Characterize apaQTLs

Chromatin regions for QTLs

This will start with the sig QTL bed files:

• /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

• /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed

Creare a python script using pybedtools:

ChromHMM.QTLs.py

import pybedtools 

sigNuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed') 

sigTot=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed')

hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")

#map hmm to snps  
Tot_overlapHMM=sigTot.map(hmm, c=4)

Nuc_overlapHMM=sigNuc.map(hmm,c=4)

#save results  

Tot_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Tot_overlapHMM.bed")

Nuc_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuc_overlapHMM.bed")

Run in three-prime-env

chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)
TotalOverlapHMM=read.table("../data/ApaQTLs/Tot_overlapHMM.bed", col.names=c("chrom", "start", "end", "pid", "significance", "strand", "number"))
TotalOverlapHMM_names=TotalOverlapHMM %>% left_join(chromHmm, by="number")

NuclearOverlapHMM=read.table("../data/ApaQTLs/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "pid", "significance", "strand", "number"))
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")


ggplot(TotalOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Total APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Past versions of unnamed-chunk-3-1.png

ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Past versions of unnamed-chunk-3-2.png

Group them to put on plot together

NuclearOverlapHMM_names_byname= NuclearOverlapHMM_names %>% group_by(name) %>% summarise(Nuclear=n())
TotalOverlapHMM_names_byname= TotalOverlapHMM_names %>% group_by(name) %>% summarise(Total=n())


bothFracHMM= TotalOverlapHMM_names_byname %>% full_join(NuclearOverlapHMM_names_byname, by="name")
bothFracHMM$Nuclear= bothFracHMM$Nuclear %>% replace_na(0)

melt and plot

bothFracHMM_melt= melt(bothFracHMM,id.vars="name")
colnames(bothFracHMM_melt)=c("Region", "Fraction", "NQTLs")

apaQTLsChromregion=ggplot(bothFracHMM_melt, aes(x=Region, y=NQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + labs(title="apaQTLs by chromatin region")
apaQTLsChromregion

Past versions of unnamed-chunk-5-1.png

ggsave(apaQTLsChromregion, file="../output/plots/apaQTLsbyChromHMM.png", width = 7, height = 5)

bothFracHMM_prop= bothFracHMM %>% mutate(Total_prop=Total/291) %>% mutate(Nuclear_prop=Nuclear/615) %>% select(-Total,-Nuclear) 

bothFracHMM_prop_melt= melt(bothFracHMM_prop,id.vars="name")
colnames(bothFracHMM_prop_melt)=c("Region", "Fraction", "PropQTLs")


propapaQTLsChromregion=ggplot(bothFracHMM_prop_melt, aes(x=Region, y=PropQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + labs(y="Proportion of apaQTLs", title="Proportion of apaQTLs by chromatin region")
propapaQTLsChromregion

Past versions of unnamed-chunk-6-1.png

ggsave(propapaQTLsChromregion, file="../output/plots/proportionapaQTLsbyChromHMM.png", width = 7, height = 5)

I will need to get matched snps to look for enrichment in these values.

Overlap apaQTLs between fractions.

Goal: Find the nominal pvalue for the significant snp peak pair in oposite fraction. I can make a dictionary with the total and nuclear QTLs then run through the nominal files to get the pvalues:

Start with apa QTLs:

• /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

• /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed

NomResFromOppFrac.py

nucQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed"  
totQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed"  

nucNom="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"
totNom="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"

outnuc="/project2/gilad/briana/threeprimeseq/data/QTL_Overlap/TotQTLs_inNucFractionRes.txt"
outtot="/project2/gilad/briana/threeprimeseq/data/QTL_Overlap/NucQTLs_inTotFractionRes.txt"


def oppFract(inRes, inQTL, out):
    fout=open(out, "w")
    qtl_dic={}
    #SNP is key, peak is value
    for ln in open(inQTL,"r"):
        snp=ln.split()[2]
        chrom=ln.split()[0]
        peak=ln.split()[3].split(":")[0]
        qtl=str(chrom) + ":" + str(snp)
        if qtl not in qtl_dic.keys():
            qtl_dic[qtl]=[peak]
        else: 
            qtl_dic[qtl].append(peak)
    #print(qtl_dic)
    for ln in open(inRes, "r"):
        pval=ln.split()[3]
        snp=ln.split()[1]
        peak=ln.split()[0].split(":")[3].split("_")[-1]
        if snp in qtl_dic.keys():
            if peak in qtl_dic[snp]:
                fout.write("%s\t%s\t%s\n"%(snp, peak, pval))   
    fout.close()
    

oppFract(nucNom, totQTLs,outnuc)  
oppFract(totNom, nucQTLs, outtot)  
                

