Explained and unexplained eQTLs at gene level

Last updated: 2019-06-12

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
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    Modified:   analysis/Readdistagainstfeatures.Rmd
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    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
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    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/config.yaml
    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.

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File	Version	Author	Date	Message
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
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
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