Last updated: 2020-01-21
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/OppositeMap.Rmd
Modified: analysis/annotationInfo.Rmd
Modified: analysis/comp2apaQTLPAS.Rmd
Modified: analysis/correlationPhenos.Rmd
Modified: analysis/index.Rmd
Modified: analysis/investigatePantro5.Rmd
Modified: analysis/multiMap.Rmd
Modified: analysis/speciesSpecific.Rmd
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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 3ef9947 | brimittleman | 2020-01-21 | write out dobule filtered |
html | a9924fb | brimittleman | 2020-01-21 | Build site. |
html | 42c66a9 | brimittleman | 2020-01-21 | Build site. |
Rmd | c27674c | brimittleman | 2020-01-21 | remove disc in chimp |
html | c211e21 | brimittleman | 2020-01-18 | Build site. |
Rmd | 7a9f608 | brimittleman | 2020-01-18 | finished benchmark |
html | 0c14f51 | brimittleman | 2020-01-17 | Build site. |
Rmd | 11b26b4 | brimittleman | 2020-01-17 | add benchmark res |
html | 9024e86 | brimittleman | 2020-01-17 | Build site. |
Rmd | 6c72627 | brimittleman | 2020-01-17 | add code to make sample APS |
html | 5eef3eb | brimittleman | 2020-01-16 | Build site. |
Rmd | 5c24c0c | brimittleman | 2020-01-16 | add cutoff code files |
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
In this analyisis I will chose the 3 sets of 6 individuals randomly from my previous study to use for a benchmark analysis.
I will need to rerun the snakefile each time.
indiv=read.table("../../apaQTL/data/MetaDataSequencing.txt", header= T, stringsAsFactors = F) %>% dplyr::select(line) %>% unique()
mkdir ../data/OverlapBenchmark
Randomly choose 3 sets:
#sample1= sample(indiv$line, 6)
#sample2= sample(indiv$line, 6)
#sample3= sample(indiv$line, 6)
#save(sample1, sample2,sample3, file = "../data/OverlapBenchmark/samples.RData")
load("../data/OverlapBenchmark/samples.RData")
sample1
[1] "NA19144" "NA18508" "NA18858" "NA19239" "NA18504" "NA19119"
sample2
[1] "NA18522" "NA18516" "NA18504" "NA19119" "NA19131" "NA19137"
sample3
[1] "NA19131" "NA18486" "NA19210" "NA19223" "NA19092" "NA19200"
mkdir ../../PAS_Sample1
mkdir ../../PAS_Sample1/code
mkdir ../../PAS_Sample1/data
mkdir ../../PAS_Sample1/data/fastq
mkdir ../../PAS_Sample2
mkdir ../../PAS_Sample2/code
mkdir ../../PAS_Sample2/data
mkdir ../../PAS_Sample2/data/fastq
mkdir ../../PAS_Sample3/
mkdir ../../PAS_Sample3/code/
mkdir ../../PAS_Sample3/data/
mkdir ../../PAS_Sample3/data/fastq
Move over these fastq files.
I will also move the necessary snakefile and run files.
Next steps:
’
mkdir ../data/cleanPeaks_anno
bedtools map -a ../data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed -b /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation.sort.bed -c 4 -S -o distinct > ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed
python chooseAnno2Bed.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed
python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.SAF
mkdir ../data/CleanLiftedPeaks_FC/
sbatch quantLiftedPAS.sh
###
python fixFChead_bothfrac.py ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc
python makeFileID.py ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../data/CleanLiftedPeaks_FC/HumanFileID.txt
mkdir ../data/phenotype/
python makePheno.py ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../data/CleanLiftedPeaks_FC/HumanFileID.txt ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt
Rscript pheno2countonly.R -I ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt -O ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt
python convertNumeric.py ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt
Pull these in, pull just the nuclear and get the mean:
Sample 1
Sample1Anno=read.table("../../PAS_Sample1/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")
Sample1Ind=colnames(Sample1Anno)[9:ncol(Sample1Anno)]
Sample1Usage=read.table("../../PAS_Sample1/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample1Ind) %>% dplyr::select(contains("_N"))
Sample1All=as.data.frame(cbind(cbind(Sample1Anno[,1:8], Sample1=rowMeans(Sample1Usage))))
Sample 2:
Sample2Anno=read.table("../../PAS_Sample2/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")
Sample2Ind=colnames(Sample2Anno)[9:ncol(Sample2Anno)]
Sample2Usage=read.table("../../PAS_Sample2/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample2Ind) %>% dplyr::select(contains("_N"))
Sample2All=as.data.frame(cbind(cbind(Sample2Anno[,1:8], Sample2=rowMeans(Sample2Usage))))
Sample 3
Sample3Anno=read.table("../../PAS_Sample3/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")
Sample3Ind=colnames(Sample3Anno)[9:ncol(Sample3Anno)]
