Top APA QTL in other phenotypes

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library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

library(reshape2)
library(tidyverse)

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✔ readr   1.1.1     ✔ forcats 0.3.0

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library(VennDiagram)

Loading required package: grid

Loading required package: futile.logger

library(data.table)

Attaching package: 'data.table'

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library(cowplot)

Attaching package: 'cowplot'

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

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Permuted Results from APA:

nuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header = T)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T)  

I want to use a buzz swarm plot to plot peak usage for some of the top QTLs. I can use the examples I gave Tony.

Nuclear:
* peak305794, sid: 7:128635754

• peak: 164036, sid: 2:3502035

Total:

• Peak: peak228606, SID 3:150302010

• Peak: peak152751, SID 19:4236475

I need to pull out the genotypes for each snp and the corresponding phenotype. I want to make a python script that I can give a snp and a peak and it will make a table with the genotypes and phenotypes for the necessary gene snp pair.

Example Peak: peak228606, SID 3:150302010

geno3_m=fread("../data/apaExamp/geno3_150302010.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
geno3df= data.frame(geno3_m) %>% separate(geno3_m, into=c("geno", "dose", "extra"), sep=":") %>% dplyr::select(dose) %>% rownames_to_column(var="ind")
apaphen228606_m= fread("../data/apaExamp/Total.peak228606.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
apaphen228606_df=data.frame(apaphen228606_m) %>% rownames_to_column(var="ind")

toplotAPA=geno3df %>% inner_join(apaphen228606_df, by="ind")
toplotAPA$dose= as.factor(toplotAPA$dose)
colnames(toplotAPA)= c("ind", "Genotype", "APA")
EIF2A_APAex=ggplot(toplotAPA, aes(y=APA, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="APA phenotype", title="Total APA: Peak 228606, EIF2A") + scale_fill_brewer(palette="YlOrRd")
ggsave("../output/plots/EIF2a_APA.png", EIF2A_APAex)

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This is in the gene EIF2A, I need to find this in the eQTL data. The ensg id for this gene is ENSG00000144895.

RNAseqEIF2A_m=read.table("../data/apaExamp/RNAseq.phenoEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t 
RNAseqEIF2A_df= data.frame(RNAseqEIF2A_m) %>% rownames_to_column("ind")

plotRNA=geno3df %>% inner_join(RNAseqEIF2A_df, by="ind")
plotRNA$dose= as.factor(plotRNA$dose)
colnames(plotRNA)= c("ind", "Genotype", "Expression")

EIF2A_RNAex=ggplot(plotRNA, aes(y=Expression, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Expression", title="Gene Expression: EIF2A") + scale_fill_brewer(palette="YlGn")

ggsave("../output/plots/EIF2a_RNA.png", EIF2A_RNAex)

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Try this in protein:

ProtEIF2A_m=read.table("../data/apaExamp/ProtEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t 
ProtEIF2A_df= data.frame(ProtEIF2A_m) %>% rownames_to_column("ind")

plotProt=geno3df %>% inner_join(ProtEIF2A_df, by="ind")
plotProt$dose= as.factor(plotProt$dose)
colnames(plotProt)= c("ind", "Genotype", "Prot_level")

IF2A_Protex= ggplot(plotProt, aes(y=Prot_level, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Protein Level", title="Protein Level: EIF2A") +scale_fill_brewer(palette="PuBu")

ggsave("../output/plots/EIF2a_Prot.png", IF2A_Protex)

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multphenoEIF2a=plot_grid(EIF2A_APAex,IF2A_Protex,EIF2A_RNAex,nrow=1)
ggsave("../output/plots/EIF2a_multpheno.png", multphenoEIF2a, width=15, height=5)

