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#Inputs:
conc_for_predictions=0.8
net_gr_wodrug=0.05
getwd()[1] "/Users/haiderinam/Box/RProjects/duplex_sequencing_screen"#Reading required tables
ic50data=read.csv("data/heatmap_concat_data.csv",header = T,stringsAsFactors = F)
# ic50data=read.csv("../data/heatmap_concat_data.csv",header = T,stringsAsFactors = F)
twinstrand_maf=read.table("data/Twinstrand/prj00053-2019-12-02.deliverables/all.mut",sep="\t",header = T,stringsAsFactors = F)
# twinstrand_maf=read.table("../data/Twinstrand/prj00053-2019-12-02.deliverables/all.mut",sep="\t",header = T,stringsAsFactors = F)
names=read.table("data/Twinstrand/prj00053-2019-12-02.deliverables/manifest.tsv",sep="\t",header = T,stringsAsFactors = F)
# names=read.table("../data/Twinstrand/prj00053-2019-12-02.deliverables/manifest.tsv",sep="\t",header = T,stringsAsFactors = F)#Deciding not to use nls() because it's a pain in the ...
#https://www.youtube.com/watch?v=aXpJE7IGiPY this has a nice overview of curve fitting
# library(dplyr)
# rm(list=ls())
####Getting effect of drug on growth rate####
ic50data=ic50data[c(1:10),]
ic50data_long=melt(ic50data,id.vars = "conc",variable.name = "species",value.name = "y")
#Removing useless mutants (for example keeping only maxipreps and removing low growth rate mutants)
ic50data_long=ic50data_long%>%filter(species%in%c("Wt","V299L_H","E355A","D276G_maxi","H396R","F317L","F359I","E459K","G250E","F359C","F359V","M351T","L248V","E355G_maxi","Q252H_maxi","Y253F","F486S_maxi","H396P_maxi","E255K","Y253H","T315I","E255V"))
#Making standardized names
ic50data_long$mutant=ic50data_long$species
ic50data_long=ic50data_long%>%
# filter(conc=="0.625")%>%
# filter(conc=="1.25")%>%
mutate(mutant=case_when(species=="F486S_maxi"~"F486S",
species=="H396P_maxi"~"H396P",
species=="Q252H_maxi"~"Q252H",
species=="E355G_maxi"~"E355G",
species=="D276G_maxi"~"D276G",
species=="V299L_H" ~ "V299L",
species==mutant ~as.character(mutant)))
# ic50data_long_625$species[order((ic50data_long_625$y),decreasing = T)]
#In the next step, I'm ordering mutants by decreasing resposne to the 625nM dose. Then I use this to change the levels of the species factor from more to less resistant. This helps with ggplot because now I can color the mutants with decreasing resistance
ic50data_long_625=ic50data_long%>%filter(conc==.625)
ic50data_long$species=factor(ic50data_long$species,levels = as.character(ic50data_long_625$species[order((ic50data_long_625$y),decreasing = T)]))
#Plotting the normalized dose response curves
getPalette = colorRampPalette(brewer.pal(9, "Spectral"))
plotly=ggplot(ic50data_long,aes(x=log(conc),y=y,color=factor(species)))+
facet_wrap(~factor(species))+
geom_line()+
geom_point()+
cleanup+
scale_color_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)########Four parameter logistic########
#Reference: https://journals.plos.org/plosone/article/file?type=supplementary&id=info:doi/10.1371/journal.pone.0146021.s001
#In short: For each dose in each species, get the response
# rm(list=ls())
ic50data_long_model=data.frame()
for (species_curr in sort(unique(ic50data_long$species))){
ic50data_species_specific=ic50data_long%>%filter(species==species_curr)
x=ic50data_species_specific$conc
y=ic50data_species_specific$y
#Next: Appproximating Response from dose (inverse of the prediction)
ic50.ll4=drm(y~conc,data=ic50data_long%>%filter(species==species_curr),fct=LL.3(fixed=c(NA,1,NA)))
b=coef(ic50.ll4)[1]
c=0
d=1
e=coef(ic50.ll4)[2]
###Getting predictions
ic50data_species_specific=ic50data_species_specific%>%group_by(conc)%>%mutate(y_model=c+((d-c)/(1+exp(b*(log(conc)-log(e))))))
ic50data_species_specific=data.frame(ic50data_species_specific) #idk why I have to end up doing this
ic50data_long_model=rbind(ic50data_long_model,ic50data_species_specific)
}
ic50data_long=ic50data_long_model
#In the next step, I'm ordering mutants by decreasing resposne to the 625nM dose. Then I use this to change the levels of the species factor from more to less resistant. This helps with ggplot because now I can color the mutants with decreasing resistance
