Explained and unexplained eQTLs at gene level

Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/motifDisruption.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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
html	d1e8082	brimittleman	2019-06-12	Build site.
Rmd	486bba2	brimittleman	2019-06-12	new genos
html	4c50025	brimittleman	2019-06-06	Build site.
Rmd	8710e5c	brimittleman	2019-06-06	add new analysis for unex v ex

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()

I want to ask if the unexplained and explaiend eQTLs are in the same or different genes.

explainedGenes=read.table("../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt", stringsAsFactors = F, col.names = c("chr", "snp", "gene") )%>% select(gene) %>% unique() 

write.table(explainedGenes, file="../data/Li_eQTLs/explainedEgenes.txt", col.names = F, row.names = F, quote = F, sep="\t")

UNexplainedGenes=read.table("../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt", stringsAsFactors = F, col.names = c("chr", "snp", "gene") )%>% select(gene) %>% unique()
write.table(UNexplainedGenes, file="../data/Li_eQTLs/UnexplainedEgenes.txt",col.names = F, row.names = F, quote = F, sep="\t")

Make these vectors:

explainedGenesVec=explainedGenes$gene
UNexplainedGenesVec=UNexplainedGenes$gene

Overlap:

both <- UNexplainedGenesVec[UNexplainedGenesVec %in% explainedGenesVec]
both

character(0)

Plot the permuted pvalues for apa in 3 seperate lines. not eGenes, unexplained egenes, explained egenes

I want to make a script that takes these genes and a fraction and will give only the permuted apa values for that set. I will also make a script that will return the values for genes in neither set.

mkdir ../data/ApaByEgene
python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/explainedEgenes.txt Total ../data/ApaByEgene/TotalApaexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/UnexplainedEgenes.txt Total ../data/ApaByEgene/TotalApaUnexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/explainedEgenes.txt Nuclear ../data/ApaByEgene/NuclearApaexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/UnexplainedEgenes.txt Nuclear ../data/ApaByEgene/NuclearApaUnexplainedeGenes.txt


python subsetApanoteGene.py Total ../data/ApaByEgene/TotalApaNOTeGene.txt
python subsetApanoteGene.py Nuclear ../data/ApaByEgene/NuclearApaNOTeGene.txt

Make QQplots with these:

Total

tot.notE=read.table("../data/ApaByEgene/TotalApaNOTeGene.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.un=na.omit(tot.un)

qqplot(-log10(runif(nrow(tot.notE))), -log10(tot.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Total Apa")
points(sort(-log10(runif(nrow(tot.ex)))), sort(-log10(tot.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(tot.un)))), sort(-log10(tot.un$bpval)),col= alpha("Blue"))
abline(0,1)

legend("topleft", legend=c("Not eGenes", "Explained eGenes", "Unexplained eGenes"),col=c("black", "red", "blue"), pch=16,bty = 'n')

Version	Author	Date
d1e8082	brimittleman	2019-06-12
4c50025	brimittleman	2019-06-06

Nuclear

nuc.notE=read.table("../data/ApaByEgene/NuclearApaNOTeGene.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)

qqplot(-log10(runif(nrow(nuc.notE))), -log10(nuc.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(nuc.ex)))), sort(-log10(nuc.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(nuc.un)))), sort(-log10(nuc.un$bpval)),col= alpha("Blue"))
abline(0,1)
legend("topleft", legend=c("Not eGenes", "Explained eGenes", "Unexplained eGenes"),col=c("black", "red", "blue"), pch=16,bty = 'n')

Version	Author	Date
d1e8082	brimittleman	2019-06-12
4c50025	brimittleman	2019-06-06

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] 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_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.3.0  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_0.3.1      cli_1.0.1        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_1.2.6        
[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

Explained and unexplained eQTLs at gene level

Briana Mittleman

6/6/2019

Total

Nuclear