Run in bash: run_NomResFromOppFrac.sh

#!/bin/bash

#SBATCH --job-name=NomResFromOppFrac
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=NomResFromOppFrac.out
#SBATCH --error=NomResFromOppFrac.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env


python NomResFromOppFrac.py

names=c("SNP", "peak", "pval")
NucQTLinTot=read.table("../data/QTL_overlap/NucQTLs_inTotFractionRes.txt", stringsAsFactors = F, col.names = names)
TotQTLinNuc=read.table("../data/QTL_overlap/TotQTLs_inNucFractionRes.txt", stringsAsFactors = F, col.names = names)

Get pi values:

qvalTot= pi0est(NucQTLinTot$pval, pi0.method = "bootstrap")
1-qvalTot$pi0

[1] 0.8424242

qvalNuc= pi0est(TotQTLinNuc$pval, pi0.method = "bootstrap")
1-qvalNuc$pi0

[1] 0.9197861

plots:

par(mfrow=c(1,2))
hist(NucQTLinTot$pval,xlab="Total apaQTL pvalue", main="Nuclear apaQTLs \nin Total Fraction")
text(.6,200, paste("pi_1=", round((1-qvalTot$pi0), digit=3), sep=" "))
hist(TotQTLinNuc$pval,xlab="Nuclear apaQTL pvalue", main="Total apaQTLs \nin Nuclear Fraction")
text(.6,125, paste("pi_1=", round((1-qvalNuc$pi0), digit=3), sep=" "))

Past versions of unnamed-chunk-12-1.png

png("../output/plots/apaFractionOverlapPi1.png", width=1000, height = 500)
par(mfrow=c(1,2))
hist(NucQTLinTot$pval,xlab="Total apaQTL pvalue", main="Nuclear apaQTLs \nin Total Fraction")
text(.6,200, paste("pi_1=", round((1-qvalTot$pi0), digit=3), sep=" "))
hist(TotQTLinNuc$pval,xlab="Nuclear apaQTL pvalue", main="Total apaQTLs \nin Nuclear Fraction")
text(.6,125, paste("pi_1=", round((1-qvalNuc$pi0), digit=3), sep=" "))
dev.off()

quartz_off_screen 
                2 

This provides evidence for high degree of QTL sharing with increased sharing total to nuclear. This demonstrates to me that there are nuclear QTLs that do not persist in the total fraction. I will want to learn more about these.

Overlap with GWAS catelog

I did this analysis with the QTLs in the preprocessed 39 individual analysis. I will follow a similar pipeline here. I will find all of the snps in LD with the QTLs then test for these in the GWAS catelog. The pipeline I used to get the LD for all of the snp is shown here. The plink files are in /project2/gilad/briana/threeprimeseq/data/GWAS_overlap/. There are both map and ped files.

I can now adapt the subset_plink4QTLs.py file to take the current QTLs list. The file just has the QTLs with the chromosome and position. I can make this and put it in:

/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed

The 50mb QTLs are in /project2/gilad/briana/threeprimeseq/data/ApaQTLs.

• NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt
  
• TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt

The QTL snps are in the 6th column.

cut -f6 -d" " /project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt | uniq > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb.txt
cut -f6 -d" "  /project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt | uniq > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb.txt

I can convert these the the way they are in GEU snp files tony made (snp_num_pos)

QTLs2GeuSnps_proc.py

tot_in=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb.txt", "r")  
nuc_in=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb.txt", "r")

tot_out=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb_GEU.txt", "w") 
nuc_out=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb_GEU.txt", "w") 


def fix_file(fin, fout):
  for ln in fin:
    chrom, pos = ln.split(":")
    fout.write("snp_%s_%s"%(chrom,pos))
  fout.close()
  

fix_file(tot_in, tot_out)
fix_file(nuc_in, nuc_out)

subset_plink4QTLs_proc.py

def main(genFile, qtlFile, outFile):
  #convert snp file to a list: 
  def file_to_list(file):
    snp_list=[]
    for ln in file:
      snp=ln.strip()
      snp_list.append(snp)
    return(snp_list)

