SNPs in signal sites
Briana Mittleman
6/24/2019
Last updated: 2019-09-06
Checks: 6 1
Knit directory: apaQTL/analysis/
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| html | 416f8e8 | brimittleman | 2019-06-27 | Build site. |
| Rmd | b8e6035 | brimittleman | 2019-06-27 | add sig |
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| Rmd | 1aa270e | brimittleman | 2019-06-25 | change order and names |
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| Rmd | fffb14b | brimittleman | 2019-06-25 | add pvalue and effect size |
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| Rmd | 1683d57 | brimittleman | 2019-06-25 | add snp write out |
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| Rmd | 45839ab | brimittleman | 2019-06-25 | results from snp in each loc |
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| 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 1.4.0
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
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✖ 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_namesThe following object is masked from 'package:tidyr':
extractmkdir ../data/SNPinSSI 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.vcfI 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.vcfI 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.vcfAlternative 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 linePull 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.shResults 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"))
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"))
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 |
|---|---|---|
| 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.shNOSSsnpswithPAS=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("SNPs in UTR PAS Signal Sites", "SNPS not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')
| Version | Author | Date |
|---|---|---|
| 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("SNPs in UTR PAS Signal Sites", "SNPS not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')
| Version | Author | Date |
|---|---|---|
| 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 0wilcox.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_1.4.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.1 git2r_0.25.2 highr_0.7 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_0.4.0 cli_1.1.0 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_1.3.1
[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] 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 labeling_0.3
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[49] crayon_1.3.4