Last updated: 2019-10-08
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Knit directory: apaQTL/analysis/
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library(seqLogo)
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In this analysis I will plot the distribution of signal sites upstream of the PAS I have found.
First I use a python script to make a bed file with the 100 base pairs upsream of the PAS:
module load Anaconda3
source activate three-prime-env
mkdir ../data/SignalSiteFiles
python Upstream100Bases_general.py ../data/PAS/APAPAS_GeneLocAnno.5perc.bed ../data/SignalSiteFiles/APAPAS_100up.bed
Now I use bedtools nuc to get the sequence for each of these regions:
sbatch getSeq100up.sh
I can now run the DistPAS2Sig.py which will give me the location for the signal site for each PAS.I am running this with the 12 most common PAS signal sites.
sbatch run_distPAS2Sig.sh
Upload all of the results:
Loc_AATAAA= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")
Join these together:
AllsiteDF=as.data.frame(rbind(Loc_AATAAA,Loc_AAAAAG,Loc_AATACA,Loc_AATAGA,Loc_AATATA,Loc_ACTAAA,Loc_AGTAAA,Loc_ATTAAA, Loc_GATAAA,Loc_TATAAA,Loc_CATAAA, Loc_AAAAAA))
colourCount = length(unique(AllsiteDF$Site))
getPalette = colorRampPalette(brewer.pal(8, "Set1"))
AllsiteDF_sep = AllsiteDF %>% separate(PAS, int=c("GenePeak", "Location"), sep="_")
ggplot(AllsiteDF_sep, aes(x=Distance2PAS, by=Site, col=Site)) + stat_ecdf() + facet_wrap(~Location)
Check to see if any PAS have more than one signal site detected:
AllsiteDFmultsites=AllsiteDF %>% group_by(PAS) %>% mutate(nSites=n()) %>% filter(nSites>1)
First take the perfect match within 50 bp then use the closest.
Write out the AllSite in order to use it in the chooseSignalSite.py script:
write.table(AllsiteDF, file="../data/SignalSiteFiles/AllSignalSite.txt", quote=F, col.names = F, row.names = F, sep="\t")
python chooseSignalSite.py ../data/SignalSiteFiles/AllSignalSite.txt ../data/SignalSiteFiles/AllSignalSite_1perPAS.txt
AllsiteDF_1per=read.table(file="../data/SignalSiteFiles/AllSignalSite_1perPAS.txt", col.names = colnames(AllsiteDF)) %>% mutate(NegCount=-1*as.integer(as.character(Distance2PAS)))
Plot
dist2signalsiteplot=ggplot(AllsiteDF_1per, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites") + scale_fill_manual(values = getPalette(colourCount))
dist2signalsiteplot
ggsave(dist2signalsiteplot, file="../output/SignalSitePlot.png")
Saving 7 x 5 in image
Plot with proportion:
allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names = c("chr","start","end","PAS","score","strand"))
AllsiteDF_1per_prop= AllsiteDF_1per %>% group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/nrow(allPAS))
Plot with prop:
dist2signalsiteplotprop=ggplot(AllsiteDF_1per_prop, aes(group=Site, x=NegCount,y=prop, fill=Site)) + geom_histogram(position="stack",bins=50,stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites",fill="Site Sequence") + scale_fill_manual(values = getPalette(colourCount))+ theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16), legend.position = "bottom",plot.title = element_text(size=22))+ guides(fill=guide_legend(nrow=3,byrow=TRUE))
Warning: Ignoring unknown parameters: binwidth, bins, pad
dist2signalsiteplotprop
nrow(AllsiteDF_1per)/nrow(allPAS)
[1] 0.5718249
Seperate by location:
AllsiteDF_1per_sep= AllsiteDF_1per %>%separate(PAS, int=c("GenePeak", "Location"), sep="_")
dist2signalsiteplot_byloc=ggplot(AllsiteDF_1per_sep, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites") + facet_wrap(~Location)+ scale_fill_manual(values = getPalette(colourCount))
dist2signalsiteplot_byloc
ggsave(dist2signalsiteplot_byloc, file="../output/SignalSitePlotbyLoc.png")
Saving 7 x 5 in image
Proportion:
allPAS_byloc=allPAS %>% separate(PAS,into=c("peakid", "loc"),sep="_") %>% group_by(loc) %>% summarise(nLoc=n())
allPAS_byloc_new=as.data.frame(allPAS_byloc$nLoc %>% t())
colnames(allPAS_byloc_new) = allPAS_byloc$loc
AllsiteDF_1per_sep_INTRON=AllsiteDF_1per_sep %>% filter(Location=="intron") %>% group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/(allPAS_byloc_new$intron)) %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))
ggplot(AllsiteDF_1per_sep_INTRON, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack",bins=5, stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites \nfor Intronic PAS", caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") + scale_fill_manual(values = getPalette(colourCount))+ theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))