  gen=open(genFile,"r")
  fout=open(outFile, "w")
  qtls=open(qtlFile, "r")
  qtl_list=file_to_list(qtls)
  for ln in gen:
      snp=ln.split()[2]
      if snp in qtl_list:
          fout.write(ln)
  fout.close()
    

if __name__ == "__main__":
    import sys
    chrom=sys.argv[1]
    fraction=sys.argv[2]
    genFile = "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap/geu_plinkYRI_LDchr%s.ld"%(chrom)
    outFile= "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/%sApaQTL_LD/chr%s.%sQTL.LD.geno.ld"%(fraction,chrom,fraction)
    qtlFile= "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sQTLs_uniq_50mb_GEU.txt"%(fraction)
    main(genFile, qtlFile, outFile) 

run_subset_plink4QTLs_proc.sh

#!/bin/bash

#SBATCH --job-name= run_subset_plink4QTLs_proc
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=run_subset_plink4QTLs_proc.out
#SBATCH --error=run_subset_plink4QTLs_proc.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env


for i  in {1..22};
do
python subset_plink4QTLs_proc.py ${i} "Total"
done

for i  in {1..22};
do
python subset_plink4QTLs_proc.py ${i} "Nuclear"
done

This added 2446 total snps and 6258 nuclear snps.

Cat and remove indels:

cat chr* > allChr.TotalQTL.LD.gene.ld
grep -v indel allChr.TotalQTL.LD.gene.ld > allChr.TotalQTL.LD.gene.ld_noIndel

cat chr* > allChr.NuclearQTL.LD.gene.ld
grep -v indel allChr.NuclearQTL.LD.gene.ld > allChr.NuclearQTL.LD.gene.ld_noIndel

Make these bed files:

makeAlloverlapbed_proc.py

#load files:  

QTL_total=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb_GEU.txt", "r")
QTL_nuclear=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb_GEU.txt", "r")
LD_total=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/TotalApaQTL_LD/allChr.TotalQTL.LD.gene.ld_noIndel", "r")
LD_nuclear=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/NuclearApaQTL_LD/allChr.NuclearQTL.LD.gene.ld_noIndel", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.bed", "w")

#function for qtl to bed format
def qtl2bed(fqtl, fraction, fout=outFile):
    for ln in fqtl:
        snp, chrom, pos = ln.split("_")
        start=int(pos)-1
        end= int(pos)
        fout.write("%s\t%d\t%d\tQTL_%s\n"%(chrom, start, end,fraction))

#function for ld to bed format 
def ld2bed(fLD, fraction, fout=outFile):
    for ln in fLD:
        snpID=ln.split()[5]
        snp, chrom, pos= snpID.split("_")
        start=int(pos)-1
        end=int(pos)
        fout.write("%s\t%d\t%d\tLD_%s\n"%(chrom, start, end,fraction))


#I will run each of these for both fractions to get all of the snps in the out file. 


qtl2bed(QTL_nuclear, "Nuclear")
qtl2bed(QTL_total, "Total")
ld2bed(LD_nuclear, "Nuclear")
ld2bed(LD_total, "Total")


outFile.close()

Sort this:

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.bed > /project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.sort.bed

Overlap with GWAS

I can use the overlapSNPsGWAS.py file I created in the previous rendition of this analysis but run it with these files.

run_overlapSNPsGWAS_proc.sh

#!/bin/bash

#SBATCH --job-name=run_overlapSNPsGWAS_proc
#SBATCH --account=pi-yangili1
#SBATCH --time=5:00:00
#SBATCH --output=run_overlapSNPsGWAS_proc.out
#SBATCH --error=run_overlapSNPsGWAS_proc.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

python overlapSNPsGWAS.py  "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.sort.bed" "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped.txt"

Total QTLs overlap: rs3117582 6:31620520

Total LD overlap:

• rs2282301 1:155868625
  
• rs3596 12:95696420
  
• rs2277862 20:34152782
  
• rs2517713 6:29918099

Nuclear QTL overlap:

rs7206971 17:45425115

Nucelar LD overlapL

• rs10889353 1:63118196
  
• rs2282301 1:155868625
• rs10859871 12:95711876
  
• rs10133111 14:103377321
• rs17382723 2:242053546
  
• rs2277862 20:34152782
  
• rs2298428 22:21982892
  
• rs13160562 5:96111371
  
• rs29784 5:172595308
  
• rs2517713 6:29918099
  
• rs3077 6:33033022

Are these eQTLs?