Do this with 4su 60:

have to remove the #

su60_EIF2A_m=read.table("../data/apaExamp/Foursu60EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t 
su60_EIF2A_df= data.frame(su60_EIF2A_m) %>% rownames_to_column("ind")

plot4su60=geno3df %>% inner_join(su60_EIF2A_df, by="ind")
plot4su60$dose= as.factor(plot4su60$dose)
colnames(plot4su60)= c("ind", "Genotype", "su60")

EIF2A_4su60ex=ggplot(plot4su60, aes(y=su60, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="4su60", title="4su 60min Value: EIF2A") + scale_fill_brewer(palette="RdPu") +  theme_classic()

ggsave("../output/plots/EIF2a_4su60.png", EIF2A_4su60ex)

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Geuvadis RNA

rnaG_EIF2A_m=read.table("../data/apaExamp/RNA_GEU_EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t 
rnaG_EIF2A_df= data.frame(rnaG_EIF2A_m) %>% rownames_to_column("ind")

plotRNAg=geno3df %>% inner_join(rnaG_EIF2A_df, by="ind")
plotRNAg$dose= as.factor(plotRNAg$dose)
colnames(plotRNAg)= c("ind", "Genotype", "RNAg")

EIF2A_RNAgex=ggplot(plotRNAg, aes(y=RNAg, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="RNA expression", title="RNA Expression Geuvadis: EIF2A") + scale_fill_brewer(palette="RdPu")

ggsave("../output/plots/EIF2a_RNAg.png", EIF2A_RNAgex)

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Ribo:

ribo_EIF2A_m=read.table("../data/apaExamp/Ribo_EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t 
ribo_EIF2A_df= data.frame(ribo_EIF2A_m) %>% rownames_to_column("ind")

plotrib=geno3df %>% inner_join(ribo_EIF2A_df, by="ind")
plotrib$dose= as.factor(plotrib$dose)
colnames(plotrib)= c("ind", "Genotype", "Ribo")

EIF2A_riboex=ggplot(plotrib, aes(y=Ribo, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="RNA expression", title="Ribo Geuvadis: EIF2A") + scale_fill_brewer(palette="RdPu")

ggsave("../output/plots/EIF2a_Ribo.png", EIF2A_riboex)

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Create a script to make the relevent files

Python script that take a chromosome, snp, peak#, fraction

createQTLsnpAPAPhenTable.py

def main(PhenFile, GenFile, outFile, snp, peak):
    fout=open(outFile, "w")
    #Phen=open(PhenFile, "r")
    Gen=open(GenFile, "r")
    #get ind and pheno info
    def get_pheno():
      Phen=open(PhenFile, "r")
      for num, ln in enumerate(Phen):
          if num == 0:
              indiv= ln.split()[4:]
          else:
              id=ln.split()[3].split(":")[3]
              peakID=id.split("_")[2]
              if peakID == peak:
                  pheno_list=ln.split()[4:]
                  pheno_data=list(zip(indiv,pheno_list))
                  print(pheno_data)
                  return(pheno_data)
    def get_geno():
      for num, lnG in enumerate(Gen):
          if num == 13:
              Ind_geno=lnG.split()[9:]
          if num >= 14: 
              sid= lnG.split()[2]
              if sid == snp: 
                  gen_list=lnG.split()[9:]
                  allele1=[]
                  allele2=[]
                  for i in gen_list:
                      genotype=i.split(":")[0]
                      allele1.append(genotype.split("|")[0])
                      allele2.append(genotype.split("|")[1])
            #now i have my indiv., phen, allele 1, alle 2     
                  geno_data=list(zip(Ind_geno, allele1, allele2))
                  print(geno_data)
                  return(geno_data)

    phenotype=get_pheno()
    pheno_df=pd.DataFrame(data=phenotype,columns=["Ind", "Pheno"])
    genotype=get_geno()
    geno_df=pd.DataFrame(data=genotype, columns=["Ind", "Allele1", "Allele2"])
    full_df=pd.merge(geno_df, pheno_df, how="inner", on="Ind")
    full_df.to_csv(fout, sep="\t", encoding='utf-8', index=False)
    fout.close()
    

if __name__ == "__main__":
    import sys
    import pandas as pd
    chrom=sys.argv[1]
    snp = sys.argv[2]
    peak = sys.argv[3]
    fraction=sys.argv[4]
    
