Last updated: 2020-03-31
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
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Rmd | 721b56f | brimittleman | 2020-03-31 | add prop |
html | 4d772b1 | brimittleman | 2020-03-31 | Build site. |
Rmd | 8280da5 | brimittleman | 2020-03-31 | add distance plots |
html | 1274a02 | brimittleman | 2020-03-30 | Build site. |
html | 469010e | brimittleman | 2020-03-30 | Build site. |
Rmd | ef52dfc | brimittleman | 2020-03-30 | add all overlap orthoexon |
html | 3d4dd47 | brimittleman | 2020-03-27 | Build site. |
Rmd | f92b89c | brimittleman | 2020-03-26 | add ortho exon and new mm |
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
I am still concerned about annotation. I want to see if using the ortho exon file as an annotation tool would be more appropriate. I want to look at it and see if there is a way to combine exons by gene and see if the spaces inbetween could be introns.
First look at the human one.
HumanOrtho=read.table("/project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf", sep="\t")
Parse this into a bed file with a python script.
mkdir ../data/OrthoExonBed
python SAF2Bed.py /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf ../data/OrthoExonBed/human.noM.bed
sort -k1,1 -k2,2n ../data/OrthoExonBed/human.noM.bed > ../data/OrthoExonBed/human.noM.sort.bed
sbatch overlapPASandOrthoexon.sh
Results:
PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors = F) %>% select(PAS, gene,loc)
OverlapPAS=read.table("../data/OrthoExonBed/allPASinOrtho.bed", col.names = c("chr", "start", "end", "PAS", "score", "strand"),stringsAsFactors = F)%>% group_by(PAS) %>% summarize(n=n())%>% mutate(OE="In") %>% inner_join(PASMeta,by="PAS")
nrow(OverlapPAS)
[1] 25621
NotOverlapPAS=read.table("../data/OrthoExonBed/allPAS_NOT_inOrtho.bed", col.names = c("chr", "start", "end", "PAS", "score", "strand"),stringsAsFactors = F)%>% group_by(PAS) %>% summarize(n=n())%>% mutate(OE="OUT") %>% inner_join(PASMeta,by="PAS")
nrow(NotOverlapPAS)
[1] 16697
ALLPAS_ortho=OverlapPAS %>% bind_rows(NotOverlapPAS)
ggplot(ALLPAS_ortho, aes(x=OE, by=loc,fill=loc)) +geom_bar(stat="count",position = "dodge") + labs(x="Is PAS overlapping ortho exon", title="All PAS in OrthoExon",y="Number of PAS")+ scale_fill_brewer(palette = "Dark2")
Version | Author | Date |
---|---|---|
469010e | brimittleman | 2020-03-30 |
Ok this is the expected distribution, We expect the coding and 3’ UTRs to be in the ortho exon file. What is more interesting is genes with and without exons in the ortho exon.
Take this to the gene level.
OrthoBed=read.table("../data/OrthoExonBed/human.noM.sort.bed", col.names = c("chr","start","end","gene", "nExon","strand"),stringsAsFactors = F) %>% group_by(gene) %>% summarise(nExon=n())
Now I look to see which of the PAS are in genes not in the ortho exon.
PASMeta_GeneOE= PASMeta %>% mutate(OE=ifelse(gene %in% OrthoBed$gene, "Yes", "No"))
PASMeta_GeneOEgene= PASMeta_GeneOE %>% group_by(gene, OE) %>% summarise()
ggplot(PASMeta_GeneOEgene, aes(x=OE, fill=OE))+ geom_histogram(stat="count") + labs(x="Is gene in Ortho Exon", y="Genes") + scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Version | Author | Date |
---|---|---|
469010e | brimittleman | 2020-03-30 |
Look at the genes without:
PASMeta_GeneOE_NO= PASMeta_GeneOE %>% filter(OE=="No")
nrow(PASMeta_GeneOE_NO)
[1] 5293
ggplot(PASMeta_GeneOE_NO,aes(x=loc, fill=OE)) + geom_bar(stat="count")
Genesnotin=PASMeta_GeneOE_NO %>% group_by(gene) %>% summarise(n=n())
1000 genes not in ortho exons.