Pull these snps from the eQTLs nominal pvalues:

Process the AllSnps_GWASoverlapped.txt file to have the RS id, the snp, and where it was found.

formatGWASOverlap.py

inFile="/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped.txt"

outFile=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped_format.txt","w")

for i in open(inFile, "r"):
   chrom=i.split()[0]
   loc=i.split()[2]
   snp=chrom + ":" + loc
   found=i.split()[3]
   rs=i.split()[7].split(":")[0]
   outFile.write("%s\t%s\t%s\n"%(snp, rs, found))
outFile.close()

This is 14 uniq snps.

/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out

I need to go throguh this file and check for the snps. I can do this by making a dictionary of the uniq snps in the overlap then testing the lines ib the nominal file. I want to keep the gene, snp, and pvalue for these associations
getexpPvalOverlap.py

overlapSnps="/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped_format.txt"
outF=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/eQTL_pval_GWASOverlap.txt","w")  
nomRes="/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out"  

snps_dic={}
for ln in open(overlapSnps, "r"):
    snp=ln.split()[0]
    if snp not in snps_dic.keys():
        snps_dic[snp]=""  

for ln in open(nomRes,"r"):
    snp=ln.split()[1]
    if snp in snps_dic.keys():
        gene=ln.split()[0].split(".")[0]
        pval=ln.split()[3]
        outF.write("%s\t%s\t%s\n"%(snp,gene,pval))
outF.close()

I can pull this in and get the lowest pval for each one

eQTLpvalOverlapGWAS=read.table("../data/ApaQTLs/eQTL_pval_GWASOverlap.txt", stringsAsFactors = F, col.names = c("snp", "gene", "pval"))

eQTLpvalOverlapGWAS_min= eQTLpvalOverlapGWAS %>% group_by(snp) %>% summarise(pvalM=min(pval)) %>% mutate(noSig=ifelse(pvalM<.05, "yes", "no"))

FIlter by the non sig ones to look at as exmaples:

eQTLpvalOverlapGWAS_NotSig= eQTLpvalOverlapGWAS_min %>% filter(noSig=="no")

eQTLpvalOverlapGWAS_NotSig

# A tibble: 5 x 3
  snp          pvalM noSig
  <chr>        <dbl> <chr>
1 1:63118196  0.400  no   
2 12:95696420 0.0813 no   
3 12:95711876 0.0813 no   
4 2:242053546 0.0878 no   
5 5:172595308 0.0530 no   

1:63118196 in LD with nuclear QTL (find which it is LD with )

grep snp_1_63118196 chr1.NuclearQTL.LD.geno.ld
#snp_1_63018852  
#r2 .91

12:95696420 LD with total QTL

grep snp_12_95696420 TotalApaQTL_LD/chr12.TotalQTL.LD.geno.ld
#snp_12_95672642
#r2 =1  

12:95711876 LD_Nuclear

grep snp_12_95711876 NuclearApaQTL_LD/chr12.NuclearQTL.LD.geno.ld
#snp_12_95688540
#r2 =1  

2:242053546 LD_Nuclear

grep snp_2_242053546 NuclearApaQTL_LD/chr2.NuclearQTL.LD.geno.ld
#snp_2_242018994
#r2 = .94  

5:172595308 LD Nuclear

grep snp_5_172595308 NuclearApaQTL_LD/chr5.NuclearQTL.LD.geno.ld
#snp_5_172580291
#r2 = .938    

Exampls plots:

In this analysis file I made boxplots for example QTLs. Here I can look at these.

grep DOCK7 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000116641  

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"])
    #print(pheno_df)
    genotype=get_geno()
    geno_df=pd.DataFrame(data=genotype, columns=["Ind", "Allele1", "Allele2"])
    #print(geno_df)
    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_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.%s.fixed.pheno_5perc.fc.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_proc_examples/qtlSNP_PeakAPA%s.%s%s.txt"%(fraction, snp, peak)
    main(PhenFile, GenFile, outFile, snp, peak)

python createQTLsnpAPAPhenTable_proc.py 1 1:63018852  peak5329 Total
python createQTLsnpAPAPhenTable_proc.py 1 1:63018852  peak5329 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "1" "1:63018852" "ENSG00000116641"

plotQTL_func= function(SNP, peak, gene){
  apaN_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
  apaT_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPATotal.", SNP, peak, ".txt", sep = "" ), header=T)
  su30_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_4su_30_", SNP, gene, ".txt", sep=""), header = T)
  su60_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_4su_60_", SNP, gene, ".txt", sep=""), header=T)
  RNA_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_RNAseq_", SNP, gene, ".txt", sep=""),header=T)
  RNAg_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_RNAseqGeuvadis_", SNP, gene, ".txt", sep=""), header = T)
  ribo_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_ribo_", SNP, gene, ".txt", sep=""),header=T)
  prot_file=read.table(paste("../data/apaExamp_proc/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, RNAplot, protplot,nrow=2)
  return (full_plot)
}