    PhenFile = "/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.%s.pheno_fixed.txt.gz.phen_chr%s"%(fraction, chrom)
    GenFile= "/project2/gilad/briana/YRI_geno_hg19/chr%s.dose.filt.vcf"%(chrom)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_PeakAPA%s.%s%s.txt"%(fraction, snp, peak)
    main(PhenFile, GenFile, outFile, snp, peak)
    

Use the results to plot the nuclear pheno:

EIF2a_APAnuc=read.table("../data/apaExamp/qtlSNP_PeakAPANuclear.3:150302010peak228606.txt", header=T, stringsAsFactors = F) %>% mutate(Geno=Allele1 + Allele2)

EIF2a_APAnuc$Geno= as.factor(as.character(EIF2a_APAnuc$Geno))


ggplot(EIF2a_APAnuc, aes(y=Pheno, x=Geno, by=Geno, fill=Geno)) + geom_boxplot() + geom_jitter() + labs(y="APA Nuc Usage", title="APA nuc: EIF2A") + scale_fill_brewer(palette="RdPu")

Expand here to see past versions of unnamed-chunk-12-1.png:

Version	Author	Date
e73be70	Briana Mittleman	2018-10-09

This does the total and nuclear fraction of APA. I will do this for a snp and gene and get all of the other phenotypes. This will be similar other than changing the names of the genes and seperating the name for all but protein.

createQTLsnpMolPhenTable.py

def main(PhenFile, GenFile, outFile, snp, gene, molPhen):
    #genenames=open("/project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt", "r" )
    #for ln in genenames:
     #   geneName=ln.split()[1]
      #  if geneName == gene:
       #gene_ensg=ln.split()[0]
    gene_ensg=gene  
    fout=open(outFile, "w")
    #Phen=open(PhenFile, "r")
    Gen=open(GenFile, "r")
    def getPheno(geneE=gene_ensg , mp=molPhen):
      pheno=open(PhenFile, "r")
      #get ind and pheno info
      mp_use=mp[1:-1]
      if mp_use=="prot":
        for num,ln in enumerate(pheno):
            if num == 0:
                indiv= ln.split()[4:]
            else:
                gene=ln.split()[3]
                if gene == str(geneE):
                    print("x")
                    pheno_list=ln.split()[4:]
                    pheno_data= list(zip(indiv, pheno_list))
                    return(pheno_data)
      else:
        for num,ln in enumerate(pheno):
            if num == 0:
                indiv= ln.split()[4:]
            else: 
                full_gene=ln.split()[3]
                gene= full_gene.split(".")[0]
                if gene == geneE:
                    print(gene)
                    pheno_list=ln.split()[4:]
                    pheno_data= list(zip(indiv, pheno_list))
                    return(pheno_data)
    def getGeno(geno, SNP):
      for num, lnG in enumerate(geno):
          if num == 13:
              Ind_geno=lnG.split()[9:]
          if num >= 14: 
              sid= lnG.split()[2]
              if sid == SNP: 
                  gen_list=lnG.split()[9:]
                  allele1=[]
                  allele2=[]
                  for i in gen_list:
                      genotype=i.split(":")[0]
                      allele1.append(genotype.split("|")[0])
                      allele2.append(genotype.split("|")[1])
            #now i have my indiv., phen, allele 1, alle 2     
                  geno_data=list(zip(Ind_geno, allele1, allele2))
                  return(geno_data)
    
