Last updated: 2020-06-08

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/unliftedsites.Rmd

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File Version Author Date Message
Rmd 16a604b brimittleman 2020-06-08 add all utr analysis
html 4ff1c53 brimittleman 2020-06-08 Build site.
Rmd 085794d brimittleman 2020-06-08 add trpseq analysis 
library(tidyverse)
── Attaching packages ──────────────── tidyverse 1.2.1 ──
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library(ggpubr)
Loading required package: magrittr

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    extract

THe trip seq paper has a method to explore 3’ UTR characteristics from a bed file

https://github.com/stephenfloor/tripseq-analysis Test transcriptome_properties.py

I need the name of the genome and a bed file. I can start by testing on the ortho 3’ UTRs

clone code/tripseq-analysis python 2

source ~/activate_anaconda_python2.sh
module load bedtools
#in trip
python transcriptome_properties.py -i ../../data/orthoUTR/HumanDistal3UTR.sort.bed -g /project2/gilad/kenneth/References/human/genome/hg38.fa   --au-elements

This gets the au rich element %.

can do this with human and chimp:

python transcriptome_properties.py -i ../../data/orthoUTR/HumanDistal3UTR.sort.bed -g /project2/gilad/kenneth/References/human/genome/hg38.fa   --au-elements  -o ../../data/orthoUTR/HumanOrthoUTR_AUrich

python transcriptome_properties.py -i ../../data/orthoUTR/ChimpDistal3UTR.sort.bed -g /project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa --au-elements  -o ../../data/orthoUTR/ChimpOrthoUTR_AUrich
HumanOrthUTR_au=read.csv("../data/orthoUTR/HumanOrthoUTR_AUrich_au_elements.csv",header = T, stringsAsFactors = F) 
ChimpOrthoUTR_au=read.csv("../data/orthoUTR/ChimpOrthoUTR_AUrich_au_elements.csv",header = T, stringsAsFactors = F)

Corr:

HumanOrthUTR_au_sm=HumanOrthUTR_au %>% select(transcriptID, au_element_frac) %>% rename(HumanAU=au_element_frac)
ChimpOrthUTR_au_sm=ChimpOrthoUTR_au %>% select(transcriptID, au_element_frac) %>% rename(ChimpAU=au_element_frac)

BothAU=HumanOrthUTR_au_sm %>% inner_join(ChimpOrthUTR_au_sm, by="transcriptID")

cor.test(BothAU$ChimpAU, BothAU$HumanAU)

    Pearson's product-moment correlation

data:  BothAU$ChimpAU and BothAU$HumanAU
t = 584.48, df = 15739, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9770321 0.9784085
sample estimates:
      cor 
0.9777308

AU number:

HumanOrthUTR_au_Numsm=HumanOrthUTR_au %>% select(transcriptID, au_element_count) %>% rename(HumanAU=au_element_count)
ChimpOrthUTR_au_Numsm=ChimpOrthoUTR_au %>% select(transcriptID, au_element_count) %>% rename(ChimpAU=au_element_count)

BothAUNum=HumanOrthUTR_au_Numsm %>% inner_join(ChimpOrthUTR_au_Numsm, by="transcriptID")

cor.test(BothAU$ChimpAU, BothAU$HumanAU)

    Pearson's product-moment correlation

data:  BothAU$ChimpAU and BothAU$HumanAU
t = 584.48, df = 15739, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9770321 0.9784085
sample estimates:
      cor 
0.9777308

same high correlation…

ggplot(BothAU, aes(x=HumanAU, y=ChimpAU)) + geom_point() + geom_abline(slope=1) + geom_density_2d()

Color by dAPA and diff iso diversity.