Warning: Ignoring unknown parameters: binwidth, bins, pad
AllsiteDF_1per_sep_UTR=AllsiteDF_1per_sep %>% filter(Location=="utr3") %>% group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/(allPAS_byloc_new$intron)) %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))
ggplot(AllsiteDF_1per_sep_UTR, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack", stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites \nin UTR",caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") + scale_fill_manual(values = getPalette(colourCount)) + theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))+ guides(fill=guide_legend(nrow=1,byrow=TRUE))
Warning: Ignoring unknown parameters: binwidth, bins, pad
Propwith=c(nrow(AllsiteDF_1per_sep %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf=as.data.frame(cbind(Location=Locations,Proportion=Propwith))
propdf$Proportion=as.numeric(as.character(propdf$Proportion))
all=ggplot(propdf,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
all
AllsiteDF_1per_sep_noncon=AllsiteDF_1per_sep %>% filter(Site != "AATAAA")
Propwithnotcon=c(nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf_noncon=as.data.frame(cbind(Location=Locations,Proportion=Propwithnotcon))
propdf_noncon$Proportion=as.numeric(as.character(propdf_noncon$Proportion))
non=ggplot(propdf_noncon,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
non
AllsiteDF_1per_sep_con=AllsiteDF_1per_sep %>% filter(Site == "AATAAA")
Propwithcon=c(nrow(AllsiteDF_1per_sep_con %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf_con=as.data.frame(cbind(Location=Locations,Proportion=Propwithcon))
propdf_con$Proportion=as.numeric(as.character(propdf_con$Proportion))
con=ggplot(propdf_con,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
con
plot_grid(all, con, non, labels=c("All PAS", "Cononical PAS", "Non-conical PAS"))
For future analysis I want to have a set of PAS with evidence for a signal site. I want those signal sites upstream 10-50 basepairs.
AllsiteDF_1per_use= AllsiteDF_1per %>% filter(Distance2PAS>10, Distance2PAS<50) %>% separate(PAS,into=c("peakid", "loc"),sep="_") %>% separate(peakid,into=c("Peaknum", "gene"),sep=":") %>% mutate(PAS=paste("peak", Peaknum, sep="")) %>% dplyr::rename("UpstreamDist"=NegCount) %>% select(PAS, gene, loc, Site, UpstreamDist)
ggplot(AllsiteDF_1per_use, aes(x=loc)) + geom_histogram(stat="count")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Write these out for
write.table(AllsiteDF_1per_use, file="../data/PAS/PASwSignalSite.txt", col.names = T, row.names = F, quote = F, sep="\t")
AllsiteDF_1per_prop_col= AllsiteDF_1per_prop %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))
dist2signalsiteplotprop=ggplot(AllsiteDF_1per_prop_col, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack",bins=50,stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites",fill=" ", caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") + theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))+ guides(fill=guide_legend(nrow=1,byrow=TRUE)) + scale_fill_manual(values = getPalette(colourCount))
Warning: Ignoring unknown parameters: binwidth, bins, pad
dist2signalsiteplotprop
#scale_fill_discrete(name="Site", labels=c("AATAAA","ATTAAA", "AAAAAA, AAAAAG, AATACA, AATAGA, AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA")
Enrichment in intron
N intronic that have a PAS:
propwSS_intron=AllsiteDF_1per_use %>% filter(loc=="intron") %>% nrow()
#nuclear PAS
intronPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", col.names = c("chr","start","end", "id", "score", "strand")) %>% separate(id, into=c("pas", "loc"), sep="_") %>% filter(loc=="intron") %>% nrow()
withSS_intron=propwSS_intron/intronPAS
withSS_intron
[1] 0.2477136
propwSS_utr=AllsiteDF_1per_use %>% filter(loc=="utr3") %>% nrow()
#nuclear PAS
utrPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", col.names = c("chr","start","end", "id", "score", "strand")) %>% separate(id, into=c("pas", "loc"), sep="_") %>% filter(loc=="utr3") %>% nrow()
propwSS_utr/utrPAS
[1] 0.7495176
intronannotation=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort.bed", col.names = c("chr", "start", "end", "loc", "gene", "score", "strand"))%>% mutate(name=paste(gene, loc, strand, sep="_")) %>% select(chr, start, end, name, score, strand)
write.table(intronannotation, "/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intronNamed.sort.bed", col.names = F, row.names = F,quote = F, sep="\t")
Compare this to a background:
I need 40 basepair regions in introns.