ic50data_long_625=ic50data_long%>%filter(conc==.625)
ic50data_long$species=factor(ic50data_long$species,levels = as.character(ic50data_long_625$species[order((ic50data_long_625$y_model),decreasing = T)]))
#Adding drug effect
##########Changed this on 2/20. Using y from 4 parameter logistic rather than raw values
ic50data_long=ic50data_long%>%
filter(!species=="Wt")%>%
mutate(drug_effect=-log(y_model)/72)
#Adding Net growth rate
ic50data_long$netgr_pred=.05-ic50data_long$drug_effectgetPalette = colorRampPalette(brewer.pal(9, "Spectral"))
plotly=ggplot(ic50data_long,aes(x=log(conc),color=factor(species)))+
facet_wrap(~factor(species))+
geom_line(aes(y=y_model))+
geom_point(aes(y=y))+
cleanup+
scale_color_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)plotly=ggplot(ic50data_long,aes(x=species,y=y_model))+
facet_wrap(~factor(conc))+
geom_col(aes(fill=factor(species)))+
cleanup+
scale_fill_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)conc.list=seq(.5,1.5,by=.1)
ic50.model.pred=data.frame(matrix(NA,nrow=length(conc.list)*length(unique(ic50data_long$species)),ncol=0))
for(species_curr in sort(unique(ic50data_long$mutant))){
ic50data_species_specific=ic50data_long%>%filter(mutant==species_curr)
#Next: Appproximating Response from dose (inverse of the prediction)
ic50.ll4=drm(y~conc,data=ic50data_species_specific,fct=LL.3(fixed=c(NA,1,NA)))
#Extracting coefficients
b=coef(ic50.ll4)[1]
c=0
d=1
e=coef(ic50.ll4)[2]
rm(ic50.model.pred.species.specific)
ic50.model.pred.species.specific=data.frame(matrix(NA,nrow=length(conc.list),ncol=0))
i=1
ic50.model.pred.species.specific$mutant=species_curr
#For loop for the unique concentrations
for(conc.curr in conc.list){
ic50.model.pred.species.specific$conc[i]=conc.curr
ic50.model.pred.species.specific$y_model[i]=c+((d-c)/(1+exp(b*(log(conc.curr)-log(e)))))
i=i+1
}
ic50.model.pred=rbind(ic50.model.pred,ic50.model.pred.species.specific)
}Warning in rm(ic50.model.pred.species.specific): object
'ic50.model.pred.species.specific' not found#Adding drug effect
ic50.model.pred=ic50.model.pred%>%
filter(!mutant=="Wt")%>%
mutate(drug_effect=-log(y_model)/72)
#Adding Net growth rate
# ic50.model.pred$netgr_pred=.05-ic50.model.pred$drug_effect
ic50data_long=ic50.model.pred
ic50data_all_conc=ic50data_long#Variables when making predictions:
#Your assumed fitness without drug
ic50data_long$netgr_pred=net_gr_wodrug-ic50data_long$drug_effect
#Your assumed concentration
ic50data_long=ic50data_long%>%filter(conc==conc_for_predictions) ###Can remove this filter if you wanna look at how well predictions would match up if there was a systematic difference in the concentrations Chuan used and you used in your IC50s
##########Changed this on 2/20. Using y from 4 parameter logistic rather than raw values
# ic50data_formerge=ic50data_long%>%filter(!species=="Wt")%>%mutate(drug_effect=-log(y)/72)
# ic50data_formerge=ic50data_long%>%filter(!species=="Wt")%>%mutate(drug_effect=-log(y_model)/72)Ideally, in the future I will use Biomart or a similar package that can do this automatically. Ideally, I’ll convert the fasta/bamh files into maf files myself Got residues and positions from here: #https://www.rcsb.org/pdb/chromosome.do?v=hg38&chromosome=chr9&pos=130862947 One thing that was tripping me up is that I was searching the database based on start position and not end-position This NCBI tool is also a good resource: https://www.ncbi.nlm.nih.gov/genome/gdv/browser/genome/?id=GCF_000001405.39 However, this is probably the best tool to go straight from genomic coordinate to protein change: https://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/DisaStr/GetPage.pl?varmap=TRUE
twinstrand_maf_merge=merge(twinstrand_maf,names,by.x = "Sample",by.y = "TwinstrandId")
#Of the 20 mutants, I don't see F486, F359C
twinstrand_maf_merge$mutant=0
twinstrand_maf_merge=twinstrand_maf_merge%>%
mutate(mutant=case_when(End==130872896 & ALT=="T" ~ "T315I",
End==130862970 & ALT=="C" ~ "Y253H",
End==130862977 & ALT=="T" ~ "E255V",
End==130873004 & ALT=="C" ~ "M351T",
End==130862962 & ALT=="A" ~ "G250E",
End==130874969 & ALT=="C" ~ "H396P",
End==130862955 & ALT=="G" ~ "L248V",
