SNPs in signal sites

Last updated: 2020-03-23

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/apaQTLoverlap.Rmd
    Modified:   analysis/apabyeQTLstatus.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/reads_graphs.Rmd
    Modified:   analysis/splicesitestrength.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/apaqtlfacetboxplots.R
    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	b93e5d6	brimittleman	2020-03-23	fix sup figs for revision
html	22541b3	brimittleman	2019-09-06	Build site.
Rmd	5976ed7	brimittleman	2019-09-06	update post new filter
html	416f8e8	brimittleman	2019-06-27	Build site.
Rmd	b8e6035	brimittleman	2019-06-27	add sig
html	77700ac	brimittleman	2019-06-25	Build site.
Rmd	1aa270e	brimittleman	2019-06-25	change order and names
html	11a3069	brimittleman	2019-06-25	Build site.
Rmd	fffb14b	brimittleman	2019-06-25	add pvalue and effect size
html	9011f4b	brimittleman	2019-06-25	Build site.
Rmd	1683d57	brimittleman	2019-06-25	add snp write out
html	e767391	brimittleman	2019-06-25	Build site.
Rmd	45839ab	brimittleman	2019-06-25	results from snp in each loc
html	433092b	brimittleman	2019-06-24	Build site.
Rmd	78dd5da	brimittleman	2019-06-24	add snp in ss analysis

In this analysis I want to ask if snps in a signal site are more likely to be apaQTLs than other snps close to the PAS. In order to do this i need to subset to the pas that have signal site (identified here) I will then identyify the region 50 bp upstream of the PAS and ask if there are snps in this region using the vcf files for the snps i tested.

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

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()

library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

mkdir ../data/SNPinSS

I want a bed file with 50bp upstream of these PAS.

PASwSS=read.table("../data/PAS/PASwSignalSite.txt", header = T,stringsAsFactors = F)
PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, header = F, col.names = c("chr", "start", "end", "PASid","score", "strand")) %>% separate(PASid, into=c("pasNum", "geneiD"), sep=":") %>% mutate(PAS=paste("peak", pasNum, sep=""),PASname=paste(PAS, geneiD, sep="_")) 

PASwSSregion=PASwSS %>% inner_join(PAS, by="PAS") %>% mutate(newEnd=ifelse(strand=="+", end+50, end),newStart=ifelse(strand=="+", start, start-50)) %>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(PASwSSregion,"../data/SNPinSS/FiftyupstreamPASwSS.bed", col.names = F, row.names = F, quote = F, sep="\t")


sort -k1,1 -k2,2n ../data/SNPinSS/FiftyupstreamPASwSS.bed > ../data/SNPinSS/FiftyupstreamPASwSS.sortbed
sbatch subsetVCF_upstreamPAS.sh

cat  ../data/SNPinSS/SNPSinFiftyupstreamPAS_chr* >../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf

I want to further subset to those in a signal site.

SSregions=PASwSS %>% inner_join(PAS, by="PAS") %>% mutate(absdist=abs(UpstreamDist),newEnd= ifelse(strand=="+", end-absdist, end+absdist), newStart=ifelse(strand=="+", end- (absdist+6), end + (absdist-6)), length=newEnd-newStart) %>% select(chr, newStart,newEnd, PASname, score, strand) 


write.table(SSregions,"../data/SNPinSS/SignalSiteRegions.bed", col.names = F, row.names = F, quote = F, sep="\t")

../data/SNPinSS/SSregions_Allchr.sort.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SignalSiteRegions.bed > ../data/SNPinSS/SignalSiteRegions.sort.bed
sbatch subsetVCF_SS.sh

cat  ../data/SNPinSS/SSregions_chr* > ../data/SNPinSS/SSregions_Allchr.recode.vcf

I will also need a different region to comare to. I can just shift these regions upstream by 7

SS_diffregion=SSregions %>% mutate(randStart=ifelse(strand=="+", newStart-7, newEnd), randend=ifelse(strand=="+", newStart, newEnd+7), length=randend-randStart) %>% select(chr, randStart,randend, PASname, score, strand) 

write.table(SS_diffregion,"../data/SNPinSS/OtherSSRegions.bed", col.names = F, row.names = F, quote = F, sep="\t")


sort -k1,1 -k2,2n ../data/SNPinSS/OtherSSRegions.bed  > ../data/SNPinSS/OtherSSRegions.sort.bed 
sbatch subsetvcf_otherreg.sh 

cat  ../data/SNPinSS/Otherregions_chr* > ../data/SNPinSS/Otherregions_Allchr.recode.vcf

