Last updated: 2019-02-16
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Knit directory: threeprimeseq/analysis/
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Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/NewPeakPostMP.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
Modified: analysis/coloc_apaQTLs_protQTLs.Rmd
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/diff_iso_pipeline.Rmd
Modified: analysis/explainpQTLs.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/flash2mash.Rmd
Modified: analysis/mispriming_approach.Rmd
Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlapMolQTL.opposite.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/peakQCPPlots.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/pipeline_55Ind.Rmd
Modified: analysis/swarmPlots_QTLs.Rmd
Modified: analysis/test.max2.Rmd
Modified: analysis/test.smash.Rmd
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