DOC7_boxplots=plotQTL_func(SNP="1:63018852", peak="peak5329", gene="ENSG00000116641")
DOC7_boxplots

ggsave(DOC7_boxplots, file="../output/plots/DOC7_boxplots.png")

Saving 7 x 5 in image

plotAPAQTL_func= function(SNP, peak, gene){
  apaN_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
  apaT_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPATotal.", SNP, peak, ".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)

  
  full_plot= plot_grid(apaNplot,apaTplot,nrow=1)
  return (full_plot)
}

plotAPAQTL_func(SNP="1:63018852", peak="peak5329", gene="DOCK7")

grep DOCK7 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000116641


grep peak5329  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/NuclearAPA.peak5329.DOCK7.nomNuc.txt

grep peak5329  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt  > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/TotalAPA.peak5329.DOCK7.nomTotal.txt

grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/RNA.DOCK7.nomTotal.txt

grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/Prot.DOCK7.nomTotal.txt

 grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/Ribo.DOCK7.nomTotal.txt

APATotal_DOCK7_LZ=read.table("../data/LocusZoom_proc/TotalAPA.peak5329.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(APATotal_DOCK7_LZ,"../data/LocusZoom_proc/apaTotalDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)


APANuclear_DOCK7_LZ=read.table("../data/LocusZoom_proc/NuclearAPA.peak5329.DOCK7.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)
write.table(APANuclear_DOCK7_LZ,"../data/LocusZoom_proc/apaNuclearDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)


prot_DOCK7_LZ=read.table("../data/LocusZoom_proc/Prot.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)
write.table(prot_DOCK7_LZ,"../data/LocusZoom_proc/ProtDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)

#sed -e 's/^/Chr/'


RNA_DOCK7_LZ=read.table("../data/LocusZoom_proc/RNA.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(RNA_DOCK7_LZ,"../data/LocusZoom_proc/RNADOCK7_LZ.txt", col.names = T, row.names = F, quote = F)

#sed -e 's/^/Chr/'


ribo_DOCK7_LZ=read.table("../data/LocusZoom_proc/Ribo.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(ribo_DOCK7_LZ,"../data/LocusZoom_proc/RiboDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
#sed -e 's/^/Chr/'

grep VEZT /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000028203 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr12.dose.filt.vcf.gz

python createQTLsnpAPAPhenTable_proc.py 12 12:95672642 peak39092 Total
python createQTLsnpAPAPhenTable_proc.py 12 12:95672642   peak39092 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "12" "12:95672642" "ENSG00000028203"

plotQTL_func(SNP="12:95672642", peak="peak39092", gene="ENSG00000028203")

grep MTERF4 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000122085 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr2.dose.filt.vcf.gz

python createQTLsnpAPAPhenTable_proc.py 2 2:242018994 peak96962 Total
python createQTLsnpAPAPhenTable_proc.py 2 2:242018994   peak96962 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "2" "2:242018994" "ENSG00000122085"

plotQTL_func(SNP="2:242018994", peak="peak96962", gene="ENSG00000122085")

grep BNIP1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000113734 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr5.dose.filt.vcf.gz

python createQTLsnpAPAPhenTable_proc.py 5 5:172580291 peak135178 Total
python createQTLsnpAPAPhenTable_proc.py 5 5:172580291   peak135178 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "5" "5:172580291" "ENSG00000113734"

plotQTL_func(SNP="5:172580291", peak="peak135178", gene="ENSG00000113734")

Warning: Removed 1 rows containing non-finite values (stat_boxplot).