                      
                  
    phenotype_data=getPheno()
    print(phenotype_data)
    pheno_df=pd.DataFrame(data=phenotype_data,columns=["Ind", "Pheno"])  
  
    genotype_data=getGeno(Gen, snp)
    print(genotype_data)
    geno_df=pd.DataFrame(data=genotype_data, columns=["Ind", "Allele1", "Allele2"])
    
    full_df=pd.merge(geno_df, pheno_df, how="inner", on="Ind")
    full_df.to_csv(fout, sep="\t", encoding='utf-8', index=False)
    fout.close()
    

if __name__ == "__main__":
    import sys
    import pandas as pd
    chrom=sys.argv[1]
    snp = sys.argv[2]
    gene = sys.argv[3]
    molPhen=sys.argv[4]
    
    PhenFile = "/project2/gilad/briana/threeprimeseq/data/molecular_phenos/fastqtl_qqnorm%sphase2.fixed.noChr.txt"%(molPhen)
    GenFile= "/project2/gilad/briana/YRI_geno_hg19/chr%s.dose.filt.vcf"%(chrom)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak%s%s%s.txt"%(molPhen, snp, gene)
    main(PhenFile, GenFile, outFile, snp, gene,molPhen)
    

test this:

python createQTLsnpMolPhenTable.py 3 3:150302010 EIF2A _RNAseq_

list for phenos:

• 4su_30

• 4su_60

• RNAseqGeuvadis

• RNAseq

• prot

• ribo

Create a bash script that will use a for loop to run the python script on a all of the phenotypes

run_createQTLsnpMolPhenTable.sh

#!/bin/bash

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

module load python 


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

for i in "_4su_30_" "_4su_60_" "_RNAseqGeuvadis_" "_RNAseq_" "_prot." "_ribo_"
do
python createQTLsnpMolPhenTable.py ${chrom} ${snp} ${gene} ${i}
done

Function to create plots:

I want to imput the files with the following structure:

/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/apaExamp/qtlSNP_Peakmolpheno.snp.peak/gene.txt

I will use these to make cowplot with ggplot boxplots for each phenotypes. To do this I will create a function that takes in a snp, peak, and gene and creates each phenotype plot. It will then return the cowplot plot grid.

plotQTL_func= function(SNP, peak, gene){
  apaN_file=read.table(paste("../data/apaExamp/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
  apaT_file=read.table(paste("../data/apaExamp/qtlSNP_PeakAPATotal.", SNP, peak, ".txt", sep = "" ), header=T)
  su30_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_4su_30_", SNP, gene, ".txt", sep=""), header = T)
  su60_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_4su_60_", SNP, gene, ".txt", sep=""), header=T)
  RNA_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_RNAseq_", SNP, gene, ".txt", sep=""),header=T)
  RNAg_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_RNAseqGeuvadis_", SNP, gene, ".txt", sep=""), header = T)
  ribo_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_ribo_", SNP, gene, ".txt", sep=""),header=T)
  prot_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_prot.", SNP, gene, ".txt", sep=""), header=T)
  
  ggplot_func= function(file, molPhen,GENE){
    file = file %>% mutate(genotype=Allele1 + Allele2)
    file$genotype= as.factor(as.character(file$genotype))
    plot=ggplot(file, aes(y=Pheno, x=genotype, by=genotype, fill=genotype)) + geom_boxplot(width=.25) + geom_jitter() + labs(y="Phenotpye",title=paste(molPhen, GENE, sep=": ")) + scale_fill_brewer(palette="Paired")
    return(plot)
  }
  
  apaNplot=ggplot_func(apaN_file, "Apa Nuclear", gene)
  apaTplot=ggplot_func(apaT_file, "Apa Total", gene)
  su30plot=ggplot_func(su30_file, "4su30",gene)
  su60plot=ggplot_func(su60_file, "4su60",gene)
  RNAplot=ggplot_func(RNA_file, "RNA Seq",gene)
  RNAgPlot=ggplot_func(RNAg_file, "RNA Seq Geuvadis",gene)
  riboPlot= ggplot_func(ribo_file, "Ribo Seq",gene)
  protplot=ggplot_func(prot_file, "Protein",gene)
  
  full_plot= plot_grid(apaNplot,apaTplot, su30plot, su60plot, RNAplot, RNAgPlot, riboPlot, protplot,nrow=2)
  return (full_plot)
}