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 
Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) 

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")

dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))

dAPAonly=dAPAandDic %>% filter(dAPA=="Yes") %>% select(gene) %>% mutate(set="dAPA")
both=dAPAandDic %>% filter(Both=="Yes") %>% select(gene)%>% mutate(set="Both")
IDonly=dAPAandDic %>% filter(OnlyIC=="Yes") %>% select(gene)%>% mutate(set="ID")



Allset=dAPAonly %>% bind_rows(both) %>% bind_rows(IDonly)
Nonegenes= Meta_PAS %>% select(gene) %>% unique()%>% anti_join(Allset, by="gene") %>% mutate(set="None")
#no diff:  
Allset_andnon=Allset %>% bind_rows(Nonegenes)
BothAU_fix= BothAU %>% separate(transcriptID, into=c("gene", "strand"), sep="\\(") %>% inner_join(Allset_andnon, by="gene") %>% mutate(anyDiff=ifelse(set=="None", "No", "Yes"))

BothAU_fix %>% group_by(set) %>% summarise(n())
# A tibble: 4 x 2
  set   `n()`
  <chr> <int>
1 Both    329
2 dAPA   1281
3 ID      345
4 None   5835

Do dAPA genes have higher AU

BothAU_fix_g= BothAU_fix %>% gather("Species", "AU", -gene, -strand, -set,-anyDiff)


ggplot(BothAU_fix_g, aes(x=AU,col=set)) +stat_ecdf() + facet_grid(~Species)

Version Author Date
4ff1c53 brimittleman 2020-06-08

box plots:

ggplot(BothAU_fix_g, aes(y=AU,x=set,fill=set)) +geom_boxplot() + facet_grid(~Species) + stat_compare_means()

ggplot(BothAU_fix_g, aes(y=AU,x=anyDiff,fill=anyDiff)) +geom_boxplot() + facet_grid(~Species) + stat_compare_means(method.args = list(alternative = "greater"))

BothAU_fix_g %>% group_by(Species,anyDiff) %>% summarise(mean(AU))
# A tibble: 4 x 3
# Groups:   Species [2]
  Species anyDiff `mean(AU)`
  <chr>   <chr>        <dbl>
1 ChimpAU No          0.0151
2 ChimpAU Yes         0.0162
3 HumanAU No          0.0152
4 HumanAU Yes         0.0163
BothAU_fix_g %>% group_by(Species,set) %>% summarise(mean(AU))
# A tibble: 8 x 3
# Groups:   Species [2]
  Species set   `mean(AU)`
  <chr>   <chr>      <dbl>
1 ChimpAU Both      0.0156
2 ChimpAU dAPA      0.0166
3 ChimpAU ID        0.0157
4 ChimpAU None      0.0151
5 HumanAU Both      0.0158
6 HumanAU dAPA      0.0166
7 HumanAU ID        0.0155
8 HumanAU None      0.0152

dAPA are the higher genes:

genes with expression diff:

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)
DiffExp=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" ,  "P.Value" ,  "adj.P.Val", "B"  )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID) %>% mutate(DE=ifelse(adj.P.Val<.05, "Yes", "No")) %>% select(gene,DE)

BothAU_fix_g_DE= BothAU_fix_g %>% inner_join(DiffExp,by="gene")
ggplot(BothAU_fix_g_DE, aes(y=AU,x=DE,fill=DE)) +geom_boxplot() + facet_grid(anyDiff~Species) + stat_compare_means()

Version Author Date
4ff1c53 brimittleman 2020-06-08

genes with differential translation:

Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No"))
RiboSmall= Ribo %>% select(gene,dTE)

BothAU_fix_g_TE= BothAU_fix_g %>% inner_join(RiboSmall,by="gene")


ggplot(BothAU_fix_g_TE, aes(y=AU,x=dTE,fill=dTE)) +geom_boxplot() + facet_grid(anyDiff~Species) + stat_compare_means()

Version Author Date
4ff1c53 brimittleman 2020-06-08

AU for the ortho UTRs are not different based on DE or translation status.

filter to dAPA genes and see if they are different in expression and translation:

BothAU_fix_g_DE_dAPA= BothAU_fix_g_DE %>% filter(set=="dAPA")
ggplot(BothAU_fix_g_DE_dAPA, aes(y=AU,x=DE,fill=DE)) +geom_boxplot() + facet_grid(~Species) + stat_compare_means()

BothAU_fix_g_TE_dAPA= BothAU_fix_g_TE %>% filter(set=="dAPA")
ggplot(BothAU_fix_g_TE_dAPA, aes(y=AU,x=dTE,fill=dTE)) +geom_boxplot() + facet_grid(~Species) + stat_compare_means()

ok so only significant relationship here is higher AU proportion in ortho 3’ UTRs for dAPA genes.

difference in dominance:

HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)

plotting au in human for each:

BothResAll_au= BothResAll %>% inner_join(BothAU_fix,by="gene")
BothResAll_au$cut=as.factor(BothResAll_au$cut)
ggplot(BothResAll_au,aes(x=cut, by=Set, y=HumanAU,fill=Set)) + geom_boxplot() + stat_compare_means(aes(label = ..p.signif..))