Sample3Usage=read.table("../../PAS_Sample3/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample3Ind) %>% dplyr::select(contains("_N"))
Sample3All=as.data.frame(cbind(cbind(Sample3Anno[,1:8], Sample3=rowMeans(Sample3Usage))))
I need to make a bed file with these to overlap with the original PAS. I want 100 bp on each side of the end. Positive strand (actual negative strand) take
Sample1bed=Sample1All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>% dplyr::select(chr, newStart, newEnd, PASName, Sample1, strand)
#write.table(Sample1bed,"../data/OverlapBenchmark/sample1PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )
Sample2bed=Sample2All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>% dplyr::select(chr, newStart, newEnd, PASName, Sample2, strand)
#write.table(Sample2bed,"../data/OverlapBenchmark/sample2PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )
Sample3bed=Sample3All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>% dplyr::select(chr, newStart, newEnd, PASName, Sample3, strand)
#write.table(Sample3bed,"../data/OverlapBenchmark/sample3PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )
sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample1PAS.bed > ../data/OverlapBenchmark/sample1PAS_sort.bed
sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample2PAS.bed > ../data/OverlapBenchmark/sample2PAS_sort.bed
sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample3PAS.bed > ../data/OverlapBenchmark/sample3PAS_sort.bed
Next step is to assess the overlap.
sbatch overlapapaQTLPAS_samples.sh
Sample 1 res:
wOverlap1=read.table("../data/OverlapBenchmark/sample1PAS_sort.Intersect.bed", col.names = colnames(Sample1bed)) %>% mutate(overlap="yes")
noOverlap1=read.table("../data/OverlapBenchmark/sample1PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample1bed)) %>% mutate(overlap="no")
AllwOinfo1=as.data.frame(rbind(wOverlap1, noOverlap1))
nrow(AllwOinfo1)
[1] 77850
overlap1=c()
totalvec1=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfo1 %>% filter(Sample1>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlap1=c(overlap1,prop)
totalvec1=c(totalvec1,total)
}
plot(seq_usage,overlap1,main="Sample 1", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
Version | Author | Date |
---|---|---|
0c14f51 | brimittleman | 2020-01-17 |
Sample 2
wOverlap2=read.table("../data/OverlapBenchmark/sample2PAS_sort.Intersect.bed", col.names = colnames(Sample2bed)) %>% mutate(overlap="yes")
noOverlap2=read.table("../data/OverlapBenchmark/sample2PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample2bed)) %>% mutate(overlap="no")
AllwOinfo2=as.data.frame(rbind(wOverlap2, noOverlap2))
nrow(AllwOinfo2)
[1] 106751
overlap2=c()
totalvec2=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfo2 %>% filter(Sample2>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlap2=c(overlap2,prop)
totalve2c=c(totalvec2,total)
}
plot(seq_usage,overlap2,main="Sample 2", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
Version | Author | Date |
---|---|---|
0c14f51 | brimittleman | 2020-01-17 |
Sample 3
wOverlap3=read.table("../data/OverlapBenchmark/sample3PAS_sort.Intersect.bed", col.names = colnames(Sample3bed)) %>% mutate(overlap="yes")
noOverlap3=read.table("../data/OverlapBenchmark/sample3PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample3bed)) %>% mutate(overlap="no")
AllwOinfo3=as.data.frame(rbind(wOverlap3, noOverlap3))
nrow(AllwOinfo3)
[1] 96769
overlap3=c()
totalvec3=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfo3 %>% filter(Sample3>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlap3=c(overlap3,prop)
totalvec3=c(totalvec3,total)
}
plot(seq_usage,overlap3,main="Sample 2", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
Version | Author | Date |
---|---|---|
0c14f51 | brimittleman | 2020-01-17 |
Plot with all:
allSamp=as.data.frame(cbind(seq_usage,overlap1,overlap2,overlap3))
actualres= read.table("../data/CompapaQTLpas/ExtendedResoverlap.txt", header = T)
SampandRes=allSamp %>% inner_join(actualres,by="seq_usage")
colnames(SampandRes)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")
SampandRes_gather=SampandRes %>% gather(key="set", value="overlap",-Cutoff)
Plot:
bench=ggplot(SampandRes_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")
bench
I will filter this to only genes passing the filter I established in the previous:
PassingGenes=read.table("../data/OverlapBenchmark/genesPassingCuttoff.txt", header = T, stringsAsFactors = F)
AllwOinfo1_filt=AllwOinfo1 %>% separate(PASName, into=c("gene", "PAS"), sep="_")%>% filter(gene %in% PassingGenes$genes)
AllwOinfo2_filt=AllwOinfo2 %>% separate(PASName, into=c("gene", "PAS"), sep="_") %>% filter(gene %in% PassingGenes$genes)
