Difference in protein and APA not expression
Briana Mittleman
3/7/2020
Last updated: 2020-07-23
Checks: 7 0
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/ExploredAPA_DF.Rmd
Modified: analysis/MMExpreiment.Rmd
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Modified: analysis/comp2apaQTLPAS.Rmd
Modified: analysis/correlationPhenos.Rmd
Modified: analysis/dInforContent.Rmd
Modified: analysis/df_QC.Rmd
Modified: analysis/diffExpression.Rmd
Modified: analysis/establishCutoffs.Rmd
Modified: analysis/incorporateQTLsAncestral.Rmd
Modified: analysis/index.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
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 036755e | brimittleman | 2020-07-23 | add n in set |
| html | 1de69e8 | brimittleman | 2020-07-03 | Build site. |
| Rmd | 8f3e622 | brimittleman | 2020-07-02 | export supp plots |
| html | 961b808 | brimittleman | 2020-04-23 | Build site. |
| Rmd | c1bc496 | brimittleman | 2020-04-23 | add interaction density, dapa and e and order proble |
| html | d969183 | brimittleman | 2020-04-23 | Build site. |
| Rmd | 3770355 | brimittleman | 2020-04-23 | add overlap diff |
| html | 3790efa | brimittleman | 2020-04-23 | Build site. |
| Rmd | e513e9f | brimittleman | 2020-04-23 | add dot chart |
| html | 5d82297 | brimittleman | 2020-04-22 | Build site. |
| Rmd | 1e814ec | brimittleman | 2020-04-22 | change colors |
| html | 6d8725a | brimittleman | 2020-04-10 | Build site. |
| Rmd | fbabfb7 | brimittleman | 2020-04-10 | remove 18499 |
| html | c2a0778 | brimittleman | 2020-04-06 | Build site. |
| Rmd | 9676d72 | brimittleman | 2020-04-06 | updated anno |
| html | 30bfaaa | brimittleman | 2020-03-16 | Build site. |
| Rmd | 94211e8 | brimittleman | 2020-03-16 | add change misprime analysis and dapa qtls |
| html | f33ef5a | brimittleman | 2020-03-08 | Build site. |
| Rmd | 6146c33 | brimittleman | 2020-03-08 | add effect corr |
| html | 25a4790 | brimittleman | 2020-03-07 | Build site. |
| Rmd | c6a11f0 | brimittleman | 2020-03-07 | add expression indep |
library(tidyverse)── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──✔ ggplot2 3.2.1 ✔ purrr 0.3.4
✔ tibble 2.1.3 ✔ dplyr 0.8.3
✔ tidyr 1.1.0 ✔ stringr 1.4.0
✔ readr 1.3.1 ✔ forcats 0.4.0── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(UpSetR)
library(ggpubr)Upload:
Protein=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)%>% dplyr::rename("gene"=gene.symbol)
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name) %>% dplyr::rename("gene"=Gene.name)
NucAPA=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)I will do this first with these then I can start to look at it by significance.
apatested=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T) %>% select(gene) %>% unique()pnotE=Protein %>% anti_join(DEgenes,by="gene")
nrow(pnotE)[1] 725nrow(Protein)[1] 1262nrow(DEgenes)[1] 3794apatested %>% inner_join(pnotE,by="gene") %>% nrow()Warning: Column `gene` joining factor and character vector, coercing into
character vector[1] 506APAandPnotE= NucAPA %>% inner_join(Protein, by="gene") %>% anti_join(DEgenes,by="gene")listInput_nucOnly <- list(DE=DEgenes$gene, DAPA=NucAPA$gene, DP=Protein$gene)
#upset(fromList(listInput_nosplice), queries = list(list(query=intersects, params=list("DAPA", "DT", "DP"), color="red", active=T,query.name="APA, Ribo, Protein"),list(query=intersects, params=list("DE", "DT", "DP"), color="orange", active=T, query.name="Expression,Ribo, Protein"),list(query=intersects, params=list("DAPA", "DT"), color="blue", active=T, query.name="APA,Ribo") ,list(query=intersects, params=list("DAPA", "DP"), color="purple", active=T, query.name="APA, Protein"),list(query=intersects, params=list("DAPA", "DE"), color="green", active=T, query.name="APA, Expression")), order.by = "freq", query.legend = "bottom")
upset(fromList(listInput_nucOnly), order.by = "freq", keep.order = T,empty.intersections = "on", queries = list(list(query=intersects, params=list("DAPA", "DP"), color="darkorchid4", active=T,query.name="APA, Protein")))
90 of these genes.
