RiboQTL QQplot

Last updated: 2020-02-20

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.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.

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File	Version	Author	Date	Message
Rmd	662a604	brimittleman	2020-02-20	add riboqq and PM coloc

In the paper I have qqPlots where I took the apaQTL data and subset it by QTL sets. I never did this for riboQTLs. I will get the riboQTLs from Battle et al and remake the plot including these.

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()

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

riboRes=read.table("../data/Battle_pQTL/riboQTLs_battle.txt", header = T,stringsAsFactors = F) %>% inner_join(geneNames,by="ENSG") 

exPe=read.table("../data/Li_eQTLs/explainedEgenes.txt", col.names = "GeneName")
UexPe=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt",col.names = "GeneName")

allE=exPe %>% full_join(UexPe,by="GeneName")

Warning: Column `GeneName` joining factors with different levels, coercing
to character vector

riboRes$bh=p.adjust(riboRes$perm.p.values, method="fdr")

riboRes_sig= riboRes %>% filter(-log10(bh)>=1)

riboGenes=riboRes_sig %>% select(GeneName) %>% anti_join(allE)

Joining, by = "GeneName"

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

mkdir ../data/ApaByRgene

python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/RiboQTLGeneNames.txt Nuclear ../data/ApaByRgene/NuclearApaRGenes.txt

python subsetpermAPAwithGenelist.py ../data/Battle_pQTL/RiboQTLGeneNames.txt Total ../data/ApaByRgene/TotalApaRGenes.txt

python subsetAPAnotEorR.py Nuclear ../data/ApaByRgene/NotRorE_nuclear.txt
python subsetAPAnotEorR.py Total ../data/ApaByRgene/NotRorE_total.txt

tot.notEorR=read.table("../data/ApaByRgene/NotRorE_total.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.RS=read.table("../data/ApaByRgene/TotalApaRGenes.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.RS=na.omit(tot.RS)
tot.notEorR=na.omit(tot.notEorR)
tot.ES=na.omit(tot.ES)
tot.un=na.omit(tot.un)

qqplot(-log10(runif(nrow(tot.notEorR))), -log10(tot.notEorR$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Total Apa")
points(sort(-log10(runif(nrow(tot.RS)))), sort(-log10(tot.RS$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 rGenes", "rGenes", "eGenes"),col=c("black", "red","blue"), pch=16,bty = 'n')

wilcox.test(tot.RS$bpval,tot.notEorR$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  tot.RS$bpval and tot.notEorR$bpval
W = 9336600, p-value = 0.0001811
alternative hypothesis: true location shift is less than 0

wilcox.test(tot_allE$bpval,tot.notEorR$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  tot_allE$bpval and tot.notEorR$bpval
W = 43090000, p-value = 1.878e-11
alternative hypothesis: true location shift is less than 0

wilcox.test(tot.RS$bpval,tot_allE$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  tot.RS$bpval and tot_allE$bpval
W = 1554300, p-value = 0.3891
alternative hypothesis: true location shift is less than 0

nuc.notEorR=read.table("../data/ApaByRgene/NotRorE_nuclear.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.RS=read.table("../data/ApaByRgene/NuclearApaRGenes.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.RS=na.omit(nuc.RS)
nuc.notEorR=na.omit(nuc.notEorR)
nuc.ES=na.omit(nuc.ES)
nuc_allE=as.data.frame(rbind(nuc.ex,nuc.un))

qqplot(-log10(runif(nrow(nuc.notEorR))), -log10(nuc.notEorR$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(nuc.RS)))), sort(-log10(nuc.RS$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 rGenes", "rGenes", "eGenes"),col=c("black", "red","blue","green"), pch=16,bty = 'n')

wilcox.test(nuc.RS$bpval,nuc.notEorR$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  nuc.RS$bpval and nuc.notEorR$bpval
W = 13007000, p-value = 0.01029
alternative hypothesis: true location shift is less than 0

wilcox.test(nuc_allE$bpval,nuc.notEorR$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  nuc_allE$bpval and nuc.notEorR$bpval
W = 57056000, p-value = 6.194e-13
alternative hypothesis: true location shift is less than 0

wilcox.test(nuc.RS$bpval,nuc_allE$bpval,alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  nuc.RS$bpval and nuc_allE$bpval
W = 2189300, p-value = 0.8658
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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
[5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
[9] 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     workflowr_1.6.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     promises_1.0.1   htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.3-2 httpuv_1.4.5     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

RiboQTL QQplot

Briana Mittleman

2/20/2020