Warning: Removed 1 rows containing missing values (geom_point).

grep EIF2A /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000144895 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr3.dose.filt.vcf.gz

python createQTLsnpAPAPhenTable_proc.py 3 3:150302010 peak114357 Total
python createQTLsnpAPAPhenTable_proc.py 3 3:150302010 peak114357 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "3" "3:150302010" "ENSG00000144895"

EIF2a_plot=plotQTL_func(SNP="3:150302010", peak="peak114357", gene="ENSG00000144895")
EIF2a_plot

ggsave(EIF2a_plot, file="../output/plots/EIF2a_boxplots.png")

Saving 7 x 5 in image

grep EIF2A /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000144895


grep peak114357  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/NuclearAPA.peak114357.EIF2A.nomNuc.txt

grep peak114357 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt  > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/TotalAPA.peak114357.EIF2A.nomTotal.txt

APATotal_EIF2A_LZ=read.table("../data/LocusZoom_proc/TotalAPA.peak114357.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)

write.table(APATotal_EIF2A_LZ,"../data/LocusZoom_proc/apaTotalEIF2A_LZ.txt", col.names = T, row.names = F, quote = F)


APANuclear_EIF2A_LZ=read.table("../data/LocusZoom_proc/NuclearAPA.peak114357.EIF2A.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope"))  %>% select( SNP, P)
write.table(APANuclear_EIF2A_LZ,"../data/LocusZoom_proc/apaNuclearEIF2A_LZ.txt", col.names = T, row.names = F, quote = F)

grep CHURC1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000258289 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr14.dose.filt.vcf.gz

python createQTLsnpAPAPhenTable_proc.py 14 14:65389250 peak48989 Total
python createQTLsnpAPAPhenTable_proc.py 14 14:65389250   peak48989 Nuclear

sbatch run_createQTLsnpMolPhenTable.sh "14" "14:65389250" "ENSG00000258289"

plotQTL_func(SNP="14:65389250", peak="peak48989", gene="ENSG00000258289")

Matched SNP for enrichment

Use https://data.broadinstitute.org/mpg/snpsnap/match_snps.html to get matched snps. Give it the total and nuclear QTLs in chr:pos format

/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/ApaQTLs

cut -f6 -d" " NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt > NuclearapaQTLs_SNPonly.txt

cut -f6 -d" " TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt > TotalapaQTLs_SNPonly.txt

I am asking for 1 snp per input. I am asking for annotation and specifying west african population. I have not exclued teh HLA snps.

I will need to make a bed file from the matches to run the overlap with the HMM regions. This will be simplt bed with just chrom, pos-1, pos.

Move to Midway

/project2/gilad/briana/threeprimeseq/data/MatchedSnp

matched2bed.py

nucSnp="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps.txt"
totSnp="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps.txt"
nucBed="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps.bed"
totBed="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps.bed"  


def toBed(Fin,Fout):
    fout=open(Fout, "w")
    fin= open(Fin, "r")
    for i, ln in enumerate(fin):
        if i >0: 
            qtl, match = ln.split()
            chrom=match.split(":")[0]
            pos=match.split(":")[1]
            start=int(pos) -1
            end=int(pos)
            fout.write("%s\t%s\t%s\n"%(chrom, start, end))
    fout.close()


toBed(nucSnp,nucBed)
toBed(totSnp,totBed)  

Sort them:

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps.bed > /project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps_sort.bed

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps.bed > /project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps_sort.bed

SCript to get HMM

ChromHMM.MatchedSNPs.py

import pybedtools 

sigNuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps_sort.bed') 

sigTot=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps_sort.bed')

hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")

#map hmm to snps  
Tot_overlapHMM=sigTot.map(hmm, c=4)

Nuc_overlapHMM=sigNuc.map(hmm,c=4)

#save results  

Tot_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/MatchedSnp/TotMatched_overlapHMM.bed")

Nuc_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/MatchedSnp/NucMatched_overlapHMM.bed")

Move here:

remove the 1 snp in the nuclear file with a “.”

TotalMatchedOverlapHMM=read.table("../data/MatchedSnps/TotMatched_overlapHMM.bed", col.names=c("chrom", "start", "end",  "number"),stringsAsFactors = F)
TotalMatchedOverlapHMM_names=TotalMatchedOverlapHMM %>% left_join(chromHmm, by="number")

NuclearMatchedOverlapHMM=read.table("../data/MatchedSnps/NucMatched_overlapHMM.bed", col.names=c("chrom", "start", "end", "number"),stringsAsFactors = F)
NuclearMatchedOverlapHMM_names=NuclearMatchedOverlapHMM %>% left_join(chromHmm, by="number")

Group togetehr:

NuclearMatchedOverlapHMM_names_byname= NuclearMatchedOverlapHMM_names %>% group_by(name) %>% summarise(Nuclear=n())
TotalMatchedOverlapHMM_names_byname= TotalMatchedOverlapHMM_names %>% group_by(name) %>% summarise(Total=n())


bothFracMatchedHMM= TotalMatchedOverlapHMM_names_byname %>% full_join(NuclearMatchedOverlapHMM_names_byname, by="name")

melt and plot

bothFracMatchedHMM_melt= melt(bothFracMatchedHMM,id.vars="name")
colnames(bothFracMatchedHMM_melt)=c("Region", "Fraction", "NQTLs")

apaMatchedChromregion=ggplot(bothFracMatchedHMM_melt, aes(x=Region, y=NQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + labs(title="Matched SNPS by chromatin region")
apaMatchedChromregion

Get proportions:

bothFracMatchedHMM_prop= bothFracMatchedHMM %>% mutate(Total_prop=Total/254) %>% mutate(Nuclear_prop=Nuclear/527) %>% select(-Total,-Nuclear) 

bothFracMatchedHMM_prop_melt= melt(bothFracMatchedHMM_prop,id.vars="name")
colnames(bothFracMatchedHMM_prop_melt)=c("Region", "Fraction", "PropQTLs")


propapaQTLsMatchedChromregion=ggplot(bothFracMatchedHMM_prop_melt, aes(x=Region, y=PropQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + labs(y="Proportion of apaQTLs", title="Proportion of matched SNPs by chromatin region")
propapaQTLsMatchedChromregion

Look at the difference between matched and actual QTLs:

PropHMM_QTLandMatch= bothFracMatchedHMM_prop %>% inner_join(bothFracHMM_prop, by="name")
colnames(PropHMM_QTLandMatch)=c("name", "Total_Matched", "Nuclear_Matched", "Total_QTL", "Nuclear_QTL")

PropHMM_QTLandMatch_diff =PropHMM_QTLandMatch %>% mutate(TotalDiff=Total_QTL-Total_Matched, NuclearDiff=Nuclear_QTL-Nuclear_Matched) %>% select(-Total_Matched, -Total_QTL, -Nuclear_Matched, -Nuclear_QTL)
colnames(PropHMM_QTLandMatch_diff)= c("name","Total", "Nuclear" )


PropHMM_QTLandMatch_diff_melt=melt(PropHMM_QTLandMatch_diff, id.vars="name")
colnames(PropHMM_QTLandMatch_diff_melt)= c("Region", "Fraction", "Difference")

diffPropPlot=ggplot(PropHMM_QTLandMatch_diff_melt, aes(x=Region, y=Difference, by=Fraction, fill=Fraction))+ geom_bar(stat="identity", position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3"))+labs(y="Difference in proportion of SNPs", title="Enrichment of QTLs compared to \nmatched SNPs by chromatin region")
diffPropPlot

ggsave(diffPropPlot, filename ="../output/plots/DiffPropChromHMM.png")

Saving 7 x 5 in image

PropHMM_QTLandMatch_melt=melt(PropHMM_QTLandMatch, id.vars="name") %>% separate(variable,sep="_", into=c("Fraction", "Set"))

colnames(PropHMM_QTLandMatch_melt)=c("Region", "Fraction", "Set", "Proportion")

ggplot(PropHMM_QTLandMatch_melt, aes(x=Region, by=Set, y=Proportion, fill=Set)) + geom_bar(stat="identity", position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +facet_grid(~Fraction) + scale_fill_manual(values=c("grey", "blue"))

Session information

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  cowplot_0.9.3   reshape2_1.4.3  qvalue_2.12.0  
 [5] forcats_0.3.0   stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5    
 [9] readr_1.1.1     tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0  
[13] tidyverse_1.2.1 workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4   splines_3.5.1      haven_1.1.2       
 [4] lattice_0.20-35    colorspace_1.3-2   htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.2.2       
[10] pillar_1.3.0       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] bindr_0.1.1        plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.13      labeling_0.3       knitr_1.20        
[25] fansi_0.4.0        broom_0.5.0        Rcpp_0.12.19      
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.2.6           hms_0.4.2          digest_0.6.17     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.0.1          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.11     httr_1.3.1        
[49] rstudioapi_0.9.0   R6_2.3.0           nlme_3.1-137      
[52] git2r_0.24.0       compiler_3.5.1