Learn about these genes.
Selection:
model.num.rna: : 1 = mRNA expression level pattern consistent with directional selection along human lineage, 2 = mRNA expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage
model.num.protein: 1 = protein expression level pattern consistent with directional selection along human lineage, 2 = protein expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage
KhanData=read.csv("../data/Khan_prot/Khan_TableS4.csv",stringsAsFactors = F) %>% dplyr::select(gene.symbol,contains("model") ) %>% dplyr::rename("gene"=gene.symbol, "Protein"=model.num.protein, "RNA"=model.num.rna)
APAandPnotE_sel= APAandPnotE %>% inner_join(KhanData,by="gene")Plot the information about the RNA and protein for these:
APAandPnotE_sel_g=APAandPnotE_sel %>% dplyr::select(gene, Protein, RNA) %>% gather("Set", "Model", -gene)
APAandPnotE_sel_g$Model= as.factor(APAandPnotE_sel_g$Model)
ggplot(APAandPnotE_sel_g,aes(x=Model, by=Set, fill=Set)) + geom_bar(stat="count", position="dodge") + scale_fill_brewer(palette = "RdYlBu")
Plot protein only:
APAandPnotE_sel_gOnlyP= APAandPnotE_sel_g %>% filter(Set=="Protein")
APAandPnotE_sel_gOnlyP$Model= as.factor(APAandPnotE_sel_gOnlyP$Model)
ggplot(APAandPnotE_sel_gOnlyP,aes(x=Model)) + geom_bar(stat="count", position="dodge", fill="darkorchid4") + labs(y="Number of Genes", x="Protein Selection Model", title="Protein and APA differences\n no difference in Expression") + scale_x_discrete( labels=c("Selection Human","Selection Chimp","Undetermined","No mean difference","Relaxation in Chimp"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16)) 
The genes in 1,2,5,6 are interesting.
APAandPnotE_selCalled= APAandPnotE_sel_g %>% filter(Set=="Protein", Model %in% c(1,2,5,6))There are 20 of these genes:
APAandPnotE_selCalled gene Set Model
1 RRM1 Protein 1
2 SART3 Protein 1
3 SUGT1 Protein 2
4 VPS36 Protein 1
5 ATP6V1D Protein 1
6 GALNT2 Protein 2
7 GNAI3 Protein 2
8 SEC22B Protein 2
9 WDR77 Protein 2
10 KYNU Protein 2
11 PPIL3 Protein 1
12 CPOX Protein 2
13 MANBA Protein 1
14 BNIP1 Protein 1
15 CCT5 Protein 2
16 CYFIP2 Protein 1
17 MYO6 Protein 2
18 CUL1 Protein 2
19 VPS41 Protein 1
20 STOM Protein 1Where are the differential PAS in these genes:
#APAandPnotE_sel_gOnlyP
Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = T) %>% dplyr::rename("ChimpUsage"=Chimp, "HumanUsage"=Human)
NucAPAres=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", "start","end", "gene"))Warning: Column `chr` joining character vector and factor, coercing into
character vectorWarning: Column `gene` joining character vector and factor, coercing into
character vectorNucAPAres_DP= NucAPAres %>% filter(gene %in%APAandPnotE_sel_gOnlyP$gene ) %>% filter(SigPAU2=="Yes")
NucAPAresSig=NucAPAres %>% filter(SigPAU2=="Yes")THere are 154 PAS in this set:
ggplot(NucAPAres_DP,aes(x=loc,fill=loc))+ geom_bar(stat="count") + scale_fill_brewer(palette = "RdYlBu")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="", y="Number of PAS", title="Expression independent PAS locations")
Enrichment for this:
Compare to all of the significant in that location.
