Last updated: 2018-10-24

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    Rmd f819c8e Briana Mittleman 2018-10-11 add distance metric analsis total apaQTL


This analysis will be used to characterize the total ApaQTLs. I will run the analysis on the total APAqtls in this analysis and will then run a similar analysis on the nuclear APAqtls in another analysis. I would like to study:

  • Distance metrics:
    • distance from snp to TSS of gene
    • Distance from snp to peak
  • Expression metrics:
    • expression of genes with significant QTLs vs other genes (by rna seq)
    • expression of genes with significant QTLs vs other genes (peak coverage)
  • Chrom HMM metrics:
    • look at the chrom HMM interval for the significant QTLs

Upload Libraries and Data:

Library

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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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'
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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.

totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_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"))

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


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

Distance Metrics

Distance from snp to TSS

I ran the QTL analysis based on the starting position of the gene.

ggplot(totalAPA, 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.

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

It looks like most of the signifcant values are 100,000 bases. This makes sense. I can zoom in on this portion.

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

Expand here to see past versions of unnamed-chunk-4-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

Distance from snp to peak

To perform this analysis I need to recover the peak positions.

The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

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))

I want to join the peak start to the totalAPA file but the peak column. I will then create a column that is snppos-peakcenter.

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

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

Plot this by significance.

ggplot(totalAPA_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.

Expand here to see past versions of unnamed-chunk-7-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

Look at the summarys based on significance:

totalAPA_peakdist_sig=totalAPA_peakdist %>% filter(sig==1)
totalAPA_peakdist_notsig=totalAPA_peakdist %>% filter(sig==0)


summary(totalAPA_peakdist_sig$DisttoPeak)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-634474.0  -26505.0   -3325.5  -23883.8     492.5  460051.0 
summary(totalAPA_peakdist_notsig$DisttoPeak)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-70147526   -269853     -1269     -6620    266370   5433999 
ggplot(totalAPA_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.

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away

totalAPA_peakdist_filt=totalAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(totalAPA_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.

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

ggplot(totalAPA_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.

Expand here to see past versions of unnamed-chunk-10-2.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
eb02fbc Briana Mittleman 2018-10-11

This gives a similar distribution.

I did snp - peak. This means if the peak is downstream of the snp on the positive strand the number will be negative.

In this case the peak is downstream of the snp.

totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak < 0) %>% nrow()
[1] 45
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak > 0) %>% nrow()
[1] 16

Peak is upstream of the snp.

totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak > 0) %>% nrow()
[1] 17
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak < 0) %>% nrow()
[1] 40

This means there is about 50/50 distribution around the peak start.

I am going to plot a violin plot for just the significant ones.

ggplot(totalAPA_peakdist_sig, aes(x=DisttoPeak)) + geom_density()

Expand here to see past versions of unnamed-chunk-13-1.png:
Version Author Date
eb02fbc Briana Mittleman 2018-10-11

Within 1000 bases of the peak center.

totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()
[1] 29
totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()
[1] 57
totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()
[1] 98

29 QTLs are within 1000 bp of the peak center, 57 within 10,000bp and 98 within 100,000bp

Expression metrics

Next I want to pull in the expression values and compare the expression of genes with a total 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.

totalAPA_wExp=totalAPA %>% inner_join(expression, by="gene") 
ggplot(totalAPA_wExp, aes(x=exp.mean, by=sig, fill=sig)) + geom_density(alpha=.3)+ scale_fill_brewer(palette="Paired")

Expand here to see past versions of unnamed-chunk-18-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
f771110 Briana Mittleman 2018-10-12

This is not exactly what I want because there are multiple peaks in a gene so some genes are plotted multiple times. I want to group the QTLs by gene and see if there is 1 sig QTL for that gene.

gene_wQTL= totalAPA_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$sigGeneFactor= as.factor(as.character(gene_wQTL$sigGeneFactor))

Therea are 92 genes in this set with a QTL.

ggplot(gene_wQTL, 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.

Expand here to see past versions of unnamed-chunk-20-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
f771110 Briana Mittleman 2018-10-12

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

ggplot(gene_wQTL, 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.

Expand here to see past versions of unnamed-chunk-21-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
f771110 Briana Mittleman 2018-10-12

Peak coverage for QTLs

I can also look at peak coverage for peaks with QLTs and those without. I will first look at this on peak level then mvoe to gene level. The peak scores come from the coverage in the peaks.

The totalAPA_peakdist data frame has the information I need for this.

ggplot(totalAPA_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")

Expand here to see past versions of unnamed-chunk-22-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23
bb6c0de Briana Mittleman 2018-10-12

This is expected. It makes sense that we have more power to detect qtls in higher expressed peaks. This leads me to believe that filtering out low peaks may add power but will not mitigate the effect.