End==130874969 & ALT=="G" ~ "H396R",
End==130862971 & ALT=="T" ~ "Y253F",
End==130862969 & ALT=="T" ~ "Q252H",
End==130862976 & ALT=="A" ~ "E255K",
End==130872901 & ALT=="C" ~ "F317L",
End==130873027 & ALT=="C" ~ "F359L",
End==130873027 & ALT=="G" ~ "F359V",
End==130873027 & ALT=="A" ~ "F359I",
End==130873016 & ALT=="G" ~ "E355G",
End==130873016 & ALT=="C" ~ "E355A",
End==130878519 & ALT=="A" ~ "E459K",
End==130872911 & ALT=="G" ~ "Y320C",
End==130872133 & ALT=="G" ~ "D276G",
End==130862969 & ALT=="C" ~ "Q252Hsyn", ###The mutants below were found only in the ENU mutagenized pools
End==130872885 & ALT=="G" ~ "F311L",
End==130873028 & ALT=="G" ~ "F359C",
End==130874971 & ALT=="C" ~ "A397P",
End==130862854 & ALT=="G" ~ "H214R",
End==130872146 & ALT=="C" ~ "V280syn",
End==130872161 & ALT=="T" ~ "K285N",
End==130872923 & ALT=="G" ~ "L324R",
End==130872983 & ALT=="T" ~ "A344D")) #Not observed on D6. Dropped out! Note that D276G looked like it was contaminant DNA because it was barely at detection threshold at D0
#Ordering mutants by level of drug resistance. Note that since we don't know the level of DR for the unique ENU mutants, I have left them out here.
twinstrand_maf_merge$mutant=factor(twinstrand_maf_merge$mutant,levels = c("T315I","Y253H","E255V","M351T","G250E","H396P","L248V","H396R","Y253F","Q252H","E255K","F317L","F359L","F359V","F359I","E355G","E355A","E459K","Y320C","D276G","F311L","F359C","A397P","H214R","K285N","L324R","A344D"))
# twinstrand_maf_merge=twinstrand_maf_merge%>%
# mutate(mutant=case_when(End==130872896 & ALT=="T" ~ "T315I",
# End==130862970 & ALT=="C" ~ "Y253H",
# End==130862977 & ALT=="T" ~ "E255V",
# End==130873004 & ALT=="C" ~ "M351T",
# End==130862962 & ALT=="A" ~ "G250E",
# End==130874969 & ALT=="C" ~ "H396P",
# End==130862955 & ALT=="G" ~ "L248V",
# End==130874969 & ALT=="G" ~ "H396R",
# End==130862971 & ALT=="T" ~ "Y253F",
# End==130862969 & ALT=="T" ~ "Q252H",
# End==130862976 & ALT=="A" ~ "E255K",
# End==130872901 & ALT=="C" ~ "F317L",
# End==130873027 & ALT=="C" ~ "F359L",
# End==130873027 & ALT=="G" ~ "F359V",
# End==130873027 & ALT=="A" ~ "F359I",
# End==130873016 & ALT=="G" ~ "E355G",
# End==130873016 & ALT=="C" ~ "E355A",
# End==130878519 & ALT=="A" ~ "E459K",
# End==130872911 & ALT=="G" ~ "Y320C",
# End==130872133 & ALT=="G" ~ "D276G"))
#Adding columns for experiment names, experiment frequencies, and time
##############Experiment Name#################
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M3D0","M3D3","M3D6")]="M3"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M4D0","M4D3","M4D6")]="M4"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M5D0","M5D3","M5D6")]="M5"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M6D0","M6D3","M6D6")]="M6"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M7D0","M7D3","M7D6")]="M7"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("Enu3_D3","Enu3_D6")]="Enu_3"
twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("Enu4_D0","Enu4_D3","Enu4_D6")]="Enu_4"
##############Spike in frequency#################
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M3D0","M3D3","M3D6")]=1000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M4D0","M4D3","M4D6")]=5000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M5D0","M5D3","M5D6")]=1000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M6D0","M6D3","M6D6")]=5000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M7D0","M7D3","M7D6")]=1000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("Enu3_D3","Enu3_D6")]=1000
twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("Enu4_D0","Enu4_D3","Enu4_D6")]=1000
##############Time point#################
twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D0","M6D0","Enu4_D0")]="D0"
twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D3","M4D3","M5D3","M6D3","M7D3","Enu3_D3","Enu4_D3")]="D3"
twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D6","M4D6","M5D6","M6D6","M7D6","Enu3_D6","Enu4_D6")]="D6"#To start off converting MAFs into 'Total number of mutant cell' numbers, we will use only mixing experiment 3 as an example.
