Last updated: 2019-02-16

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
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    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

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File Version Author Date Message
Rmd b3d5773 Briana Mittleman 2019-02-16 add n sig genes
html c2c77b6 Briana Mittleman 2019-02-15 Build site.
Rmd b7a0e32 Briana Mittleman 2019-02-15 add window size qtl analysis 
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

In the QTL analysis where I accounted for mappping bias I looked for QTLs within 50kb around the peak using –window 2.5e4. At 10%FDR I found 291 QTLs in the total fraction and 615 in the nuclear fraction. In this analysis I will test different window sizes.

40 kb –window 2.0e4 30 kb –window 1.5e4 20 kb –window 1.0e4 10 kb –window 5.0e3

APAqtl_perm_GeneLocAnno_noMP_5percUsage_40kb.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_GeneLocAnno_noMP_5percUsage_40kb
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_GeneLocAnno_noMP_5percUsage_40kb.out
#SBATCH --error=APAqtl_perm_GeneLocAnno_noMP_5percUsage_40kb.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static  --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear_fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 2.0e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static   --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 2.0e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done

APAqtl_perm_GeneLocAnno_noMP_5percUsage_30kb.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_GeneLocAnno_noMP_5percUsage_30kb
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_GeneLocAnno_noMP_5percUsage_30kb.out
#SBATCH --error=APAqtl_perm_GeneLocAnno_noMP_5percUsage_30kb.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static  --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear_fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 1.5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static   --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 1.5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done

APAqtl_perm_GeneLocAnno_noMP_5percUsage_20kb.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_GeneLocAnno_noMP_5percUsage_20kb
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_GeneLocAnno_noMP_5percUsage_20kb.out
#SBATCH --error=APAqtl_perm_GeneLocAnno_noMP_5percUsage_20kb.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static  --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear_fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 1.0e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static   --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 1.0e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done

APAqtl_perm_GeneLocAnno_noMP_5percUsage_10kb.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_GeneLocAnno_noMP_5percUsage_10kb
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_GeneLocAnno_noMP_5percUsage_10kb.out
#SBATCH --error=APAqtl_perm_GeneLocAnno_noMP_5percUsage_10kb.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static  --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear_fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5.0e3 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static   --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5.0e3 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/SAMPLE.txt
done

Concatinate all of the chromosomes together then run the script to get the BH corrected pvalues.

Make an R scrips that will write the BH files and make the plots. I Will make 1 bash script to run all of them

APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_40KB.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_GeneLocAnno_noMP_5percCov_40KB.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps\n Gene Loc Anno 40KB")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH_40KB.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_GeneLocAnno_noMP_5percCov_40KB.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps \n Gene Loc Anno 40KB")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_40KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH_40KB.txt", col.names = T, row.names = F, quote = F)

APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_30KB.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_GeneLocAnno_noMP_5percCov_30KB.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps\n Gene Loc Anno 30KB")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH_30KB.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_GeneLocAnno_noMP_5percCov_30KB.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps \n Gene Loc Anno 30KB")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_30KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH_30KB.txt", col.names = T, row.names = F, quote = F)

APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_20KB.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_GeneLocAnno_noMP_5percCov_20KB.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps\n Gene Loc Anno 20KB")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH_20KB.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_GeneLocAnno_noMP_5percCov_20KB.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps \n Gene Loc Anno 20KB")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_20KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH_20KB.txt", col.names = T, row.names = F, quote = F)

APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_10KB.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_GeneLocAnno_noMP_5percCov_10KB.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps\n Gene Loc Anno 10KB")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH_10KB.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_GeneLocAnno_noMP_5percCov_10KB.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps \n Gene Loc Anno 40KB")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs_10KB/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH_10KB.txt", col.names = T, row.names = F, quote = F)

Bash script to run these:
run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_diffWindows.sh

#!/bin/bash


#SBATCH --job-name=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_diffW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_diffW.out
#SBATCH --error=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_diffW.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_40KB.R 
Rscript APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_30KB.R 
Rscript APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_20KB.R 
Rscript APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs_10KB.R