bedtools makewindows -i src -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intronNamed.sort.bed -w 40 > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort_randomIntervals.bed
Make this into a bed file (with strand):
python fix_randomIntron.py
I need to get the sequences for these with bedtools nuc.
bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort_randomIntervals.fixed.bed > /project2/gilad/briana/apaQTL/data/SignalSiteFiles/ncbiRefSeq_intron.sort_randomIntervalsSeq.bed
sbatch run_dist2sig_randomintron.sh
Upload all of the results:
Loc_AATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG_randomIntron= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")
Join these together:
AllsiteDF_randomIntron=as.data.frame(rbind(Loc_AATAAA_randomIntron,Loc_AAAAAG_randomIntron,Loc_AATACA_randomIntron,Loc_AATAGA_randomIntron,Loc_AATATA_randomIntron,Loc_ACTAAA_randomIntron,Loc_AGTAAA_randomIntron,Loc_ATTAAA_randomIntron, Loc_GATAAA_randomIntron,Loc_TATAAA_randomIntron,Loc_CATAAA_randomIntron, Loc_AAAAAA_randomIntron))
Number of tested sites:
withSS_random=nrow(AllsiteDF_randomIntron)
possiblereg=84432042
propwithRandom=withSS_random/possiblereg
propwithRandom
[1] 0.002426792
Difference in prop test:
prop.test(x=c(withSS_random,propwSS_intron), n=c(possiblereg,intronPAS),alternative = "less" )
2-sample test for equality of proportions with continuity
correction
data: c(withSS_random, propwSS_intron) out of c(possiblereg, intronPAS)
X-squared = 313010, df = 1, p-value < 2.2e-16
alternative hypothesis: less
95 percent confidence interval:
-1.0000000 -0.2389699
sample estimates:
prop 1 prop 2
0.002426792 0.247713593
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_0.9.4 gplots_3.0.1 reshape2_1.4.3
[4] workflowr_1.4.0 RColorBrewer_1.1-2 forcats_0.3.0
[7] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[10] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[13] ggplot2_3.1.1 tidyverse_1.2.1 seqLogo_1.48.0
loaded via a namespace (and not attached):
[1] gtools_3.8.1 tidyselect_0.2.5 haven_1.1.2
[4] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2
[7] htmltools_0.3.6 stats4_3.5.1 yaml_2.2.0
[10] rlang_0.4.0 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 caTools_1.17.1.1
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] broom_0.5.1 Rcpp_1.0.2 KernSmooth_2.23-15
[28] scales_1.0.0 backports_1.1.2 gdata_2.18.0
[31] jsonlite_1.6 fs_1.3.1 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 rprojroot_1.3-2
[37] bitops_1.0-6 cli_1.1.0 tools_3.5.1
[40] magrittr_1.5 lazyeval_0.2.1 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_0.10 R6_2.3.0
[52] nlme_3.1-137 git2r_0.25.2 compiler_3.5.1