##########M3##########
twinstrand_maf_merge$totalcells=0
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D0"]=493000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D3"]=1295000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D6"]=13600000
##########M5##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D0"]=588000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D3"]=1299000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D6"]=11294000
##########M7##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D0"]=611000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D3"]=857000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D6"]=14568000
##########M4##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D0"]=405000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D3"]=980000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D6"]=1783000
##########M6##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D0"]=510000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D3"]=798000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D6"]=842000
##########ENU3##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D0"]=166000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D3"]=1282000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D6"]=97200000
##########ENU4##########
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D0"]=316000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D3"]=1264000
twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D6"]=40000000
########Converting MAF to Total Count##########
twinstrand_maf_merge=twinstrand_maf_merge%>%mutate(totalmutant=AltDepth/Depth*totalcells)detach("package:dplyr", character.only = TRUE)
library("dplyr", character.only = TRUE)
Attaching package: 'dplyr'The following object is masked from 'package:MASS':
selectThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, uniontwinstrand_simple=twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!is.na(mutant),!is.na(experiment))
twinstrand_simple=twinstrand_simple%>%select("mutant","experiment","Spike_in_freq","time_point","totalmutant")
twinstrand_simple_cast=dcast(twinstrand_simple,mutant+experiment+Spike_in_freq~time_point,value.var="totalmutant")
twinstrand_simple_cast$d0d3=log(twinstrand_simple_cast$D3/twinstrand_simple_cast$D0)/72
twinstrand_simple_cast$d3d6=log(twinstrand_simple_cast$D6/twinstrand_simple_cast$D3)/72
twinstrand_simple_cast$d0d6=log(twinstrand_simple_cast$D6/twinstrand_simple_cast$D0)/144
#Check if ln(final/initial)/time is the correct formula. Also notice how I'm using days not hours
twinstrand_simple_melt=melt(twinstrand_simple_cast[,-c(4:6)],id.vars=c("mutant","experiment","Spike_in_freq"),variable.name = "duration",value.name = "netgr_obs") #!!!!!!!!!!!value name should be drug effect. And drug effect should be drug_effect_obs i think. NO. I think this should be drug_effect_obs. Fixed 4/2/20
twinstrand_simple_melt$drug_effect_obs=net_gr_wodrug-twinstrand_simple_melt$netgr_obs
# twinstrand_simple_melt_merge=merge(twinstrand_simple_melt,ic50data_formerge,"mutant")
twinstrand_simple_melt_merge=merge(twinstrand_simple_melt,ic50data_long,"mutant")head(twinstrand_maf_merge) Sample Chromosome Start End VariationType REF ALT AltDepth
1 dna00762 chr9 130862900 130862905 indel CCCAA C 2
2 dna00762 chr9 130872157 130872159 indel GA G 1
3 dna00762 chr9 130872199 130872200 snv/snp G A 20665