Alternative option, permute the distances:

permdist=sample(PASwSS$UpstreamDist, length(PASwSS$UpstreamDist), replace = F)

SSregions_perm=as.data.frame(cbind(PASwSS,  permdist))%>% inner_join(PAS, by="PAS")  %>% mutate(absdist=abs(permdist),newEnd= ifelse(strand=="+", end-absdist, end+absdist), newStart=ifelse(strand=="+", end- (absdist+6), end + (absdist-6)), length=newEnd-newStart)%>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(SSregions_perm,"../data/SNPinSS/SSRegions_permuted.bed", col.names = F, row.names = F, quote = F, sep="\t")


sort -k1,1 -k2,2n ../data/SNPinSS/SSRegions_permuted.bed > ../data/SNPinSS/SSRegions_permuted.sort.bed
sbatch subsetvcf_permSS.sh 

cat  ../data/SNPinSS/SSRegionsPerm_chr* > ../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf
#remove # in first line

Pull in QTL snps:

totQTLs=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% select(sid) %>% unique()
write.table(totQTLs,"../data/apaQTLs/TotalQTLSNPsRSID.txt", col.names = F, row.names = F, quote = F)

nucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% select(sid) %>% unique()
write.table(nucQTLs,"../data/apaQTLs/NuclearQTLSNPsRSID.txt", col.names = F, row.names = F, quote = F)

Signal site results:

SS_snps=read.table("../data/SNPinSS/SSregions_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))


permutedSS_snps=read.table("../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

otherReg_snp=read.table("../data/SNPinSS/Otherregions_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

fiftybp_snp=read.table("../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

There are only 2 qtl snps in these signal sites. This is not enough to draw anything from this.

Try with pvalues. Are the snps in pvals more likely to be significant than those not.

I need to figure out which peak is associated with each snp.

I can make a bedfile from the SS snps in python and overlap this with the Signal site regions.

python vcf2bed.py ../data/SNPinSS/SSregions_Allchr.recode.vcf  ../data/SNPinSS/SSregions_Allchr.bed

sort -k1,1 -k2,2n ../data/SNPinSS/SSregions_Allchr.bed > ../data/SNPinSS/SSregions_Allchr.sort.bed


python vcf2bed.py ../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf  ../data/SNPinSS/SSRegionsPerm_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SSRegionsPerm_Allchr.bed> ../data/SNPinSS/SSRegionsPerm_Allchr.sort.bed



python vcf2bed.py ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf  ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.bed > ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.sort.bed


python vcf2bed.py ../data/SNPinSS/Otherregions_Allchr.recode.vcf  ../data/SNPinSS/Otherregions_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/Otherregions_Allchr.bed > ../data/SNPinSS/Otherregions_Allchr.sort.bed


sort -k1,1 -k2,2n ../data/SNPinSS/SignalSiteRegions.bed > ../data/SNPinSS/SignalSiteRegions.sort.bed


sort -k1,1 -k2,2n ../data/SNPinSS/SSRegions_permuted.bed >../data/SNPinSS/SSRegions_permuted.sort.bed

sort -k1,1 -k2,2n ../data/SNPinSS/FiftyupstreamPASwSS.bed > ../data/SNPinSS/FiftyupstreamPASwSS.sort.bed

sort -k1,1 -k2,2n ../data/SNPinSS/OtherSSRegions.bed > ../data/SNPinSS/OtherSSRegions.sort.bed

intersect with bedtools to map the snps to the regions. Then I will be able to select the snp PAS associations.

sbatch mapSSsnps2PAS.sh

Results to get the associations:

SSsnpswithPAS=read.table("../data/SNPinSS/SNPinSS2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SSsnpswithPAS, "../data/SNPinSS/SS_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")


SSsnpswithPERMPAS=read.table("../data/SNPinSS/SNPinPermSS2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SSsnpswithPERMPAS, "../data/SNPinSS/PermSS_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