After this i can pull all of the files in and write a script that will tell me how many QTLs:

howManyQTLs=function(window, fraction){
  file=paste("../data/perm_QTL_diffWindow/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.", fraction, ".fixed.pheno_5perc_permResBH_", window, ".txt", sep = "" )
  inFile=read.table(file, stringsAsFactors = F, header = T)
  sigQTL=inFile %>% filter(-log10(bh)>=1)
  return(nrow(sigQTL))
}

Run this for total fraction:

window_sizes=c(50, 40, 30, 20, 10)
QTLsbyWindow_T=c(291,howManyQTLs("40KB", "Total"), howManyQTLs("30KB", "Total"),howManyQTLs("20KB", "Total"),howManyQTLs("10KB", "Total") )

Run for nuclear

QTLsbyWindow_N=c(615,howManyQTLs("40KB", "Nuclear"), howManyQTLs("30KB", "Nuclear"),howManyQTLs("20KB", "Nuclear"),howManyQTLs("10KB", "Nuclear") )

DF

diffWindow=as.data.frame(cbind(WindowSize=window_sizes, Total=QTLsbyWindow_T, Nuclear=QTLsbyWindow_N))

diffWindow_melt=melt(diffWindow, id.vars = "WindowSize")                       
colnames(diffWindow_melt)=c("WindowSize", "Fraction", "nQTL")

Plot

ggplot(diffWindow_melt,aes(x=WindowSize, y=nQTL, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") +scale_fill_manual(values=c("darkviolet","deepskyblue3")) + labs(title="apaQTLs at FDR 10% by cis Window Size", x="Window size (kb)")

Version Author Date
c2c77b6 Briana Mittleman 2019-02-15

Look at this but with the number of genes:

howManyQTLGenes=function(window, fraction){
  file=paste("../data/perm_QTL_diffWindow/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.", fraction, ".fixed.pheno_5perc_permResBH_", window, ".txt", sep = "" )
  inFile=read.table(file, stringsAsFactors = F, header = T) %>% separate(pid,into=c("chr", "start", "end", "id"),sep=":") %>% separate(id, into=c("gene", 'strand', 'peak'), sep="_") 
  sigQTL=inFile %>% filter(-log10(bh)>=1)
  sigGenes= sigQTL %>% group_by(gene) %>% tally()
  return(nrow(sigGenes))
}
QTLGenesbyWindow_T=c(235,howManyQTLGenes("40KB", "Total"), howManyQTLGenes("30KB", "Total"),howManyQTLGenes("20KB", "Total"),howManyQTLGenes("10KB", "Total") )
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].
QTLGenesWindow_N=c(496,howManyQTLGenes("40KB", "Nuclear"), howManyQTLGenes("30KB", "Nuclear"),howManyQTLGenes("20KB", "Nuclear"),howManyQTLGenes("10KB", "Nuclear") )
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].
diffWindowG=as.data.frame(cbind(WindowSize=window_sizes, Total=QTLGenesbyWindow_T, Nuclear=QTLGenesWindow_N))

diffWindowG_melt=melt(diffWindowG, id.vars = "WindowSize")                       
colnames(diffWindowG_melt)=c("WindowSize", "Fraction", "QTLGenes")   
ggplot(diffWindowG_melt,aes(x=WindowSize, y=QTLGenes, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") +scale_fill_manual(values=c("darkviolet","deepskyblue3")) + labs(title="apaQTL Genes at FDR 10% by cis Window Size", x="Window size (kb)")

QTLGenesbyWindow_T
[1] 235 244 262 303 353
QTLGenesWindow_N
[1] 496 528 572 581 621


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  reshape2_1.4.3  forcats_0.3.0   stringr_1.4.0  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.0     git2r_0.24.0     bindr_0.1.1      tools_3.5.1     
 [9] digest_0.6.17    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2 
[17] rlang_0.2.2      cli_1.0.1        rstudioapi_0.9.0 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        fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.4 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.11   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 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0     
[49] crayon_1.3.4