RiboQTL QQplot
Briana Mittleman
2/20/2020
Last updated: 2020-02-27
Checks: 7 0
Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/ExploreNpas.Rmd
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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 | 7449f09 | brimittleman | 2020-02-27 | add figure spec |
| html | 1ca62a2 | brimittleman | 2020-02-20 | Build site. |
| 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 vectorriboRes$bh=p.adjust(riboRes$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.txttot.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$bpval)),col= alpha("blue"))
abline(0,1)
legend("topleft", legend=c("Neither eGenes nor rGenes", "rGenes", "eGenes"),col=c("black", "red","blue"), pch=16,bty = 'n')
| Version | Author | Date |
|---|---|---|
| 1ca62a2 | brimittleman | 2020-02-20 |
wilcox.test(tot.RS$bpval,tot.notEorR$bpval,alternative="less")
Wilcoxon rank sum test with continuity correction
data: tot.RS$bpval and tot.notEorR$bpval
W = 9336600, p-value = 0.0001811
alternative hypothesis: true location shift is less than 0wilcox.test(tot_allE$bpval,tot.notEorR$bpval,alternative="less")
Wilcoxon rank sum test with continuity correction
data: tot_allE$bpval and tot.notEorR$bpval
W = 43090000, p-value = 1.878e-11
alternative hypothesis: true location shift is less than 0wilcox.test(tot.RS$bpval,tot_allE$bpval,alternative="less")
Wilcoxon rank sum test with continuity correction
data: tot.RS$bpval and tot_allE$bpval
W = 1554300, p-value = 0.3891
alternative hypothesis: true location shift is less than 0nuc.notEorR=read.table("../data/ApaByRgene/NotRorE_nuclear.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.RS=read.table("../data/ApaByRgene/NuclearApaRGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.ES=read.table("../data/ApaByPgene/NuclearApaESGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
nuc.ex=read.table("../data/ApaByEgene/NuclearApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.un=read.table("../data/ApaByEgene/NuclearApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
nuc.un=na.omit(nuc.un)
nuc.RS=na.omit(nuc.RS)
nuc.notEorR=na.omit(nuc.notEorR)
nuc.ES=na.omit(nuc.ES)
nuc_allE=as.data.frame(rbind(nuc.ex,nuc.un))qqplot(-log10(runif(nrow(nuc.notEorR))), -log10(nuc.notEorR$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')
| Version | Author | Date |
|---|---|---|
| 1ca62a2 | brimittleman | 2020-02-20 |
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 0wilcox.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 0wilcox.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