Characterize Nuclear ApaQTLs

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Library

library(workflowr)

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

library(reshape2)
library(tidyverse)

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✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ 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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    between, first, last

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library(cowplot)

Attaching package: 'cowplot'

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    ggsave

Permuted Results from APA:

I will add a column to this dataframe that will tell me if the association is significant at 10% FDR. This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.

NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header=T)  %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))

NuclearAPA$sig=as.factor(NuclearAPA$sig)


print(names(NuclearAPA))

 [1] "chr"    "start"  "end"    "gene"   "strand" "peak"   "nvar"  
 [8] "shape1" "shape2" "dummy"  "sid"    "dist"   "npval"  "slope" 
[15] "ppval"  "bpval"  "bh"     "sig"   

Distance Metrics

ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000)) + scale_fill_brewer(palette="Paired")

peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))

NuclearAPA_peakdist= NuclearAPA %>%  inner_join(peaks, by="peak") %>%  separate(sid, into=c("snpCHR", "snpLOC"), by=":")
NuclearAPA_peakdist$snpLOC= as.numeric(NuclearAPA_peakdist$snpLOC)

NuclearAPA_peakdist= NuclearAPA_peakdist %>%  mutate(DisttoPeak= snpLOC-PeakCenter)

ggplot(NuclearAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

NuclearAPA_peakdist_sig=NuclearAPA_peakdist %>% filter(sig==1)
NuclearAPA_peakdist_notsig=NuclearAPA_peakdist %>% filter(sig==0)


summary(NuclearAPA_peakdist_sig$DisttoPeak)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1003786   -17579      -91    -8818     6588   891734 

summary(NuclearAPA_peakdist_notsig$DisttoPeak)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-70147526   -265059     -2067      7263    255169 125172864 

ggplot(NuclearAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

NuclearAPA_peakdist_filt=NuclearAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(NuclearAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

ggplot(NuclearAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)+ scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

ggplot(NuclearAPA_peakdist_sig, aes(x=DisttoPeak)) + geom_density(fill="deepskyblue3")+ labs(title="Nuclear: Distance from QTL to PAS Peak", x="Distance from SNP to PAS")

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()

[1] 192

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()

[1] 420

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()

[1] 726

Expression metrics

Next I want to pull in the expression values and compare the expression of genes with a nuclear APA qtl in comparison to genes without one. I will also need to pull in the gene names file to add in the gene names from the ensg ID.

Remove the # from the file.

expression=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T,stringsAsFactors = F)
expression_mean=apply(expression[,5:73],1,mean,na.rm=TRUE)
expression_var=apply(expression[,5:73],1,var,na.rm=TRUE)
expression$exp.mean= expression_mean 
expression$exp.var=expression_var
expression= expression %>% separate(ID, into=c("Gene.stable.ID", "ver"), sep ="[.]")

Now I can pull in the names and join the dataframes.

geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F) 

expression=expression %>% inner_join(geneNames,by="Gene.stable.ID") 

expression=expression %>% select(Chr, start, end, Gene.name, exp.mean,exp.var) %>%  rename("gene"=Gene.name)

Now I can join this with the qtls.

NuclearAPA_wExp=NuclearAPA %>% inner_join(expression, by="gene") 

gene_wQTL_N= NuclearAPA_wExp %>% group_by(gene) %>% summarise(sig_gene=sum(as.numeric(as.character(sig)))) %>% ungroup() %>% inner_join(expression, by="gene") %>% mutate(sigGeneFactor=ifelse(sig_gene>=1, 1,0))

gene_wQTL_N$sigGeneFactor= as.factor(as.character(gene_wQTL_N$sigGeneFactor))
summary(gene_wQTL_N$sigGeneFactor)

   0    1 
4589  607 

There are 607 genes with a QTL

ggplot(gene_wQTL_N, aes(x=exp.mean, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) +labs(title="Mean in RNA expression by genes with significant QTL", x="Mean in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

I can do a similar analysis but test the variance in the gene expression.

ggplot(gene_wQTL_N, aes(x=exp.var, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) + labs(title="Varriance in RNA expression by genes with significant QTL", x="Variance in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

ggplot(NuclearAPA_peakdist, aes(x=PeakScore,fill=sig,by=sig)) + geom_density(alpha=.5)+ scale_x_log10() + labs(title="Peak score by significance") + scale_fill_brewer(palette="Paired")

chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)

