Last updated: 2019-04-03

Checks: 6 0

Knit directory: threeprimeseq/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(12345)

The command set.seed(12345) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: 1fb3ca5

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store
    Ignored:    data/perm_QTL_trans_noMP_5percov/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  KalistoAbundance18486.txt
    Untracked:  analysis/4suDataIGV.Rmd
    Untracked:  analysis/AdaptTonyExampleplots.Rmd
    Untracked:  analysis/DirectionapaQTL.Rmd
    Untracked:  analysis/EvaleQTLs.Rmd
    Untracked:  analysis/YL_QTL_test.Rmd
    Untracked:  analysis/groSeqAnalysis.Rmd
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/peaksWithMisprimming.Rmd
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  analysis/verifyBAM.Rmd
    Untracked:  analysis/verifybam_dubs.Rmd
    Untracked:  code/PeaksToCoverPerReads.py
    Untracked:  code/strober_pc_pve_heatmap_func.R
    Untracked:  data/18486.genecov.txt
    Untracked:  data/APApeaksYL.total.inbrain.bed
    Untracked:  data/AllPeak_counts/
    Untracked:  data/ApaQTLs/
    Untracked:  data/ApaQTLs_otherPhen/
    Untracked:  data/CTCF/
    Untracked:  data/ChromHmmOverlap/
    Untracked:  data/DistTXN2Peak_genelocAnno/
    Untracked:  data/EmpiricalDists/
    Untracked:  data/ExampleQTLplot2/
    Untracked:  data/FeatureoverlapPeaks/
    Untracked:  data/GM12878.chromHMM.bed
    Untracked:  data/GM12878.chromHMM.txt
    Untracked:  data/GWAS_overlap/
    Untracked:  data/LianoglouLCL/
    Untracked:  data/LocusZoom/
    Untracked:  data/LocusZoom_Unexp/
    Untracked:  data/LocusZoom_proc/
    Untracked:  data/MatchedSnps/
    Untracked:  data/NucSpecQTL/
    Untracked:  data/NuclearApaQTLs.txt
    Untracked:  data/PeakCounts/
    Untracked:  data/PeakCounts_noMP_5perc/
    Untracked:  data/PeakCounts_noMP_genelocanno/
    Untracked:  data/PeakUsage/
    Untracked:  data/PeakUsage_noMP/
    Untracked:  data/PeakUsage_noMP_GeneLocAnno/
    Untracked:  data/PeaksUsed/
    Untracked:  data/PeaksUsed_noMP_5percCov/
    Untracked:  data/PolyA_DB/
    Untracked:  data/QTL_overlap/
    Untracked:  data/RNAdecay/
    Untracked:  data/RNAkalisto/
    Untracked:  data/RefSeq_annotations/
    Untracked:  data/Replicates_usage/
    Untracked:  data/Signal_Loc/
    Untracked:  data/TotalApaQTLs.txt
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/UnderstandPeaksQC/
    Untracked:  data/WASP_STAT/
    Untracked:  data/YL-SP-18486-T-combined-genecov.txt
    Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
    Untracked:  data/YL_QTL_test/
    Untracked:  data/apaExamp/
    Untracked:  data/apaExamp_proc/
    Untracked:  data/apaQTL_examp_noMP/
    Untracked:  data/bedgraph_peaks/
    Untracked:  data/bin200.5.T.nuccov.bed
    Untracked:  data/bin200.Anuccov.bed
    Untracked:  data/bin200.nuccov.bed
    Untracked:  data/clean_peaks/
    Untracked:  data/comb_map_stats.csv
    Untracked:  data/comb_map_stats.xlsx
    Untracked:  data/comb_map_stats_39ind.csv
    Untracked:  data/combined_reads_mapped_three_prime_seq.csv
    Untracked:  data/diff_iso_GeneLocAnno/
    Untracked:  data/diff_iso_proc/
    Untracked:  data/diff_iso_trans/
    Untracked:  data/eQTL_inAPA/
    Untracked:  data/eQTLs_Lietal/
    Untracked:  data/ensemble_to_genename.txt
    Untracked:  data/example_gene_peakQuant/
    Untracked:  data/explainProtVar/
    Untracked:  data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
    Untracked:  data/first50lines_closest.txt
    Untracked:  data/gencov.test.csv
    Untracked:  data/gencov.test.txt
    Untracked:  data/gencov_zero.test.csv
    Untracked:  data/gencov_zero.test.txt
    Untracked:  data/gene_cov/
    Untracked:  data/joined
    Untracked:  data/leafcutter/
    Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
    Untracked:  data/molPheno_noMP/
    Untracked:  data/mol_overlap/
    Untracked:  data/mol_pheno/
    Untracked:  data/nom_QTL/
    Untracked:  data/nom_QTL_opp/
    Untracked:  data/nom_QTL_trans/
    Untracked:  data/nuc6up/
    Untracked:  data/nuc_10up/
    Untracked:  data/other_qtls/
    Untracked:  data/pQTL_inAPA/
    Untracked:  data/pQTL_otherphen/
    Untracked:  data/pacbio_cov/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/peaks4DT/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov_3UTR/
    Untracked:  data/perm_QTL_diffWindow/
    Untracked:  data/perm_QTL_opp/
    Untracked:  data/perm_QTL_trans/
    Untracked:  data/perm_QTL_trans_filt/
    Untracked:  data/protAndAPAAndExplmRes.Rda
    Untracked:  data/protAndAPAlmRes.Rda
    Untracked:  data/protAndExpressionlmRes.Rda
    Untracked:  data/reads_mapped_three_prime_seq.csv
    Untracked:  data/smash.cov.results.bed
    Untracked:  data/smash.cov.results.csv
    Untracked:  data/smash.cov.results.txt
    Untracked:  data/smash_testregion/
    Untracked:  data/ssFC200.cov.bed
    Untracked:  data/temp.file1
    Untracked:  data/temp.file2
    Untracked:  data/temp.gencov.test.txt
    Untracked:  data/temp.gencov_zero.test.txt
    Untracked:  data/threePrimeSeqMetaData.csv
    Untracked:  data/threePrimeSeqMetaData55Ind.txt
    Untracked:  data/threePrimeSeqMetaData55Ind.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_redobatch4.xlsx
    Untracked:  data/~$threePrimeSeqMetaData55Ind_redobatch4.xlsx
    Untracked:  manuscript/
    Untracked:  output/APAqtlExamp/
    Untracked:  output/LZ/
    Untracked:  output/deeptools_plots/
    Untracked:  output/picard/
    Untracked:  output/plots/
    Untracked:  output/qual.fig2.pdf

Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/HistoneModandPAS.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/NuclearSpecQTL.Rmd
    Modified:   analysis/PeakToXper.Rmd
    Modified:   analysis/RNAdecayAndAPA.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/characterize_apaQTLs.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diffIsoAnalysisNewMapping.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/fixBWChromNames.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/initialPacBioQuant.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:   analysis/unexplainedeQTL_analysis.Rmd
    Modified:   code/Snakefile

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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 1fb3ca5 Briana Mittleman 2019-04-03 add boxplot to script
html 69a8526 Briana Mittleman 2019-04-02 Build site.
Rmd c4062e5 Briana Mittleman 2019-04-02 add general script
html 5244c0f Briana Mittleman 2019-03-26 Build site.
Rmd 01af963 Briana Mittleman 2019-03-26 add example heatmap code 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.1  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0  
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'tidyr' was built under R version 3.5.2
Warning: package 'purrr' was built under R version 3.5.2
Warning: package 'dplyr' was built under R version 3.5.2
Warning: package 'stringr' was built under R version 3.5.2
Warning: package 'forcats' 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
library(cowplot)
Warning: package 'cowplot' was built under R version 3.5.2

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

Start with EIF2a example:

3 150302009 150302010 peak114357:EIF2A 5.39078186842105e-07 +

Get the phenotype

less /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 | grep EIF2A_ > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/EIF2a_TotalPeaksPheno.txt

less chr3.dose.filt.vcf.gz | grep 3:150302010 > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/EIF2a_TotalPeaksGenotype.txt

less chr3.dose.filt.vcf.gz | head -n14 | tail -n1 > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/genotypeHeader.txt
phenohead=read.table("../data/ExampleQTLplot2/Phenotypeheader.txt", header = T,stringsAsFactors = F)
phenoEIF=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksPheno.txt", col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(phenoEIF, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/") %>%   separate(chrom, into=c("chrom", "start", "end", "peakID"),sep=":") %>% mutate(PeakLoc=paste(start, end, sep=":"))


meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)

I want to join the genotype.