Try this with the EIF2A QTL:

eif2a_allplots=plotQTL_func(SNP="3:150302010", peak="peak228606", gene="EIF2A")

ggsave("../output/plots/EIF2A_allplots.png", eif2a_allplots, height=5, width=12)

Try with another snp:

• peak164036, sid: 2:3502035

Step 1: Figure out what gene the peak is in.

grep peak164036 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt

This peak is in ADI1

Step2: Get the total and nuclear APA values by genotype with createQTLsnpAPAPhenTable.py

python createQTLsnpAPAPhenTable.py 2 2:3502035 peak164036 Total

python createQTLsnpAPAPhenTable.py 2 2:3502035 peak164036 Nuclear

Step 3: Get the ensg gene name:

grep ADI1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt

Step 4: Run this on the other phenotypes with : run_createQTLsnpMolPhenTable.sh

sbatch run_createQTLsnpMolPhenTable.sh "2" "2:3502035" "ENSG00000182551"

Step 4: copy files to computer:

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak*2:*  . 

Step 5: plot

plotQTL_func(SNP="2:3502035", peak="peak164036", gene="ENSG00000182551")

Expand here to see past versions of unnamed-chunk-23-1.png:

Version	Author	Date
8211a07	Briana Mittleman	2018-10-11

• peak305794, sid: 7:128635754

grep peak305794 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt
#gene=IRF5
grep IRF5 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000128604

python createQTLsnpAPAPhenTable.py 7 7:128635754  peak305794 Total
python createQTLsnpAPAPhenTable.py 7 7:128635754  peak305794 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "7" "7:128635754" "ENSG00000128604"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak_*7:* .
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_PeakAPA*.7* .

plotQTL_func(SNP="7:128635754", peak="peak305794", gene="ENSG00000128604")

Expand here to see past versions of unnamed-chunk-25-1.png:

Version	Author	Date
8211a07	Briana Mittleman	2018-10-11

• Peak: peak152751, SID 19:4236475

grep peak152751 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt
#gene=EBI3
grep EBI3 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000105246

python createQTLsnpAPAPhenTable.py 19 19:4236475  peak152751 Total
python createQTLsnpAPAPhenTable.py 19 19:4236475  peak152751 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "19" "19:4236475 " "ENSG00000105246"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*19:4236475* .

plotQTL_func(SNP="19:4236475", peak="peak152751", gene="ENSG00000105246")

Expand here to see past versions of unnamed-chunk-27-1.png:

Version	Author	Date
8211a07	Briana Mittleman	2018-10-11

• 4:84382181:84382182:MRPS18C_+_peak241853, snp4:84382264

#gene=MRPS18C
grep MRPS18C /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000163319

python createQTLsnpAPAPhenTable.py 4 4:84382264  peak241853 Total
python createQTLsnpAPAPhenTable.py 4 4:84382264  peak241853 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "4" "4:84382264 " "ENSG00000163319"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*4:84382264* .

We dont have protein information for this gene

plotQTL_func(SNP="4:84382264", peak="peak241853", gene="ENSG00000163319")

Expand here to see past versions of unnamed-chunk-29-1.png:

Version	Author	Date
8211a07	Briana Mittleman	2018-10-11

• 7:66703515:66703516:TYW1_+_peak298097 7:66595366

#gene=TYW1
grep TYW1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000198874

python createQTLsnpAPAPhenTable.py 7 7:66595366 peak298097 Total
python createQTLsnpAPAPhenTable.py 7 7:66595366  peak298097 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "7" "7:66595366" "ENSG00000198874"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*7:66595366* .

plotQTL_func(SNP="7:66595366", peak="peak298097", gene="ENSG00000198874")

• 8:2792874:2792875:CSMD1_-_peak310334 8:3037787

#gene=CSMD1
grep CSMD1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000183117

python createQTLsnpAPAPhenTable.py 8 8:3037787   peak310334 Total
python createQTLsnpAPAPhenTable.py 8 8:3037787    peak310334 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "8" "8:3037787" "ENSG00000183117"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*8:3037787* .