Where are the snps:

Download the GM12878 chromHMM annotation. I downleaded this from uscs and put it in:

  • /Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/GM12878.chromHMM.txt
  • /project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.txt

Column:

  • bin
  • chrom
  • chromstart
  • chromend
  • name
  • score
  • strand
  • thich start
  • thick end
  • item rgb

I can make this a bedfile to use a bedtools pipeline:

  • chrom (nochr)

  • start

  • end

  • name (txn hetero ect)

  • score

  • strand

fout = open("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.bed",'w')
for ln in open("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.txt", "r"):
    bin, chrom, start, end, name, score, strand, thSt, thE, rgb = ln.split()
    chrom=chrom[3:]
    name=name.split("_")[0]
    fout.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end, name, score, strand))
fout.close()
fout = open("/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/GM12878.chromHMM.bed",'w')
for ln in open("/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/GM12878.chromHMM.txt", "r"):
    bin, chrom, start, end, name, score, strand, thSt, thE, rgb = ln.split()
    chrom=chrom[3:]
    fout.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end, name, score, strand))
fout.close()

I also need to create a significant QTL snp bedfile for the total qtls. Bed files are 0 bases meaning I want the end to be the position I care about.

chrom, start (pos -1), end (pos), name, score (bh), strand

I can do this in python using the /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt. I will make the script general to use on the total or nuclear file.

QTLres2SigSNPbed.py


def main(inFile, outFile):
    fout=open(outFile, "w")
    fin=open(inFile, "r")
    for num, ln in enumerate(fin):
      if num >= 1:
          pid, nvar, shape1, shape2, dummy, sid, dist, npval, slope, ppval, bpval, bh = ln.split()
          chrom, pos= sid.split(":")
          name=sid
          start= int(pos)-1
          end=int(pos)
          strand=pid.split(":")[3].split("_")[1]
          bh=float(bh)
          if bh <= .1: 
              fout.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end, name, bh, strand))
    fout.close()

if __name__ == "__main__":
    import sys
    fraction=sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_%s_transcript_permResBH.txt"%(fraction)
    outFile= "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps/ApaQTLsignificantSnps_10percFDR_%s.bed"%(fraction)
    main(inFile,outFile) 

I am going to try to use pybedtools instead for bedtools for this analysis. First I can add it to my conda environment.

Remove header from HMM

import pybedtools 

sigNuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps/ApaQTLsignificantSnps_10percFDR_Nuclear.sort.bed') 

sigTot=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps/ApaQTLsignificantSnps_10percFDR_Total.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/perm_APAqtl_trans/sigSnps/Tot_overlapHMM.bed")

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

I want to make a file that has all of the numbers for the chromatin regions for downstream analysis.

cut -f5 GM12878.chromHMM.txt | sort | uniq > chromHMM_regions.txt

I then manually seperate the numbers from the name with a tab and remove the name line.

Evaluate results for total:

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

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

TotalOverlapHMM_names=TotalOverlapHMM %>% 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))

Expand here to see past versions of unnamed-chunk-29-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23

This is the count but I want enrichemnt. I need to randomly choose 188 snps from the ones I tested (nominal res) and get the same inforamtion on where they are.

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/nominal_APAqtl_trans/randomSnps/ApaQTL_total_Random118.txt

Now I need to make these into the snp bed format.

QTLNOMres2SigSNPbed.py give this * total or nuclear
* number of snps


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]
    number=sys.argv[2]
    inFile = "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_%s_Random%s.txt"%(fraction,number)
    outFile= "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_%s_Random%s.bed"%(fraction,number)
    main(inFile,outFile) 

Sort output

import pybedtools 

RANDtot=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_total_Random118.sort.bed') 



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

#map hmm to snps  
TotRnad_overlapHMM=RANDtot.map(hmm, c=4)


#save results  

TotRnad_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_total_Random_overlapHMM.bed")
TotalRandOverlapHMM=read.table("../data/ChromHmmOverlap/ApaQTL_total_Random_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))

TotalRandOverlapHMM_names=TotalRandOverlapHMM %>% left_join(chromHmm, by="number")
ggplot(TotalRandOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Total APAQtls (Random)" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Expand here to see past versions of unnamed-chunk-34-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23

To put this on the same plot I can count the number in each then plot them next to eachother.

random_perChromHMM=TotalRandOverlapHMM_names %>%  group_by(name) %>% summarise(Random=n())
sig_perChromHMM= TotalOverlapHMM_names %>%  group_by(name) %>%  summarise(Total_QTLs=n())

perChrommHMM=random_perChromHMM %>%  full_join(sig_perChromHMM, by="name", ) %>% replace_na(list(Random=0,Total_QTLs=0))  

perChrommHMM_melt=melt(perChrommHMM, id.vars="name")
names(perChrommHMM_melt)=c("Region","Set", "N_Snps" )
chromenrichTotalplot=ggplot(perChrommHMM_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 Total QTLs by chromatin region", y="Number of Snps", x="Chromatin Region") + scale_fill_brewer(palette="Paired")
chromenrichTotalplot

Expand here to see past versions of unnamed-chunk-36-1.png:
Version Author Date
cb92826 Briana Mittleman 2018-10-23

ggsave("../output/plots/ChromHmmEnrich_Total.png", chromenrichTotalplot)
Saving 7 x 5 in image

I want to make a plot with the enrichment by fraction. I am first going to get an enrichemnt score for each bin naively by looking at the QTL-random in each category.

perChrommHMM$Random= as.integer(perChrommHMM$Random)
perChrommHMM$Total_QTLs= as.integer(perChrommHMM$Total_QTLs)
perChrommHMM_enr=perChrommHMM %>%  mutate(Total=Total_QTLs- Random)

Write this file so I can put it in the nuclear analysis and compare between groups.

write.table(perChrommHMM_enr, file="../data/ChromHmmOverlap/perChrommHMM_Total_enr.txt", quote=F, sep="\t", col.names = T, row.names = F)

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      



This reproducible R Markdown analysis was created with workflowr 1.1.1