SNPregion=read.table("../data/SNPinSS/SNPSinFiftyupstream2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SNPregion, "../data/SNPinSS/PASregion_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")


Otherregion=read.table("../data/SNPinSS/Otherregions2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(Otherregion, "../data/SNPinSS/Otherregions_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

Process the snp region with python

python fixPASregionSNPs.py

Run this with total and nuclear


sbatch RunRes2PAS.sh

#all of this code is in the above batch script 
python NomResfromPASSNP.py ../data/SNPinSS/SS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/SS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/SS_Total_nomRes.txt 


python NomResfromPASSNP.py ../data/SNPinSS/PermSS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/PermSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/PermSS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/PermSS_Total_nomRes.txt 


python NomResfromPASSNP.py ../data/SNPinSS/PASregion_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/RegionSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/PASregion_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/RegionSS_Total_nomRes.txt  

python NomResfromPASSNP.py ../data/SNPinSS/Otherregions_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/OtherSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/Otherregions_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/OtherSS_Total_nomRes.txt

Nuclear_SS=read.table('../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="SS")
Nuclear_Perm=read.table('../data/apaQTLNominal_4pc/PermSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Permuted")

Total_SS=read.table('../data/apaQTLNominal_4pc/SS_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="SS")
Total_Perm=read.table('../data/apaQTLNominal_4pc/PermSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Permuted")


Nuclear_Region=read.table('../data/apaQTLNominal_4pc/RegionSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Region")

Total_Region=read.table('../data/apaQTLNominal_4pc/RegionSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Region")

Nuclear_other=read.table('../data/apaQTLNominal_4pc/OtherSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Upstream")

Total_other=read.table('../data/apaQTLNominal_4pc/OtherSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Upstream")


all_SS_pval=bind_rows(Nuclear_SS,Nuclear_Perm,Total_SS,Total_Perm,Nuclear_Region,Total_Region,Nuclear_other,Total_other)

all_SS_pval$set <- factor(all_SS_pval$set, levels=c("SS", "Upstream", "Permuted", "Region"))

ggplot(all_SS_pval, aes(x=fraction, fill=set, y=pval)) + geom_boxplot() + labs(x="Fraction", title="p-values for SNPs in Signal Sites",y="nominal P-value" ) + scale_fill_discrete(name = 'Set', labels = c('Signal Sites', 'Region upstream of Signal Site', 'Permuted Distance to Signal Site',"50 bp upstream of PAS"))

Version	Author	Date
22541b3	brimittleman	2019-09-06
77700ac	brimittleman	2019-06-25
11a3069	brimittleman	2019-06-25

ggplot(all_SS_pval, aes(x=fraction, fill=set, y=abs(slope))) + geom_boxplot()  + labs(x="Fraction", title="p-values for SNPs in Signal Sites",y="absolute value effect size" ) + scale_fill_discrete(name = 'Set', labels = c('Signal Sites', 'Region upstream of Signal Site', 'Permuted Distance to Signal Site',"50 bp upstream of PAS"))

Version	Author	Date
22541b3	brimittleman	2019-09-06
77700ac	brimittleman	2019-06-25
11a3069	brimittleman	2019-06-25

I am plotting this the wrong way. I need to make qqplots with the snps.

#plot qqplot
qqplot(-log10(runif(nrow(Nuclear_SS))), -log10(Nuclear_SS$pval))
points(sort(-log10(runif(nrow(Total_SS)))), sort(-log10(Total_SS$pval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(Nuclear_Region)))), sort(-log10(Nuclear_Region$pval)),col= alpha("Orange"))
points(sort(-log10(runif(nrow(Total_Region)))), sort(-log10(Total_Region$pval)),col= alpha("Green"))

abline(0,1)

Version	Author	Date
22541b3	brimittleman	2019-09-06
416f8e8	brimittleman	2019-06-27

This is close but i need to make this a better comparison.I want just the UTR variants.