4 dna00762 chr9 130872205 130872206 snv/snp G A 1
5 dna00762 chr9 130872205 130872206 snv/snp G T 1
6 dna00762 chr9 130872938 130872939 snv/snp G C 2
Depth tki_resistant_mutation tki_resistant_mutation_evidence CustomerName
1 27896 False BCR-Abl Wt
2 23301 False BCR-Abl Wt
3 20665 False BCR-Abl Wt
4 20982 False BCR-Abl Wt
5 20982 False BCR-Abl Wt
6 34493 False BCR-Abl Wt
Annotation mutant experiment Spike_in_freq
1 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
2 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
3 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
4 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
5 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
6 Wild type BCR-Abl Ba/F3- no spike in <NA> <NA> NA
time_point totalcells totalmutant
1 <NA> 0 0
2 <NA> 0 0
3 <NA> 0 0
4 <NA> 0 0
5 <NA> 0 0
6 <NA> 0 0head(twinstrand_simple_melt_merge) mutant experiment Spike_in_freq duration netgr_obs drug_effect_obs conc
1 D276G Enu_4 1000 d0d3 NA NA 0.8
2 D276G Enu_4 1000 d3d6 NA NA 0.8
3 D276G M3 1000 d0d3 0.01771526 0.03228474 0.8
4 D276G Enu_4 1000 d0d6 NA NA 0.8
5 D276G M3 1000 d0d6 NA NA 0.8
6 D276G M3 1000 d3d6 NA NA 0.8
y_model drug_effect netgr_pred
1 0.1216524 0.02925816 0.02074184
2 0.1216524 0.02925816 0.02074184
3 0.1216524 0.02925816 0.02074184
4 0.1216524 0.02925816 0.02074184
5 0.1216524 0.02925816 0.02074184
6 0.1216524 0.02925816 0.02074184# write.csv(twinstrand_maf_merge,"twinstrand_maf_merge.csv")
# write.csv(twinstrand_simple_melt_merge,"twinstrand_simple_melt_merge.csv")
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_0.8.4 drc_3.0-1 MASS_7.3-51.5
[4] BiocManager_1.30.10 plotly_4.9.1 ggsignif_0.6.0
[7] devtools_2.2.1 usethis_1.5.1 RColorBrewer_1.1-2
[10] reshape2_1.4.3 ggplot2_3.2.1 doParallel_1.0.15
[13] iterators_1.0.12 foreach_1.4.7 VennDiagram_1.6.20
[16] futile.logger_1.4.3 tictoc_1.0 knitr_1.27
[19] workflowr_1.6.0
loaded via a namespace (and not attached):
[1] fs_1.3.1 httr_1.4.1 rprojroot_1.3-2
[4] tools_3.5.2 backports_1.1.5 R6_2.4.1
[7] lazyeval_0.2.2 colorspace_1.4-1 withr_2.1.2
[10] tidyselect_1.0.0 prettyunits_1.1.1 processx_3.4.1
[13] curl_4.3 compiler_3.5.2 git2r_0.26.1
[16] cli_2.0.1 formatR_1.7 sandwich_2.5-1
[19] desc_1.2.0 labeling_0.3 scales_1.1.0
[22] mvtnorm_1.0-12 callr_3.4.1 stringr_1.4.0
[25] digest_0.6.23 foreign_0.8-75 rmarkdown_2.1
[28] rio_0.5.16 pkgconfig_2.0.3 htmltools_0.4.0
[31] sessioninfo_1.1.1 plotrix_3.7-7 fastmap_1.0.1
[34] htmlwidgets_1.5.1 rlang_0.4.4 readxl_1.3.1
[37] shiny_1.4.0 zoo_1.8-7 jsonlite_1.6
[40] crosstalk_1.0.0 gtools_3.8.1 zip_2.0.4
[43] car_3.0-6 magrittr_1.5 Matrix_1.2-18
[46] Rcpp_1.0.3 munsell_0.5.0 fansi_0.4.1
[49] abind_1.4-5 lifecycle_0.1.0 multcomp_1.4-12
[52] stringi_1.4.5 whisker_0.4 yaml_2.2.1
[55] carData_3.0-3 pkgbuild_1.0.6 plyr_1.8.5
[58] promises_1.1.0 forcats_0.4.0 crayon_1.3.4
[61] lattice_0.20-38 splines_3.5.2 haven_2.2.0
[64] hms_0.5.3 ps_1.3.0 pillar_1.4.3
[67] codetools_0.2-16 pkgload_1.0.2 futile.options_1.0.1
[70] glue_1.3.1 evaluate_0.14 lambda.r_1.2.4
[73] data.table_1.12.8 remotes_2.1.0 vctrs_0.2.2
[76] httpuv_1.5.2 testthat_2.3.1 cellranger_1.1.0
[79] gtable_0.3.0 purrr_0.3.3 tidyr_1.0.2
[82] assertthat_0.2.1 xfun_0.12 openxlsx_4.1.4
[85] mime_0.8 xtable_1.8-4 later_1.0.0
[88] survival_3.1-8 viridisLite_0.3.0 tibble_2.1.3
[91] memoise_1.1.0 TH.data_1.0-10 ellipsis_0.3.0