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

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))

shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.txt

import pybedtools 

RANDnuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.sort.bed') 



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

#map hmm to snps  
NucRnad_overlapHMM=RANDnuc.map(hmm, c=4)


#save results  

NucRnad_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random_overlapHMM.bed")

NuclearRandOverlapHMM=read.table("../data/ChromHmmOverlap/ApaQTL_nuclear_Random_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))

NuclearRandOverlapHMM_names=NuclearRandOverlapHMM %>% left_join(chromHmm, by="number")

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

random_perChromHMM_nuc=NuclearRandOverlapHMM_names %>%  group_by(name) %>% summarise(Random=n())
sig_perChromHMM_nuc= NuclearOverlapHMM_names %>%  group_by(name) %>%  summarise(Nuclear_QTLs=n())

perChrommHMM_nuc=random_perChromHMM_nuc %>%  full_join(sig_perChromHMM_nuc, by="name", ) %>% replace_na(list(Random=0,Total_QTLs=0))  

perChrommHMM_nuc_melt=melt(perChrommHMM_nuc, id.vars="name")
names(perChrommHMM_nuc_melt)=c("Region","Set", "N_Snps" )

chromenrichNuclearplot=ggplot(perChrommHMM_nuc_melt, aes(x=Region, y=N_Snps, by=Set, fill=Set)) + geom_bar(position="dodge", stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Enrichment of Nuclear QTLs by chromatin region", y="Number of Snps", x="Chromatin Region") + scale_fill_brewer(palette="Paired")
chromenrichNuclearplot

ggsave("../output/plots/ChromHmmEnrich_Nuclear.png", chromenrichNuclearplot)

Saving 7 x 5 in image

perChrommHMM_nuc$Random= as.integer(perChrommHMM_nuc$Random)
perChrommHMM_nuc_enr=perChrommHMM_nuc %>%  mutate(Nuclear=Nuclear_QTLs-Random)

perChrommHMM_tot_enr=read.table("../data/ChromHmmOverlap/perChrommHMM_Total_enr.txt",stringsAsFactors = F,header = T)

allenrich=perChrommHMM_tot_enr %>% inner_join(perChrommHMM_nuc_enr, by="name") %>% select(name, Total, Nuclear)

allenrich_melt=melt(allenrich, id.vars="name")

chromenrichBoth=ggplot(allenrich_melt, aes(x=name, by=variable, y=value, fill=variable)) + geom_bar(stat="identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="QTL-Random for each bin by fraction", y="Num QTL SNPs - Num Random SNPs") + scale_fill_manual(values=c("darkviolet", "deepskyblue3"))


ggsave("../output/plots/ChromHmmEnrich_BothFrac.png", chromenrichBoth)

Saving 7 x 5 in image

Permutations

I want to permute the background snps so i can get a better expectation. To do this I need to chose random lines from the nominal file, change the lines to snp format, overlap with HMM, count how many are in each category, and append the list to a dataframe that is category by permuation. I will do all of this in python.

def main(inFile, outFile, nperm,nsamp):
  nom_res= pd.read_csv(inFile, sep="\t", encoding="utf-8",header=None)
  out=open(outFile, "w")
  categories=list(range(1,16))
  out.write(" ".join(categories)+'\n')
  
  def make_rand_snp(x):
    #x is from the random snps pulled from the nom_res, return the snp df
    chrom_list=list()
    start_list=list()
    end_list=list()
    name_list=list()
    pval_list=list()
    strand_list=list()
    for ln in x:
          pid, sid, dist, pval, slope = ln.split()
          chrom, pos= sid.split(":")
          name=sid
          start= int(pos)-1
          end=int(pos)
          strand=pid.split(":")[3].split("_")[1]
          pval=float(pval)
          chrom_list.append(chrom)
          start_list.append(start)
          end_list.append(end)
          name_list.append(name)
          pval_list.append(pval)
          strand_list.append(strand)
          # add info to the lists 
    #zip lists
    zip_list=list(zip(chrom_list,start_list,end_list,name_list,pval_list, strand_list))
    snp_df=pd.DataFrame(data=zip_list, columns=["Chrom", "Start", "End", "Name", "Pval", "Strand"])
    return snp_df
  