genoHead=read.table("../data/ExampleQTLplot2/genotypeHeader.txt", header = T,stringsAsFactors = F)
genoEIF=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksGenotype.txt", col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 

lettersGeno=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksGenotype.txt", col.names =colnames(genoHead),stringsAsFactors = F, colClasses = c("character") ) %>% select(REF, ALT)



refAllele=as.character(lettersGeno$REF)
altAllele=as.character(lettersGeno$ALT)

genoMelt=melt(genoEIF, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)

Join these:

PhenandGene= meltpheno %>% inner_join(genoMelt, by="Individual")  %>% group_by(PeakLoc, genotype) %>% summarise(SumNum=sum(num), SumDenom=sum(denom)) %>% mutate(PAU=SumNum/SumDenom)

Check sums

Groupsumscheck = PhenandGene %>% group_by(genotype) %>% summarise(SUM=sum(PAU))
my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))



eif2aplot=ggplot(PhenandGene, aes(PeakLoc, genotype)) + geom_tile(aes(fill = PAU))+ scale_fill_gradientn(colors =my_palette(100)) + labs(title="EIF2A", y="Genotype",x="PAS")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

eif2aplot

Version Author Date
69a8526 Briana Mittleman 2019-04-02
5244c0f Briana Mittleman 2019-03-26 
ggsave(eif2aplot, file="../output/plots/testEIF2A.png",height = 10, width = 10)

Make plot reproducible on midway

I want the script to take a fraction, gene, chr, a snp (chr:loc)

steps: * get phenotypes from /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 and /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 * get genotype from /project2/gilad/briana/YRI_geno_hg19/chrX.dose.filt.vcf.gz *rscript for making plot

I will write the rscript first:

makeQTLheatmap.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-G", "--geno"), action="store", default=NA, type='character',
              help="input genotype"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


phenohead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/Phenotypeheader.txt", header = T,stringsAsFactors = F)
pheno=read.table(opt$pheno, col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(pheno, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/")  %>%   separate(chrom, into=c("chrom", "start", "end", "peakID"),sep=":") %>% mutate(PeakLoc=paste(start, end, sep=":"))

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)


genoHead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/genotypeHeader.txt", header = T,stringsAsFactors = F)
geno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 


lettersGeno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F,colClasses = c("character")) %>% select(REF, ALT)

refAllele=lettersGeno$REF
altAllele=lettersGeno$ALT


genoMelt=melt(geno, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)

PhenandGene= meltpheno %>% inner_join(genoMelt, by="Individual")  %>% group_by(PeakLoc, genotype) %>% summarise(SumNum=sum(num), SumDenom=sum(denom)) %>% mutate(PAU=SumNum/SumDenom)



my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))



heatplot=ggplot(PhenandGene, aes(PeakLoc, genotype)) + geom_tile(aes(fill = PAU))+ scale_fill_gradientn(colors =my_palette(100)) + labs(title=opt$gene, y="Genotype" , x= "PAS") + theme(axis.text.x = element_text(angle = 90, hjust = 1))



ggsave(plot=heatplot, filename=opt$output, height=10, width=10)

qtlHeatmap.sh

#!/bin/bash

#SBATCH --job-name=qtlHeatmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=qtlHeatmap.out
#SBATCH --error=qtlHeatmap.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Fraction=$1
gene=$2
chrom=$3
snp=$4



less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.${Fraction}.fixed.pheno_5perc.fc.gz | grep ${gene}_ > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt


less /project2/gilad/briana/YRI_geno_hg19/chr${chrom}.dose.filt.vcf.gz | grep ${snp} > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt


Rscript makeQTLheatmap.R -P /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt -G  /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}_${SNP}.png
totalQTLs=read.table("../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", header=F)

14:65401627:65401711:CHURC1_-_peak48994

14:65389250

sbatch qtlHeatmap.sh "Total" "CHURC1" "14" "14:65389250"

12:57489617:57489715:STAT6_+_peak36983 12:57489648

sbatch qtlHeatmap.sh "Total" "STAT6" "12" "12:57489648"

Try a nuclear one:

nucQTLs=read.table("../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", header=F)

19:4688114:4688228:DPP9_+_peak77244

19:4680128

sbatch qtlHeatmap.sh "Nuclear" "DPP9" "19" "19:4680128"

4:83355978:83356052:ENOPH1_-_peak121076 4:83352186

sbatch qtlHeatmap.sh "Nuclear" "ENOPH1" "4" "4:83352186"