#sno
Genesnotin %>% filter(grepl("SNO",gene)) %>% nrow()
[1] 112
#linc
Genesnotin %>% filter(grepl("LINC",gene)) %>% nrow()
[1] 63
#loc
Genesnotin %>% filter(grepl("LOC",gene)) %>% nrow()
[1] 327
Are these the genes with different dominant PAS.
allPAS= read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T)
ChimpPASwMean =allPAS %>% dplyr::select(-Human)
HumanPASwMean =allPAS %>% dplyr::select(-Chimp)
Chimp_Dom= ChimpPASwMean %>%
group_by(gene) %>%
top_n(1,Chimp) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
dplyr::select(gene,loc,PAS,Chimp) %>%
rename(ChimpLoc=loc, ChimpPAS=PAS)
Human_Dom= HumanPASwMean %>%
group_by(gene) %>%
top_n(1, Human) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
dplyr::select(gene,loc,PAS,Human) %>%
rename(HumanLoc=loc, HumanPAS=PAS)
#merge
BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
DifDom= BothDom %>% filter(ChimpPAS!=HumanPAS)
Plot this before I remove those genes.
DifDom_before=DifDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("species","loc",-gene)
ggplot(DifDom_before, aes(x=loc, by=species, fill=species))+geom_histogram(position = "dodge",stat = "count")+ labs(title="Location of PAS with different Dominant",y="PAS")+scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Version | Author | Date |
---|---|---|
469010e | brimittleman | 2020-03-30 |
Remove the not ortho genes:
DifDom_after=DifDom_before %>% anti_join(Genesnotin,by="gene")
Warning: Column `gene` joining factor and character vector, coercing into
character vector
ggplot(DifDom_after, aes(x=loc, by=species, fill=species))+geom_histogram(position = "dodge",stat = "count")+ labs(title="Location of PAS with different Dominant\n after removing genes not in orthoexon",y="PAS")+scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Version | Author | Date |
---|---|---|
469010e | brimittleman | 2020-03-30 |
That didnt help.
Check the matching ones (are these in ortho exon)
SameDom= BothDom %>% filter(ChimpPAS==HumanPAS)
nrow(SameDom)
[1] 7638
SameDom_after=SameDom %>% anti_join(Genesnotin,by="gene")
Warning: Column `gene` joining factor and character vector, coercing into
character vector
nrow(SameDom_after)
[1] 6906
Where did I lose genes.
6906/7638
[1] 0.9041634
3432/4028
[1] 0.8520357
Lose more in the different dominant than in the same dominant. Percent lost=
(7638-6906)/7638
[1] 0.09583661
(4028-3432)/4028
[1] 0.1479643
I will check if some of the problem genes, I found in my pipeline are in this.
ChimpMM=read.table("../data/multimap/Chimp_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
HumanMM=read.table("../data/multimap/Human_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
BothMM=read.table("../data/multimap/Both_multimapPAS.txt",stringsAsFactors = F, header = T)
AllMM=ChimpMM %>% bind_rows(HumanMM) %>% bind_rows(BothMM)
I will overlap this with the ortho exon file. I will look at those that overlap and those that do not. I need a bed file
AllMM_bed=AllMM %>% mutate(Name=paste(gene, PAS,loc, MultiMap, sep=":")) %>% select(chr, start,end, Name, Human, strandFix)
write.table(AllMM_bed,"../data/multimap/allMM.bed",col.names = F, quote = F, row.names = F, sep="\t")
sort -k1,1 -k2,2n ../data/multimap/allMM.bed > ../data/multimap/allMM.sort.bed
Overlap.
sbatch overlapMMandOrthoexon.sh
Results:
InOrtho=read.table("../data/OrthoExonBed/allMMinOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n())%>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":") %>% mutate(OE="In")
NotInOrtho=read.table("../data/OrthoExonBed/allMM_NOT_inOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n()) %>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":") %>% mutate(OE="Out")
AllOrthores=InOrtho %>% bind_rows(NotInOrtho)
ggplot(AllOrthores, aes(x=OE, by=loc,fill=loc)) +geom_bar(stat="count",position = "dodge") + labs(x="Is PAS overlapping ortho exon", title="PAS impacted by multimapping and ortho exon",y="Number of PAS")+ scale_fill_brewer(palette = "Dark2")
Version | Author | Date |
---|---|---|
469010e | brimittleman | 2020-03-30 |
Proportion:
AllOrthores %>% group_by(OE) %>% summarise(n=n())
# A tibble: 2 x 2
OE n
<chr> <int>
1 In 831
2 Out 578
#look only at utr
AllOrthores %>% filter(loc=="utr3") %>% group_by(OE) %>% summarise(n=n())
# A tibble: 2 x 2
OE n
<chr> <int>
1 In 492
2 Out 120
Look at the UTR sequences that are in:
AllOrthores %>% filter(OE=="In", loc=="utr3")
# A tibble: 492 x 6
gene PAS loc MM n OE
<chr> <chr> <chr> <chr> <int> <chr>
1 AARS2 human285010 utr3 Human 2 In
2 ABCB10 human30678 utr3 Both 2 In
3 ABHD17A human153108 utr3 Both 8 In
4 ABR chimp125493 utr3 Human 10 In
5 ACAD8 chimp63259 utr3 Chimp 6 In
6 ACAD8 human66039 utr3 Chimp 6 In
7 ACTB human299134 utr3 Both 7 In
8 ADAM9 chimp303728 utr3 Human 8 In
9 ADAM9 chimp303729 utr3 Human 8 In
10 ADH5 human247010 utr3 Chimp 2 In
# … with 482 more rows
I can look at the intronic PAS in both species to see how far they are from an ortho exon.