  for i in range(1, nperm+1):
    sample=nom_res.sample(nsamp)
    sample_snp=make_rand_snp(sample)
    sample_snp_sort=sample_snp.sort_values(by=['Chrom', 'Start'])
    hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
    sample_snp_bed=pybedtools.from_dataframe(sample_snp_sort)
    samp_overHMM=sample_snp_bed(hmm, c=4)
    samp_overHMM_df=pybedtools.to_dataframe(samp_overHMM,names=["chrom", "start", "end", "sid", "significance", "strand", "number"])
    samp_overHMM_df.groupby('number').count()
    #need to see how this comes out and how I can make it into a list, after i have the list for each I can zip them together (list_i)

if __name__ == "__main__":
    import sys
    import pybedtools
    import pandas as pd
    fraction = sys.argv[1]
    nperm= sys.argv[2]
    nperm=int(nperm)
    nsamp=sys.argv[3]
    nsamp=int(nsamp)
    inFile = "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_%s_NomRes.txt"%(fraction)
    outFile = "dataframe with res"%()
    main(inFile, outFile, nperm, nsamp)
    
    
    

Maybe it is better to make this a bash script that has a pipeline of different scripts. This way I wont have to worry about files/dataframes and all of that.

DO this for total first (118 snps)

total_random118_chromHmm.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm.out
#SBATCH --error=total_random118_chromHmm.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {1..100};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Total_118_${i}.txt
done

#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Total 118 ${i}
done 


#cat res together   
cat /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed/* > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.bed


#sort full file 
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.bed > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.sort.bed


#hmm overlap
python overlap_chromHMM.py  Total 118 1000 

#Next I would pull this into R to do the group by and average!

pull_random_lines.py

def main(inFile, outFile ,nsamp):
  nom_res= pd.read_csv(inFile, sep="\t", encoding="utf-8",header=None)
  out=open(outFile, "w")
  sample=nom_res.sample(nsamp)
  sample.to_csv(out, sep="\t", encoding='utf-8', index=False, header=F)
  out.close()
    
if __name__ == "__main__":
    import sys
    import pandas as pd
    fraction = sys.argv[1]
    nsamp=sys.argv[2]
    nsamp=int(nsamp)
    iter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_%s_NomRes.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/randomRes_%s_%d_%s.txt"%(fraction,fraction, nsamp, iter)
    main(inFile, outFile, nsamp)

randomRes2SNPbed.py

def main(inFile, outFile):
    fout=open(outFile, "w")
    fin=open(inFile, "r")
    for ln in fin:
          pid, sid, dist, pval, slope = ln.split()
          chrom, pos= sid.split(":")
          name=sid
          start= int(pos)-1
          end=int(pos)
          strand=pid.split(":")[3].split("_")[1]
          pval=float(pval)
          fout.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end, name, pval, strand))
    fout.close()

if __name__ == "__main__":
    import sys
    fraction=sys.argv[1]
    nsamp=sys.argv[2]
    nsamp=int(nsamp)
    iter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/randomRes_%s_%d_%s.txt"%(fraction,fraction, nsamp, iter)
    outFile= "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/snp_bed/randomRes_%s_%d_%s.bed"%(fraction,fraction, nsamp, iter)
    main(inFile,outFile) 

overlap_chromHMM.py

def main(inFile, outFile):
  rand=pybedtools.BedTool(inFile) 
  hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
  #map hmm to snps
  Rand_overlapHMM=rand.map(hmm, c=4)
  #save results
  Rand_overlapHMM.saveas(outFile)


if __name__ == "__main__":
    import sys
    import pandas as pd
    import pybedtools
    fraction=sys.argv[1]
    nsamp=sys.argv[2]
    niter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/snp_bed_all/randomRes_%s_%s_ALLperm.sort.bed"%(fraction,fraction, nsamp)
    outFile= "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/chromHMM_overlap/randomres_overlapChromHMM_%s_%s_%s.txt"%(fraction,fraction,nsamp, niter)
    main(inFile,outFile)

*Nuclear 880

nuclear_random880_chromHmm.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=nuc_random880_chromHmm.out
#SBATCH --error=nuc_random880_chromHmm.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {1..1000};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Nuclear 880 ${i} 
done 


#cat res together   
cat /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed/* > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed


#sort full file 
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.sort.bed


#hmm overlap
python overlap_chromHMM.py  Nuclear 880 1000

#Next I would pull this into R to do the group by and average!

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