Overlap apaQTLs and eQTLs

Last updated: 2019-06-05

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/rerunQTL_changePC.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_permuted.sh
    Modified:   code/apaQTLsnake.err
    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.

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	c743502	brimittleman	2019-06-05	first pass overlap

library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(workflowr)

This is workflowr version 1.3.0
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library(reshape2)


Attaching package: 'reshape2'

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    smiths

Files with eQTLs created in https://brimittleman.github.io/threeprimeseq/EmpDistforOverlaps.html

/project2/gilad/briana/threeprimeseq/data/eQTL_myanalysis/fastqtl_qqnorm_RNAseq_phase2.fixed.perm_GeneNames.out /project2/gilad/briana/threeprimeseq/data/eQTL_myanalysis/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal_GeneNames.out /project2/gilad/briana/threeprimeseq/data/eQTL_myanalysis/permRes_significanteQTLs_GeneNames.txt

/project2/gilad/briana/threeprimeseq/data/eQTL_myanalysis/permRes_NOTeQTLs_eneNames.txt

Subset the eQTL genes to those tested in apa.

mkdir ../data/overlapeQTLs/
python eQTLgenestestedapa.py

Not eGenes:

total Number of genes not tested in apa = 5233 nuclear Number of genes not tested in apa = 5151 total Number of genes not tested in apa = 165 nuclear Number of genes not tested in apa = 163

I need to make a file with the number of peaks per gene:

TotPeaks=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", header = T, stringsAsFactors = F) %>% select(pid) %>% separate(pid, into=c("chr", "start", "end", "peak"), sep=":") %>%  separate(peak, into=c("gene", "loc",'strand', 'peaknum'), sep="_")%>% group_by(gene) %>% summarise(NPeaks=n())

write.table(TotPeaks, file="../data/overlapeQTLs/TotalQTL_nPeaks.txt", quote=F, sep="\t", col.names = F, row.names = F)

NucPeaks=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T, stringsAsFactors = F) %>% select(pid) %>% separate(pid, into=c("chr", "start", "end", "peak"), sep=":") %>%  separate(peak, into=c("gene", "loc",'strand', 'peaknum'), sep="_")%>% group_by(gene) %>% summarise(NPeaks=n())

write.table(NucPeaks, file="../data/overlapeQTLs/NuclearQTL_nPeaks.txt", quote=F, sep="\t", col.names = F, row.names = F)

Make empirical distribution:

sbatch run_getApaPval4eqtl.sh

sbatch runMakeeQTLempirical.sh

Subset to the unexplained eQTLs

python subsetUnexplainedeQTLs.py

sbatch run_getapapval4eqtl_unexp.sh

#actual
nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
eQTLinTotal=read.table("../data/overlapeQTLs/eQTLinTotalApa.txt", stringsAsFactors = F, col.names = nomnames)
eQTLinNuclear=read.table("../data/overlapeQTLs/eQTLinNuclearApa.txt", stringsAsFactors = F, col.names = nomnames)
#empirical
empTotal=read.table("../data/overlapeQTLs/eQTL_Total_EmpiricalDist.txt", col.names = nomnames,stringsAsFactors = F)
empNuclear=read.table("../data/overlapeQTLs/eQTL_Nuclear_EmpiricalDist.txt", col.names = nomnames, stringsAsFactors = F)

toaddTotal=runif(nrow(eQTLinTotal)-nrow(empTotal))
toaddNuclear=runif(nrow(eQTLinNuclear)-nrow(empNuclear))
empNuclearUse= c(as.vector(empNuclear$pval),toaddNuclear)

empTotalUse= c(as.vector(empTotal$pval),toaddTotal)

Unexpplained:

#real
UneQTLinTotal=read.table("../data/overlapeQTLs/UnexplainedeQTLinTotalApa.txt", stringsAsFactors = F, col.names = nomnames)
UNeQTLinNuclear=read.table("../data/overlapeQTLs/UnexplainedeQTLinNuclearApa.txt", stringsAsFactors = F, col.names = nomnames)
#empirical
empTotalUn=read.table("../data/overlapeQTLs/eQTLUnexp_Total_EmpiricalDist.txt", col.names = nomnames,stringsAsFactors = F)
empNuclearUn=read.table("../data/overlapeQTLs/eQTLUnexp_Nuclear_EmpiricalDist.txt", col.names = nomnames, stringsAsFactors = F)

toaddTotalUn=runif(nrow(UneQTLinTotal)-nrow(empTotalUn))
toaddNuclearUn=runif(nrow(UNeQTLinNuclear)-nrow(empNuclearUn))
empNuclearUseUN= c(as.vector(empNuclearUn$pval),toaddNuclearUn)

empTotalUseUN= c(as.vector(empTotalUn$pval),toaddTotalUn)

#png("../output/plots/eqtlsinTotAPAQQPlot.png")
qqplot(-log10(empTotalUse), -log10(eQTLinTotal$pval),ylab="-log10 Total APA pval", xlab="Empirical expectation", main="eQTLs in totalAPA analysis")
points(sort(-log10(empTotalUseUN)), sort(-log10(UneQTLinTotal$pval)),col= alpha("Red"))
legend("topleft", legend=c("All eQTLs", "Unexplained eQTLs"),col=c("black", "red"), pch=16,bty = 'n')
abline(0,1)

#dev.off()

#png("../output/plots/eqtlsinNucAPAQQPlot.png")
qqplot(-log10(empNuclearUse), -log10(eQTLinNuclear$pval),ylab="-log10 Nuclear APA pval", xlab="Empirical expectation", main="eQTLs in nuclearAPA analysis")
points(sort(-log10(empNuclearUseUN)), sort(-log10(UNeQTLinNuclear$pval)),col= alpha("Red"))
legend("topleft", legend=c("All eQTLs", "Unexplained eQTLs"),col=c("black", "red"), pch=16,bty = 'n')
abline(0,1)

#dev.off()

Goals:

Proportion of unexplained we can explain now.

Number of unexplained we test:

UneQTLinTotal_sig= UneQTLinTotal  %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% filter(pval<.05) %>% group_by(gene) %>% summarise(nGenes=n()) %>% nrow()
UneQTLinTotal_sig

[1] 22

UneQTLinNuclear_sig= UNeQTLinNuclear %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% filter(pval<.05) %>% group_by(gene) %>% summarise(nGenes=n()) %>% nrow()
UneQTLinNuclear_sig

[1] 27

TestedunexpNuc=read.table("../data/overlapeQTLs/permRes_Unexplained_eQTLs_GeneNames_inNuc.txt") %>% nrow()
TestedunexpTot=read.table("../data/overlapeQTLs/permRes_Unexplained_eQTLs_GeneNames_inTot.txt") %>% nrow()

Proportion explained:

#total: 
UneQTLinTotal_sig/TestedunexpTot

[1] 0.3859649

#nuclear:
UneQTLinNuclear_sig/TestedunexpNuc

[1] 0.4736842

prop.test(x=c(UneQTLinTotal_sig,UneQTLinNuclear_sig), n=c(TestedunexpTot,TestedunexpNuc))


    2-sample test for equality of proportions with continuity
    correction

data:  c(UneQTLinTotal_sig, UneQTLinNuclear_sig) out of c(TestedunexpTot, TestedunexpNuc)
X-squared = 0.57268, df = 1, p-value = 0.4492
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.2862989  0.1108603
sample estimates:
   prop 1    prop 2 
0.3859649 0.4736842

Only looking at about 443 eGenes. Should I be using permuted for this?

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] reshape2_1.4.3  workflowr_1.3.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   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       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Overlap apaQTLs and eQTLs

Briana Mittleman

6/4/2019