Nuclear_SS_all=read.table('../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)  %>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total_SS_all=read.table('../data/apaQTLNominal_4pc/SS_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)%>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

The benchmark set is the UTRs without signal sites

PASwSS=read.table("../data/PAS/PASwSignalSite.txt", header = T,stringsAsFactors = F)
PAS_noSS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, header = F, col.names = c("chr", "start", "end", "PASid","score", "strand")) %>% separate(PASid, into=c("pasNum", "geneiD"), sep=":") %>% mutate(PAS=paste("peak", pasNum, sep=""),PASname=paste(PAS, geneiD, sep="_")) %>% anti_join(PASwSS, by="PAS") %>% separate(geneiD,into=c("gene", "loc"), sep="_") %>% filter(loc=="utr3") %>% mutate(newEnd=ifelse(strand=="+", end+50, end),newStart=ifelse(strand=="+", start, start-50))%>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(PAS_noSS,"../data/SNPinSS/UTRregionsPASnoSS.bed", col.names = F, row.names = F, quote = F, sep="\t")


sort -k1,1 -k2,2n ../data/SNPinSS/UTRregionsPASnoSS.bed > ../data/SNPinSS/UTRregionsPASnoSS.sort.bed


sbatch subsetVCF_noSSregions.sh

cat  ../data/SNPinSS/UTRnoSS_chr* >../data/SNPinSS/UTRnoSS_Allchr.recode.vcf


python vcf2bed.py ../data/SNPinSS/UTRnoSS_Allchr.recode.vcf  ../data/SNPinSS/UTRnoSS_SNPsAllchr.bed

sort -k1,1 -k2,2n ../data/SNPinSS/UTRregionsPASnoSS.bed > ../data/SNPinSS/UTRregionsPASnoSS.sort.bed
sort -k1,1 -k2,2n ../data/SNPinSS/UTRnoSS_SNPsAllchr.bed > ../data/SNPinSS/UTRnoSS_SNPsAllchr.sort.bed

sbatch mapSSsnps2PAS.sh

NOSSsnpswithPAS=read.table("../data/SNPinSS/UTRnoSS_SNPsAllchr2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(NOSSsnpswithPAS, "../data/SNPinSS/NoSSUTR_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

Get the pval:

python NomResfromPASSNP.py ../data/SNPinSS/NoSSUTR_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/NoSSUTR_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/NoSSUTR_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/NoSSUTR_Total_nomRes.txt

Nuclear_NOSS=read.table('../data/apaQTLNominal_4pc/NoSSUTR_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)  %>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total_NOSS=read.table('../data/apaQTLNominal_4pc/NoSSUTR_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)%>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total

#plot qqplot
qqplot(-log10(runif(nrow(Total_NOSS))), -log10(Total_NOSS$pval), xlab="-log10(Uniform)", ylab="-log10(pval)", main="Total Apa")
points(sort(-log10(runif(nrow(Total_SS)))), sort(-log10(Total_SS$pval)),col= alpha("Red"))
abline(0,1)
legend("topleft", legend=c("apaQTLs in UTR PAS Signal Sites", "apaQLTs not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')

Version	Author	Date
22541b3	brimittleman	2019-09-06
416f8e8	brimittleman	2019-06-27

Nuclear:

qqplot(-log10(runif(nrow(Nuclear_NOSS))), -log10(Nuclear_NOSS$pval),xlab="-log10(Uniform)", ylab="-log10(pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(Nuclear_SS)))), sort(-log10(Nuclear_SS$pval)),col= alpha("Red"))
abline(0,1)

legend("topleft", legend=c("apaQTLs in UTR PAS Signal Sites", "apaQTLs not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')

Version	Author	Date
22541b3	brimittleman	2019-09-06
416f8e8	brimittleman	2019-06-27

Assess significance:

wilcox.test(Nuclear_SS$pval,Nuclear_NOSS$pval,alternative = 'less')


    Wilcoxon rank sum test with continuity correction

data:  Nuclear_SS$pval and Nuclear_NOSS$pval
W = 13543, p-value = 0.2143
alternative hypothesis: true location shift is less than 0

wilcox.test(Total_SS$pval,Total_NOSS$pval, alternative = "less")


    Wilcoxon rank sum test with continuity correction

data:  Total_SS$pval and Total_NOSS$pval
W = 14728, p-value = 0.5313
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] ggpubr_0.2      magrittr_1.5    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 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1

SNPs in signal sites

Briana Mittleman

6/24/2019