DP not DE:

ProtInfo=read.table("../data/PTM/ProtLength.txt", sep = "\t",stringsAsFactors = F,header = T,col.names = c("entry","organism", "nAA", "gene")) %>% select(nAA, gene)
Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F)  %>% mutate(dP=ifelse(pval<0.05, "Yes", "No")) %>% filter(dP=="Yes")
Interactions=read.table("../data/bioGRID/GeneswInteractions.txt",stringsAsFactors = F, header = T) %>% inner_join(ProtInfo, by="gene")%>% mutate(NormInter=nInt/nAA)

#DiffExp$gene, DT=Ribo$gene, DP=Prot$gene

dAPAandDic_wP=dAPAandDic %>% mutate(dE=ifelse(gene %in%DiffExp$gene, "Yes", "No" ), dP=ifelse(gene %in%Prot$gene,"Yes","No" ), dPnotDE=ifelse(dE=="No"&dP=="Yes", "Yes","No")) %>% inner_join(Interactions, by="gene") %>% inner_join(BothAU_fix,by="gene")
ggplot(dAPAandDic_wP, aes(x=dPnotDE, y=HumanAU)) + geom_boxplot()

only one gene with the pattern and an ortho 3’ UTR.

Human 3’ UTRs:

Merge from refseq anno:

mkdir ../data/HumanUTR
bedtools merge -s -i ../data/orthoUTR/g38_ncbiRefseq_Formatted_Allannotation_UTR3.bed -c 4,5,6 -o distinct,mean,distinct > ../data/HumanUTR/Human3UTR.merged.bed

still multiple per genes. take most 3’ ?

humanMergeutr= read.table("../data/HumanUTR/Human3UTR.merged.bed", col.names = c('chr','start','end','gene','score','strand'), stringsAsFactors = F) %>% group_by(gene)

humanMergeutrpos= humanMergeutr %>% filter(strand=="+") %>% group_by(gene) %>% top_n(1,start)

humanMergeutrneg= humanMergeutr %>% filter(strand=="-") %>% group_by(gene) %>% top_n(-1,start)

humanDistalboth=humanMergeutrpos %>% bind_rows(humanMergeutrneg)

write.table(humanDistalboth, "../data/HumanUTR/HumanDistal3UTR.bed", col.names = F, row.names = F, quote = F, sep="\t")

Run these through AU:

python transcriptome_properties.py -i../../data/HumanUTR/HumanDistal3UTR.bed -g /project2/gilad/kenneth/References/human/genome/hg38.fa   --au-elements  -o ../../data/HumanUTR/HumanAllUTR_AUrich
HumanALLUTR_au=read.csv("../data/HumanUTR/HumanAllUTR_AUrich_au_elements.csv",header = T, stringsAsFactors = F) %>%  separate(transcriptID, into=c("geneloc", "strand"), sep="\\(") %>% separate(geneloc, into=c("loc","gene"),sep=":") %>% na.omit()
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 611 rows
[63, 64, 65, 66, 135, 136, 409, 410, 463, 464, 611, 612, 691, 692, 974,
975, 1074, 1075, 1466, 1467, ...].
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 302 rows
[64, 66, 136, 410, 464, 612, 692, 975, 1075, 1467, 1747, 1955, 2461, 2566,
2620, 2688, 2801, 2970, 3002, 3029, ...].

missing pieces were overlapping.