NucAPAres_sig= NucAPAres %>% filter(SigPAU2=="Yes") %>% mutate(dPnotE=ifelse(PAS %in% NucAPAres_DP$PAS,"Yes", "No"))
enrich=c()
pval=c()
for (i in c("cds", "end", "intron", "utr3")){
x=nrow(NucAPAres_sig %>% filter(dPnotE=="Yes", loc==i))
m=nrow(NucAPAres_sig %>% filter( loc==i))
n=nrow(NucAPAres_sig %>% filter(loc!=i))
k=nrow(NucAPAres_sig %>% filter(dPnotE=="Yes"))
N=nrow(NucAPAres_sig)
pval=c(pval, phyper(x-1,m,n,k,lower.tail=F))
enrichval=(x/k)/(m/N)
enrich=c(enrich, enrichval)
}
enrich[1] 1.3100158 0.3521451 0.7012860 1.2145339pval[1] 0.144079246 0.993503594 0.976950002 0.005705065NucAPAres_DPLocEnrich=NucAPAres_DP %>% group_by(loc) %>% summarise(n=n()) %>% bind_cols(enrichment=enrich, pvalue=pval)
locplot=ggplot(NucAPAres_DPLocEnrich, aes(x=loc, y=n, fill=loc)) + geom_bar(stat="identity") + scale_fill_brewer(palette = "RdYlBu")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="", y="Number of PAS", title="Expression independent PAS locations")+ geom_text(aes(label=paste("Enrichment=",round(enrichment,2), "X", sep=""), vjust=0)) +geom_text(aes(label=paste("Pval=",round(pvalue,3), sep=""), vjust=2))+ theme_classic() + theme(legend.position = "none", axis.text.x = element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
locplot
Interactions:
Are there differences in protien interactions for these.
Interactions=read.table("../data/bioGRID/GeneswInteractions.txt",stringsAsFactors = F, header = T)
OrthoUTR=read.table("../data/orthoUTR/HumanDistal3UTR.sort.bed", col.names = c("chr",'start','end','gene','score','strand'),stringsAsFactors = F) %>% mutate(length=end-start) %>% select(gene, length)
InteractionsAPA=Interactions %>%filter(gene %in% NucAPAresSig$gene) %>% mutate(dPnotE=ifelse(gene %in% NucAPAres_DP$gene, "Yes", "No"))%>% inner_join(OrthoUTR, by="gene") %>% mutate(density=nInt/length)
ggplot(InteractionsAPA,aes(x=dPnotE, y=log10(nInt+1),fill=dPnotE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_brewer(palette = "Set1")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="Gene in Expression independent set", y="log10(Number of Protein Interactions)", title="Protein Interactions for Expression \nindependent dAPA genes")
| Version | Author | Date |
|---|---|---|
| 1de69e8 | brimittleman | 2020-07-03 |
Plot density?
ggplot(InteractionsAPA,aes(x=dPnotE, y=log10(density),fill=dPnotE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_brewer(palette = "Set1")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="Gene in Expression independent set", y="log10(UTR density of interactions)", title="Protein Interactions for Expression \nindependent dAPA genes")
More likly to have one:
InteractionsAPA %>% mutate(HasInteraction=ifelse(nInt>0, "Yes", "No")) %>% group_by(dPnotE, HasInteraction) %>% summarise(nWithSet=n())# A tibble: 2 x 3
# Groups: dPnotE [2]
dPnotE HasInteraction nWithSet
<chr> <chr> <int>
1 No Yes 1292
2 Yes Yes 86Set should be the interaction set dAPA, de, and dP.
Alldiff=Protein %>% inner_join(DEgenes,by="gene") %>% inner_join(NucAPA, by="gene") %>% dplyr::select(gene)
#This is 101 genes.
geneAPAPnotEG=APAandPnotE %>% dplyr::select(gene)
GenesMatter= Alldiff %>% bind_rows(geneAPAPnotEG) %>% mutate(Ex=ifelse(gene %in% geneAPAPnotEG$gene, "No", "Yes")) %>% inner_join(Interactions, by="gene")ggplot(GenesMatter, aes(x=Ex, y=nInt, fill=Ex))+ geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "RdYlBu")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="DE gene", y="Number of protein protein interactions", title="dAPA, DP, and DE")
Effect sizes :
Look at the PAS effect sizes here and in protien, translation, and expression.