AllwOinfo3_filt=AllwOinfo3 %>% separate(PASName, into=c("gene", "PAS"), sep="_") %>% filter(gene %in% PassingGenes$genes)
Actual results:
chroms=c('chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr1', 'chr2', 'chr20', 'chr21', 'chr22', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7','chr8', 'chr9')
compAPAPAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed", header = T, stringsAsFactors = F) %>% filter(Human>=0.05, chr %in% chroms)
metaDataPAS=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, gene)
wOverlapExt=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect_ext.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="yes")
noOverlapExt=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect.NoOverlap_ext.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="no")
AllwOinfoExt=as.data.frame(rbind(wOverlapExt, noOverlapExt)) %>% inner_join(metaDataPAS,by="PAS") %>% filter(gene %in% PassingGenes$genes)
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
nrow(AllwOinfoExt)
[1] 36802
overlapFilt1=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfo1_filt %>% filter(Sample1>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlapFilt1=c(overlapFilt1,prop)
}
overlapFilt2=c()
for (i in seq_usage){
x=AllwOinfo2_filt %>% filter(Sample2>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlapFilt2=c(overlapFilt2,prop)
}
overlapFilt3=c()
for (i in seq_usage){
x=AllwOinfo3_filt %>% filter(Sample3>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlapFilt3=c(overlapFilt3,prop)
}
overlapFiltActual=c()
for (i in seq_usage){
x=AllwOinfoExt %>% filter(Human>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlapFiltActual=c(overlapFiltActual,prop)
}
FilteredRes=as.data.frame(cbind(seq_usage,overlapFilt1,overlapFilt2,overlapFilt3,overlapFiltActual))
colnames(FilteredRes)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")
FilteredRes_gather=FilteredRes %>% gather(key="set", value="overlap",-Cutoff)
filteredbench=ggplot(FilteredRes_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples\n filtered gene expresssion") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")
filteredbench
Last step is to remove the PAS that were discovered originally in chimp.
PASdisc=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% filter(disc!="Chimp")
Filter and rerun loop for percent overlap. Only the actual need to be updated for this.
AllwOinfoExt_noChimp =AllwOinfoExt %>% filter(PAS %in% PASdisc$PAS)
Rerun:
overlapFiltActualnoChimp=c()
for (i in seq_usage){
x=AllwOinfoExt_noChimp %>% filter(Human>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlapFiltActualnoChimp=c(overlapFiltActualnoChimp,prop)
}
FilteredResNochimp=as.data.frame(cbind(seq_usage,overlapFilt1,overlapFilt2,overlapFilt3,overlapFiltActualnoChimp))
colnames(FilteredResNochimp)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")
FilteredResNochimp_gather=FilteredResNochimp %>% gather(key="set", value="overlap",-Cutoff)
filteredbencnoChimop=ggplot(FilteredResNochimp_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples\n filtered gene expresssion, remove discovered in Chimp") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")
filteredbencnoChimop
Version | Author | Date |
---|---|---|
42c66a9 | brimittleman | 2020-01-21 |
Plot together:
plot_grid(bench,filteredbench,filteredbencnoChimop)
Looks like using a 10% usage and this expression cuttoff is pretty good.
I will write out the list of PAS and call them DoubleFilter
I will put in the PAS meta data one more time and filter human or chimp 10% and the genes in the passing list.
mkdir ../data/PAS_doubleFilter
PAS=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt",header = T,stringsAsFactors = F) %>% filter(gene %in% PassingGenes$genes) %>% filter(Chimp >=.1 || Human >= .1)
This brings it to 42K PAS.
write.table(PAS, "../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",col.names = T,row.names = F, quote = F)
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 rlang_0.4.0
[9] later_0.7.5 pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[17] gtable_0.2.0 workflowr_1.5.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 labeling_0.3 knitr_1.20 httpuv_1.4.5
[25] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1 scales_1.0.0
[29] backports_1.1.2 jsonlite_1.6 fs_1.3.1 hms_0.4.2
[33] digest_0.6.18 stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.1.0 tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[41] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[45] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[53] compiler_3.5.1