I need to merge exons.
sort -k1,1 -k2,2n ../data/OrthoExonBed/chimp.noM.bed > ../data/OrthoExonBed/chimp.noM.sort.bed
bedtools merge -s -c 4,5,6 -o distinct,count,distinct -i ../data/OrthoExonBed/chimp.noM.sort.bed > ../data/OrthoExonBed/chimp.noM.sort.merged.bed
bedtools merge -s -c 4,5,6 -o distinct,count,distinct -i ../data/OrthoExonBed/human.noM.sort.bed > ../data/OrthoExonBed/human.noM.sort.merged.bed
mkdir ../data/TestAnnoBiasOE
Now I can map all of the intronic PAS
For intronic PAS in genes in the ortho exon, filter the bed files
PASMeta_GeneOE_intronYes= PASMeta_GeneOE %>% filter(loc=="intron", OE=="Yes")
HumanBed=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr", 'start','end','PAS','usage','strand'),stringsAsFactors = F) %>% semi_join(PASMeta_GeneOE_intronYes, by="PAS")
write.table(HumanBed, "../data/TestAnnoBiasOE/HumanIntronicGeneinOE.bed",col.names = F, row.names = F, quote = F, sep="\t")
ChimpBed=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed", col.names = c("chr", 'start','end','PAS','usage','strand'),stringsAsFactors = F) %>% semi_join(PASMeta_GeneOE_intronYes, by="PAS")
write.table(ChimpBed, "../data/TestAnnoBiasOE/ChimpIntronicGeneinOE.bed",col.names = F, row.names = F, quote = F, sep="\t")
Find the closest:
same strand. look only upstream (-id) to say closest annotated 5’ splice site. then do only downstream to say closest annotated 3’ splice site.
sbatch ClosestorthoEx.sh
Look at upstream
If the gene is a different gene, make it 0
HumanUpstream=read.table("../data/TestAnnoBiasOE/HumanUpstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "HumanUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "UpstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, UpstreamdistancePAS2Exon,0))
HumanDownstream=read.table("../data/TestAnnoBiasOE/HumanDownstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "HumanUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "DownstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, DownstreamdistancePAS2Exon,0))
HumanBoth=as.data.frame(cbind(HumanUpstream[,1:6],gene=HumanUpstream[,14], "UpstreamHuman"=HumanUpstream[,16],"DownstreamHuman"=HumanDownstream[,16])) %>%
mutate(DominanceHuman=ifelse(PAS %in% BothDom$HumanPAS, "yes","no"))
Chimp:
ChimpUpstream=read.table("../data/TestAnnoBiasOE/ChimpUpstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "ChimpUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "UpstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, UpstreamdistancePAS2Exon,0))
ChimpDownstream=read.table("../data/TestAnnoBiasOE/ChimpDownstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "ChimpUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "DownstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, DownstreamdistancePAS2Exon,0))
ChimpBoth=as.data.frame(cbind(PAS=ChimpUpstream[,4], 'UpstreamChimp'=ChimpUpstream[,16],'DownstreamChimp'=ChimpDownstream[,16])) %>%
mutate(DominanceChimp=ifelse(PAS %in% BothDom$ChimpPAS, "yes","no"))
Join together:
BothSpeciesDistance= HumanBoth %>% inner_join(ChimpBoth, by="PAS")
Warning: Column `PAS` joining character vector and factor, coercing into
character vector
BothSpeciesDistance$UpstreamChimp=as.numeric(as.character(BothSpeciesDistance$UpstreamChimp))
BothSpeciesDistance$DownstreamChimp=as.numeric(as.character(BothSpeciesDistance$DownstreamChimp))
Look at distance based on human dominance then chimp dominance
ggplot(BothSpeciesDistance, aes(x=UpstreamHuman, y=UpstreamChimp, col=DominanceHuman)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
ggplot(BothSpeciesDistance, aes(x=UpstreamHuman, y=UpstreamChimp, col=DominanceHuman)) + geom_point(alpha=.3) + ylim(c(-10000,0)) +xlim(c(-10000,0)) + geom_abline(aes(slope=1,intercept=0)) + scale_color_brewer(palette = "Dark2")
Warning: Removed 1651 rows containing missing values (geom_point).