NucAPAres_sig_dpnotE = NucAPAres_sig %>% filter(dPnotE =="Yes")
#nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)
#DE data
DE=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)
#translation
Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% dplyr::rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% dplyr::rename("gene"=Gene.name)
#prot
Prot=read.table("../data/Khan_prot/ProtData_effectSize.txt", header = T, stringsAsFactors = F)APAandE=NucAPAres_sig_dpnotE %>% inner_join(DE, by="gene")
ggplot(APAandE, aes(x=logFC, y=deltaPAU)) + geom_point(alpha=.3) + geom_smooth(method="lm") +stat_cor()
ggplot(APAandE, aes(x=logFC, y=deltaPAU, col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm") +stat_cor(label.x = 1)
APAandRibo=NucAPAres_sig_dpnotE %>% inner_join(Ribo, by="gene")
ggplot(APAandRibo, aes(x=HvC.beta, y=deltaPAU)) + geom_point(alpha=.3) + geom_smooth(method="lm") +stat_cor()
APAandprot=NucAPAres_sig_dpnotE %>% inner_join(Prot, by="gene")
ggplot(APAandprot, aes(x=logEf, y=deltaPAU))+ geom_point(alpha=.3) + geom_smooth(method="lm") +stat_cor( )
ggplot(APAandprot, aes(x=logEf, y=deltaPAU, col=loc))+ geom_point(alpha=.3) + geom_smooth(method="lm") +stat_cor( )
None of these are significant.
Check if any of these are genes with QTLs.
I will pull in the genes with nuclear apaQTLs first.
apaQTLs=read.table("../../apaQTL/data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.sort.bed",col.names = c('chr','start','end', 'PASid','score', 'strand')) %>% separate(PASid, into=c("gene", "PAS", "loc"),sep=":")
apaQTLGenes= apaQTLs %>% select(gene) %>% unique()APAandPnotE_apaQTL=APAandPnotE %>% mutate(apaQTL=ifelse(gene %in% apaQTLGenes$gene, "Yes", "No"))
APAandPnotE_apaQTL %>% group_by(apaQTL) %>% summarize(n=n())# A tibble: 2 x 2
apaQTL n
<chr> <int>
1 No 86
2 Yes 4APAandPnotE_apaQTL %>% filter(apaQTL=="Yes") gene HC.qvalues.protein ENSG apaQTL
1 STAT6 0.049128043 ENSG00000166888 Yes
2 RHOT1 0.009672323 ENSG00000126858 Yes
3 RNASEL 0.006755528 ENSG00000135828 Yes
4 BNIP1 0.021646516 ENSG00000113734 YesBackground for enrichment is all of the dAPA genes.
x= nrow(APAandPnotE_apaQTL %>% filter(apaQTL =="Yes"))
m= nrow(APAandPnotE_apaQTL)
n=nrow(NucAPA)- nrow(APAandPnotE_apaQTL)
k=nrow(apaQTLGenes)
x[1] 4#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 4phyper(x,m, n , k,lower.tail=F)[1] 1Not enriched for apaQTL.
pQTLs
Using protien specific QTLs from Battle et al.
