Last updated: 2019-06-25

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Knit directory: apaQTL/analysis/

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    Modified:   analysis/NuclearSpecAPAqtl.Rmd
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    Deleted:    code/test.txt

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Rmd fffb14b brimittleman 2019-06-25 add pvalue and effect size
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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)
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library(tidyverse)
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    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_withCHR.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")
sed 's/^chr//' ../data/SNPinSS/FiftyupstreamPASwSS.bed  > ../data/SNPinSS/FiftyupstreamPASwSS.nochr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/FiftyupstreamPASwSS.nochr.bed > ../data/SNPinSS/FiftyupstreamPASwSS.nochr.sort.bed
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")
sed 's/^chr//' ../data/SNPinSS/SignalSiteRegions.bed  > ../data/SNPinSS/SignalSiteRegions.nochr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SignalSiteRegions.nochr.bed > ../data/SNPinSS/SignalSiteRegions.nochr.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")
sed 's/^chr//' ../data/SNPinSS/OtherSSRegions.bed  > ../data/SNPinSS/OtherSSRegions.nochr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/OtherSSRegions.nochr.bed > ../data/SNPinSS/OtherSSRegions.nochr.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")
sed 's/^chr//' ../data/SNPinSS/SSRegions_permuted.bed  > ../data/SNPinSS/SSRegions_permuted.nochr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SSRegions_permuted.nochr.bed > ../data/SNPinSS/SSRegions_permuted.nochr.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

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)
ggplot(all_SS_pval, aes(x=fraction, fill=set, y=pval)) + geom_boxplot()

ggplot(all_SS_pval, aes(x=fraction, fill=set, y=abs(slope))) + geom_boxplot() + labs(y="absolute value effect size")

Does not look like there is an effect here.


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 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     whisker_0.3-2   
[37] backports_1.1.2  scales_1.0.0     htmltools_0.3.6  rvest_0.3.2     
[41] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3     stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4