Create box plots for the figure:

pheno_qtlpeak=meltpheno %>% filter(grepl("peak114357", peakID)) %>% mutate(PAU=num/denom) %>% select(Individual, PeakLoc,PAU)
PhenandGene_qtl= pheno_qtlpeak %>% inner_join(genoMelt, by="Individual")  

eif2aqtlplot=ggplot(PhenandGene_qtl, aes(x=genotype, y=PAU, fill=genotype)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd")

ggsave(eif2aqtlplot, file="../output/APAqtlExamp/eif2aboxplot.png", height = 10, width=10)

make a script for this:

Make an R script that will be added to the make the heatmap too

apaqtlboxplot.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-G", "--geno"), action="store", default=NA, type='character',
              help="input genotype"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-p", "--peakID"), action="store", default=NA, type='character',
              help="peakID"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


phenohead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/Phenotypeheader.txt", header = T,stringsAsFactors = F)
pheno=read.table(opt$pheno, col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(pheno, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/")  %>%   separate(chrom, into=c("chrom", "start", "end", "peakID"),sep=":") %>% mutate(PeakLoc=paste(start, end, sep=":"))

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)


genoHead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/genotypeHeader.txt", header = T,stringsAsFactors = F)
geno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 


lettersGeno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F,colClasses = c("character")) %>% select(REF, ALT)

refAllele=lettersGeno$REF
altAllele=lettersGeno$ALT


genoMelt=melt(geno, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)


pheno_qtlpeak=meltpheno %>% filter(grepl(opt$peakID, peakID)) %>% mutate(PAU=num/denom) %>% select(Individual, PeakLoc,PAU)
PhenandGene_qtl= pheno_qtlpeak %>% inner_join(genoMelt, by="Individual")  

qtlplot=ggplot(PhenandGene_qtl, aes(x=genotype, y=PAU, fill=genotype)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd")

ggsave(plot=qtlplot, filename=opt$output, height=10, width=10)

qtlHeatmapandBoxplot.sh

#!/bin/bash

#SBATCH --job-name=qtlHeatmapandBoxplot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=qtlHeatmapandBoxplot.out
#SBATCH --error=qtlHeatmapandBoxplot.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Fraction=$1
gene=$2
chrom=$3
snp=$4
peakID=$5


less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.${Fraction}.fixed.pheno_5perc.fc.gz | grep ${gene}_ > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt


less /project2/gilad/briana/YRI_geno_hg19/chr${chrom}.dose.filt.vcf.gz | grep ${snp} > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt


Rscript makeQTLheatmap.R -P /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt -G  /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}${SNP}${peakID}_heatmap.png


Rscript apaqtlboxplot.R -P /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt -G  /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt -g ${gene} -p ${peakID}  -o /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}${SNP}${peakID}_boxplot.png

14:65401627:65401711:CHURC1_-_peak48994

14:65389250

sbatch qtlHeatmapandBoxplot.sh "Total" "CHURC1" "14" "14:65389250" "peak48994"

12:57489617:57489715:STAT6_+_peak36983 12:57489648

sbatch qtlHeatmapandBoxplot.sh "Total" "STAT6" "12" "12:57489648" "peak36983"

Try a nuclear one:

19:4688114:4688228:DPP9_+_peak77244

19:4680128

sbatch qtlHeatmapandBoxplot.sh "Nuclear" "DPP9" "19" "19:4680128" "peak77244"

4:83355978:83356052:ENOPH1_-_peak121076 4:83352186

sbatch qtlHeatmapandBoxplot.sh "Nuclear" "ENOPH1" "4" "4:83352186" "peak121076"


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] cowplot_0.9.4   reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.0.1   purrr_0.3.1     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.0.1    ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   xfun_0.5           haven_2.1.0       
 [4] lattice_0.20-38    colorspace_1.4-0   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.3.1       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.4       readxl_1.3.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] workflowr_1.2.0    cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.13      labeling_0.3       knitr_1.21        
[25] broom_0.5.1        Rcpp_1.0.0         scales_1.0.0      
[28] backports_1.1.3    jsonlite_1.6       fs_1.2.6          
[31] hms_0.4.2          digest_0.6.18      stringi_1.3.1     
[34] grid_3.5.1         rprojroot_1.3-2    cli_1.0.1         
[37] tools_3.5.1        magrittr_1.5       lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.11     httr_1.4.0         rstudioapi_0.9.0  
[49] R6_2.4.0           nlme_3.1-137       git2r_0.24.0      
[52] compiler_3.5.1