pQTLs=read.table("../../apaQTL/data/Battle_pQTL/psQTLGeneNames.txt")
APAandPnotE_pQTL=APAandPnotE %>% mutate(pQTL=ifelse(gene %in% pQTLs$V1, "Yes", "No"))
APAandPnotE_pQTL %>% group_by(pQTL) %>% summarize(n=n())# A tibble: 2 x 2
pQTL n
<chr> <int>
1 No 86
2 Yes 4APAandPnotE_pQTL %>% filter(pQTL=="Yes") gene HC.qvalues.protein ENSG pQTL
1 TARS2 0.004796093 ENSG00000143374 Yes
2 ZBTB8OS 0.000021900 ENSG00000176261 Yes
3 NUP50 0.002061087 ENSG00000093000 Yes
4 UBA6 0.000154285 ENSG00000033178 Yesx= nrow(APAandPnotE_pQTL %>% filter(pQTL =="Yes"))
m= nrow(APAandPnotE_pQTL)
n=nrow(NucAPA)- nrow(APAandPnotE_pQTL)
k=nrow(pQTLs)
x[1] 4#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 4phyper(x,m, n , k,lower.tail=F)[1] 0.8959063Are any of the these the diff dom set? Test .4 first:
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_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
NucAPAres_sig_sm= NucAPAres_sig %>% filter(dPnotE=="Yes")
BothRes_40_dp= BothRes_40 %>% filter(gene %in% NucAPAres_sig_sm$gene)
BothRes_40_dp %>% group_by(Set) %>% summarise(n())# A tibble: 2 x 2
Set `n()`
<chr> <int>
1 Different 8
2 Same 32metaSm= Meta %>% select(loc, PAS)
DiffHuman= BothRes_40_dp %>% filter(Set=="Different") %>% select(gene, Human_PAS) %>% rename(PAS= Human_PAS)%>% inner_join(metaSm, by="PAS")Warning: Column `PAS` joining character vector and factor, coercing into
character vectorDiffChimp= BothRes_40_dp %>% filter(Set=="Different") %>% select(gene, Chimp_PAS)%>% rename(PAS= Chimp_PAS)%>% inner_join(metaSm, by="PAS")Warning: Column `PAS` joining character vector and factor, coercing into
character vectorDiffHuman gene PAS loc
1 SEC22B human18938 end
2 EIF4G2 human54877 utr3
3 TUBGCP3 human100208 intron
4 IRF3 human170101 utr3
5 HK2 human183666 intron
6 PPIL3 chimp195902 intron
7 FLNB human233016 utr3
8 CPOX human235691 utr3DiffChimp gene PAS loc
1 SEC22B chimp17094 end
2 EIF4G2 human54890 cds
3 TUBGCP3 chimp91832 utr3
4 IRF3 human170093 utr3
5 HK2 human183677 utr3
6 PPIL3 human199028 utr3
7 FLNB human233019 utr3
8 CPOX human235678 utr3
sessionInfo()R version 3.6.1 (2019-07-05)
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_0.3.0 UpSetR_1.4.0 forcats_0.4.0 stringr_1.4.0
[5] dplyr_0.8.3 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[9] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] httr_1.4.1 jsonlite_1.6 carData_3.0-2
[4] modelr_0.1.8 assertthat_0.2.1 cellranger_1.1.0
[7] yaml_2.2.0 pillar_1.4.2 backports_1.1.4
[10] lattice_0.20-38 glue_1.3.1 digest_0.6.20
[13] RColorBrewer_1.1-2 promises_1.0.1 ggsignif_0.5.0
[16] rvest_0.3.5 colorspace_1.4-1 htmltools_0.3.6
[19] httpuv_1.5.1 plyr_1.8.4 pkgconfig_2.0.2
[22] broom_0.5.2 haven_2.3.1 scales_1.1.0
[25] whisker_0.3-2 openxlsx_4.1.0.1 later_0.8.0
[28] rio_0.5.16 git2r_0.26.1 generics_0.0.2
[31] farver_2.0.1 car_3.0-5 ellipsis_0.2.0.1
[34] withr_2.1.2 lazyeval_0.2.2 cli_1.1.0
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[40] evaluate_0.14 fs_1.3.1 fansi_0.4.0
[43] nlme_3.1-140 rstatix_0.5.0 xml2_1.3.2
[46] foreign_0.8-71 tools_3.6.1 data.table_1.12.8
[49] hms_0.5.3 lifecycle_0.1.0 munsell_0.5.0
[52] reprex_0.3.0 zip_2.0.3 compiler_3.6.1
[55] rlang_0.4.6 grid_3.6.1 rstudioapi_0.10
[58] labeling_0.3 rmarkdown_1.13 gtable_0.3.0
[61] abind_1.4-5 DBI_1.1.0 curl_3.3
[64] R6_2.4.0 gridExtra_2.3 lubridate_1.7.4
[67] knitr_1.23 utf8_1.1.4 workflowr_1.6.2
[70] rprojroot_1.3-2 stringi_1.4.3 Rcpp_1.0.3
[73] vctrs_0.3.0 dbplyr_1.4.2 tidyselect_1.1.0
[76] xfun_0.8