))
abline(0,1)

legend("topleft", legend=c("Neither eGenes nor pGenes", "pGenes", "eGenes"),col=c("black", "red","blue","green"), pch=16,bty = 'n')

Version Author Date
416f8e8 brimittleman 2019-06-27 
 wilcox.test(nuc.PS$bpval,nuc.notEorP$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  nuc.PS$bpval and nuc.notEorP$bpval
W = 4945300, p-value = 0.0006142
alternative hypothesis: true location shift is less than 0
 wilcox.test(nuc_allE$bpval,nuc.notEorP$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  nuc_allE$bpval and nuc.notEorP$bpval
W = 58722000, p-value = 1.375e-12
alternative hypothesis: true location shift is less than 0
  wilcox.test(nuc.PS$bpval,nuc_allE$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  nuc.PS$bpval and nuc_allE$bpval
W = 810940, p-value = 0.148
alternative hypothesis: true location shift is less than 0

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_1.4.0 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_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        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] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4