dAPAandDic_wPAU=dAPAandDic %>% mutate(dE=ifelse(gene %in%DiffExp$gene, "Yes", "No" ), dP=ifelse(gene %in%Prot$gene,"Yes","No" ), dPnotDE=ifelse(dE=="No"&dP=="Yes", "Yes","No")) %>% inner_join(Interactions, by="gene") %>% inner_join(HumanALLUTR_au, by="gene")

dAPAandDic_wPAU$au_element_frac= as.numeric(as.character(dAPAandDic_wPAU$au_element_frac))
ggplot(dAPAandDic_wPAU, aes(x=dPnotDE, y=au_element_frac)) + geom_boxplot()

dAPAandDic_wPAU %>% group_by(dPnotDE) %>% summarise(n())
# A tibble: 2 x 2
  dPnotDE `n()`
  <chr>   <int>
1 No       7132
2 Yes         2

only 2…

Allset_andnon_au= Allset_andnon %>% inner_join(HumanALLUTR_au, by="gene")
Allset_andnon_au$au_element_frac= as.numeric(as.character(Allset_andnon_au$au_element_frac))


ggplot(Allset_andnon_au, aes(x=set,fill=set,y=au_element_frac)) + geom_boxplot() + stat_compare_means()

Allset_andnon_au %>% group_by(set ) %>% summarise(mean(au_element_frac))
# A tibble: 4 x 2
  set   `mean(au_element_frac)`
  <chr>                   <dbl>
1 Both                   0.0150
2 dAPA                   0.0161
3 ID                     0.0162
4 None                   0.0154
Allset_andnon_aude=Allset_andnon_au %>% inner_join(DiffExp,by="gene")

ggplot(Allset_andnon_aude, aes(x=set,fill=set,y=au_element_frac,by=DE)) + geom_boxplot() + stat_compare_means()

Allset_andnon_auTE=Allset_andnon_au  %>% inner_join(RiboSmall,by="gene")

ggplot(Allset_andnon_auTE, aes(x=set,fill=set,y=au_element_frac,by=dTE)) + geom_boxplot() + stat_compare_means()

ok there are no interesting relationships with this… I would have to proportion the UTRs probably…

number:

Allset_andnon_au$au_element_count=as.numeric(Allset_andnon_au$au_element_count)

ggplot(Allset_andnon_au, aes(x=set,fill=set,y=au_element_count)) + geom_boxplot() + stat_compare_means() +ylim(c(0,100))
Warning: Removed 1 rows containing non-finite values (stat_boxplot).
Warning: Removed 1 rows containing non-finite values (stat_compare_means).

Allset_andnon_auany= Allset_andnon_au %>% mutate(AnyAPA=ifelse(set=="None","No", "Yes"))

ggplot(Allset_andnon_auany, aes(x=AnyAPA,fill=AnyAPA,y=au_element_count)) + geom_boxplot() + stat_compare_means() +ylim(c(0,100))
Warning: Removed 1 rows containing non-finite values (stat_boxplot).
Warning: Removed 1 rows containing non-finite values (stat_compare_means).

ggplot(Allset_andnon_auany, aes(x=AnyAPA,fill=AnyAPA,y=au_element_frac)) + geom_boxplot() + stat_compare_means(method.args = list(alternative = "less")) 

Allset_andnon_auany_de= Allset_andnon_auany %>% inner_join(DiffExp,by="gene")

ggplot(Allset_andnon_auany_de, aes(x=DE,fill=AnyAPA,y=au_element_count)) + geom_boxplot() + stat_compare_means() +ylim(c(0,100))

dAPA have more AU elements but the other relationships dont make much sense. nothing is strong.. on hold for now.


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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 reshape2_1.4.3   haven_1.1.2      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] utf8_1.1.4       rlang_0.4.0      later_0.7.5      pillar_1.3.1    
[13] glue_1.3.0       withr_2.1.2      modelr_0.1.2     readxl_1.1.0    
[17] plyr_1.8.4       munsell_0.5.0    gtable_0.2.0     workflowr_1.6.0 
[21] cellranger_1.1.0 rvest_0.3.2      evaluate_0.12    labeling_0.3    
[25] knitr_1.20       httpuv_1.4.5     fansi_0.4.0      broom_0.5.1     
[29] Rcpp_1.0.4.6     promises_1.0.1   scales_1.0.0     backports_1.1.2 
[33] jsonlite_1.6     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  cli_1.1.0       
[41] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[45] pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0       lubridate_1.7.4 
[49] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[53] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1