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
I want to color by dominant in human, chimp, both neither
BothSpeciesDistance_dom=BothSpeciesDistance %>% mutate(Dominance=ifelse(DominanceHuman=="yes", ifelse(DominanceChimp=="yes", "Both", "human"), ifelse(DominanceChimp=="yes", "chimp", "Neither")))
Upstream Plots:
ggplot(BothSpeciesDistance_dom, aes(x=UpstreamHuman, y=UpstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0)) + geom_smooth(method="lm", alpha=.1)+scale_color_brewer(palette = "Dark2")
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
ggplot(BothSpeciesDistance_dom, aes(x=UpstreamHuman, y=UpstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))+ylim(c(-50000,0)) +xlim(c(-50000,0)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.1)
Warning: Removed 122 rows containing non-finite values (stat_smooth).
Warning: Removed 122 rows containing missing values (geom_point).
Warning: Removed 1 rows containing missing values (geom_smooth).
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
Downstream
ggplot(BothSpeciesDistance_dom, aes(x=DownstreamHuman, y=DownstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.1) + geom_smooth(method="lm", alpha=.1)
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
There are a lot of PAS that are far from an exon on the human side.
ggplot(BothSpeciesDistance_dom, aes(x=DownstreamHuman, y=DownstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))+ylim(c(0,50000)) +xlim(c(0,50000)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.01)
Warning: Removed 395 rows containing non-finite values (stat_smooth).
Warning: Removed 395 rows containing missing values (geom_point).
Warning: Removed 3 rows containing missing values (geom_smooth).
Version | Author | Date |
---|---|---|
4d772b1 | brimittleman | 2020-03-31 |
Look at the top examples:
BothSpeciesDistance_dom_TopDiffUp=BothSpeciesDistance_dom %>% mutate(Updiff=abs(UpstreamHuman-UpstreamChimp)) %>% select(PAS, gene, Updiff) %>% arrange(desc(Updiff))
head(BothSpeciesDistance_dom_TopDiffUp)
PAS gene Updiff
1 human34001 SFMBT2 51262
2 chimp298751 ACTR3C 34423
3 human296634 TFB1M 33486
4 human175470 ANKRD36 31986
5 chimp298752 ACTR3C 31871
6 human175469 ANKRD36 31766
I can look at where these are in the “intron”. Here I am calling the space between 2 orthologous exons in the same genes introns. I will remove PAS that have 0 distance on either side in either species first.
BothSpeciesDistance_dom_remove0= BothSpeciesDistance_dom %>% filter(UpstreamHuman!=0,DownstreamHuman!=0,UpstreamChimp!=0, DownstreamChimp!=0 )
nrow(BothSpeciesDistance_dom)-nrow(BothSpeciesDistance_dom_remove0)
[1] 1123
nrow(BothSpeciesDistance_dom_remove0)
[1] 8229
Lose 1123. Looking at 8229.
BothSpeciesDistance_dom_remove0_dist= BothSpeciesDistance_dom_remove0 %>% mutate(HumanLength=abs(UpstreamHuman)+ DownstreamHuman, HumanProp=abs(UpstreamHuman)/HumanLength, ChimpLength=abs(UpstreamChimp)+DownstreamChimp, ChimpProp=abs(UpstreamChimp)/ChimpLength)
ggplot(BothSpeciesDistance_dom_remove0_dist,aes(x=HumanProp, y=ChimpProp,col=Dominance)) + geom_point(alpha=.3) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm") + labs(x="Proportion of Human Intron",y= "Proportion of Chimp Intron", title="Intronic PAS between ortho exons")
BothSpeciesDistance_dom_remove0_dist_filt=BothSpeciesDistance_dom_remove0_dist %>% filter(Dominance !="Neither")
ggplot(BothSpeciesDistance_dom_remove0_dist_filt,aes(x=HumanProp, y=ChimpProp,col=Dominance)) + geom_point(alpha=.3) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm") + labs(x="Proportion of Human Intron", y="Proportion of Chimp Intron", title="Intronic PAS between ortho exons")
What about just length:
ggplot(BothSpeciesDistance_dom_remove0_dist,aes(x=HumanLength, y=ChimpLength,col=Dominance)) + geom_point(alpha=.3) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm") + labs(x="Human Distance between ortho exon", "Chimp Distance between ortho exon", title="Intronic PAS between ortho exons")
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 cellranger_1.1.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[28] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[31] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 magrittr_1.5
[43] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[46] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[49] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[52] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[55] git2r_0.26.1 compiler_3.5.1