We do not have molecular phenotypes for this gene.

• 6:11183530:11183531:NEDD9_-_peak272002 6:11212754

#gene=NEDD9
grep NEDD9 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000111859

python createQTLsnpAPAPhenTable.py 6 6:11212754  peak272002 Total
python createQTLsnpAPAPhenTable.py 6 6:11212754  peak272002 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "6" "6:11212754" "ENSG00000111859"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*6:11212754* .

plotQTL_func(SNP="6:11212754", peak="peak272002", gene="ENSG00000111859")

Warning: Removed 9 rows containing non-finite values (stat_boxplot).

Warning: Removed 9 rows containing missing values (geom_point).

• 12:51453213:51453214:LETMD1_+_peak71110 12:51405335

#gene=LETMD1
grep LETMD1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000050426

python createQTLsnpAPAPhenTable.py 12 12:51405335  peak71110 Total
python createQTLsnpAPAPhenTable.py 12 12:51405335  peak71110 Nuclear


sbatch run_createQTLsnpMolPhenTable.sh "12" "12:51405335" "ENSG00000050426"

scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*12:51405335* .

plotQTL_func(SNP="12:51405335", peak="peak71110", gene="ENSG00000050426")

Warning: Removed 7 rows containing non-finite values (stat_boxplot).

Warning: Removed 7 rows containing missing values (geom_point).

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2      cowplot_0.9.3       data.table_1.11.8  
 [4] VennDiagram_1.6.20  futile.logger_1.4.3 forcats_0.3.0      
 [7] stringr_1.3.1       dplyr_0.7.6         purrr_0.2.5        
[10] readr_1.1.1         tidyr_0.8.1         tibble_1.4.2       
[13] ggplot2_3.0.0       tidyverse_1.2.1     reshape2_1.4.3     
[16] workflowr_1.1.1    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4     haven_1.1.2          lattice_0.20-35     
 [4] colorspace_1.3-2     htmltools_0.3.6      yaml_2.2.0          
 [7] rlang_0.2.2          R.oo_1.22.0          pillar_1.3.0        
[10] glue_1.3.0           withr_2.1.2          R.utils_2.7.0       
[13] RColorBrewer_1.1-2   lambda.r_1.2.3       modelr_0.1.2        
[16] readxl_1.1.0         bindr_0.1.1          plyr_1.8.4          
[19] munsell_0.5.0        gtable_0.2.0         cellranger_1.1.0    
[22] rvest_0.3.2          R.methodsS3_1.7.1    evaluate_0.11       
[25] labeling_0.3         knitr_1.20           broom_0.5.0         
[28] Rcpp_0.12.19         formatR_1.5          backports_1.1.2     
[31] scales_1.0.0         jsonlite_1.5         hms_0.4.2           
[34] digest_0.6.17        stringi_1.2.4        rprojroot_1.3-2     
[37] cli_1.0.1            tools_3.5.1          magrittr_1.5        
[40] lazyeval_0.2.1       futile.options_1.0.1 crayon_1.3.4        
[43] whisker_0.3-2        pkgconfig_2.0.2      xml2_1.2.0          
[46] lubridate_1.7.4      assertthat_0.2.0     rmarkdown_1.10      
[49] httr_1.3.1           rstudioapi_0.8       R6_2.3.0            
[52] nlme_3.1-137         git2r_0.23.0         compiler_3.5.1