growthrate_plots

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-09-02

Checks: 6 1

Knit directory: duplex_sequencing_screen/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200402)

The command set.seed(20200402) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: b998df6

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version b998df6. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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---

####Please change required directories this chunk if compiling in R rather than RmD

#Inputs:
conc_for_predictions=0.8
net_gr_wodrug=0.05
#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("output/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("output/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)

# twinstrand_maf_merge=read.csv("../output/twinstrand_maf_merge.csv",header = T,stringsAsFactors = F)
twinstrand_maf_merge=read.csv("output/twinstrand_maf_merge.csv",header = T,stringsAsFactors = F)

# twinstrand_simple_melt_merge=read.csv("../output/twinstrand_simple_melt_merge.csv",header = T,stringsAsFactors = F)
twinstrand_simple_melt_merge=read.csv("output/twinstrand_simple_melt_merge.csv",header = T,stringsAsFactors = F)

# ic50data_long=read.csv("../output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
ic50data_long=read.csv("output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
ic50data_long$netgr_pred=net_gr_wodrug-ic50data_long$drug_effect

###Plotting 1:1000 data

ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M5","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant),label=mutant))+geom_text()+geom_abline()+facet_wrap(~"experiment")+cleanup

Warning: Removed 8 rows containing missing values (geom_text).

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
86ce8ca	haiderinam	2022-08-22
eaca616	haiderinam	2020-04-20
0b9b87b	haiderinam	2020-04-02

plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M5","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(experiment),label=factor(mutant)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

#trying out the dotplot verison to show the individual datapoints
plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M5","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 6 rows containing missing values (stat_boxplot).

Warning: Removed 2 rows containing non-finite values (stat_boxplot).

###Replicate M5 seems to be a little underapproximative. Only M7 M3 look better.
plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 4 rows containing missing values (stat_boxplot).

Warning: Removed 2 rows containing non-finite values (stat_boxplot).

Analyzing the %age of reads used up by the most resistant mutants

t315=twinstrand_maf_merge%>%filter(mutant%in%c("T315I","Y253H","E255V","G250E"))%>%mutate(percentage=AltDepth*100/Depth)
wasted_reads=t315%>%group_by(experiment,time_point,mutant)%>%summarize(percentage=percentage)

`summarise()` has grouped output by 'experiment', 'time_point'. You can override using the `.groups` argument.

ggplot(wasted_reads%>%filter(!time_point%in%"D0"),aes(x=mutant,y=percentage,fill=mutant))+geom_col(position=position_dodge(1))+facet_grid(time_point~experiment)+cleanup+theme(axis.text.x=element_text(angle=90))

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Version	Author	Date
86ce8ca	haiderinam	2022-08-22
eaca616	haiderinam	2020-04-20
0b9b87b	haiderinam	2020-04-02

# ggsave("wasted_reads_bymutant.pdf",width=6,height=3,units="in")

wasted_reads_sum_top4=wasted_reads%>%group_by(experiment,time_point)%>%summarize(percentage=sum(percentage))

`summarise()` has grouped output by 'experiment'. You can override using the `.groups` argument.

ggplot(wasted_reads_sum_top4,aes(x=time_point,y=percentage,fill=time_point))+geom_col(position=position_dodge(1))+facet_wrap(~experiment)+cleanup+scale_y_continuous(name = "Percentage of reads taken \n up by top 4 mutants")

# ggsave("wasted_reads_total_top4.pdf",width=4,height=3,units="in")

wasted_reads_sum_top2=wasted_reads%>%filter(mutant%in%c("T315I","Y253H"))%>%group_by(experiment,time_point)%>%summarize(percentage=sum(percentage))

`summarise()` has grouped output by 'experiment'. You can override using the `.groups` argument.

ggplot(wasted_reads_sum_top2,aes(x=time_point,y=percentage,fill=time_point))+geom_col(position=position_dodge(1))+facet_wrap(~experiment)+cleanup+scale_y_continuous(name = "Percentage of reads taken \n up by top 2 mutants")

# ggsave("wasted_reads_total_top2.pdf",width=4,height=3,units="in")


######Looking at the scenario where the top 4 mutants weren't there in the ENU experiments:
###Percentage of wasted reads (top 4 mutants) for both enu experiments were ~60% for D3 and ~83% for D6
# a=twinstrand_maf_merge%>%filter(experiment%in%)
maf_without_wasted_reads=twinstrand_maf_merge%>%filter(!mutant%in%c(NA,"T315I","Y253H","G250E"),experiment%in%"Enu_4")%>%group_by(time_point)%>%mutate(Percentage_new=AltDepth*100/sum(AltDepth))
ggplot(maf_without_wasted_reads,aes(x=mutant,y=Percentage_new,fill=mutant))+geom_col(position=position_dodge(1))+facet_wrap(~time_point,ncol=1)+cleanup+theme(axis.text.x=element_text(angle=90))

# a=twinstrand_maf_merge%>%filter(!mutant%in%c(NA,"T315I","Y253H","G250E"),experiment%in%"Enu_4",time_point=="D0")
# sum(a$AltDepth)

maf_without_wasted_reads$mutant=factor(maf_without_wasted_reads$mutant,levels = as.character(unique(maf_without_wasted_reads$mutant[order((maf_without_wasted_reads$AltDepth),decreasing = T)])))

getPalette = colorRampPalette(brewer.pal(9, "Spectral"))


ggplot(maf_without_wasted_reads%>%filter(time_point=="D3"),aes(x="",y=Percentage_new,fill=mutant))+geom_bar(width=1,stat="identity")+cleanup+theme(axis.text.x=element_text(angle=90),axis.title = element_blank())+coord_polar("y",start=0)+scale_fill_manual(values = getPalette(length(unique(maf_without_wasted_reads$mutant))))+ggtitle("With Mutants Removed")

# ggsave("maf_piechart_new.pdf",width=4,height=4,units="in")




maf_with_wasted_reads=twinstrand_maf_merge%>%filter(experiment%in%"Enu_4",!mutant%in%NA)%>%group_by(time_point)%>%mutate(Percentage=AltDepth*100/sum(AltDepth))
# a=twinstrand_maf_merge%>%filter(experiment%in%"Enu_4",!mutant%in%NA,time_point=="D3")


maf_with_wasted_reads$mutant=factor(maf_with_wasted_reads$mutant,levels = as.character(unique(maf_with_wasted_reads$mutant[order((maf_with_wasted_reads$AltDepth),decreasing = T)])))

ggplot(maf_with_wasted_reads%>%filter(time_point=="D3"),aes(x="",y=Percentage,fill=mutant))+geom_bar(width=1,stat="identity")+cleanup+theme(axis.text.x=element_text(angle=90),axis.title = element_blank())+coord_polar("y",start=0)+scale_fill_manual(values = getPalette(length(unique(maf_with_wasted_reads$mutant))))+ggtitle("Without Removing Mutants")

# ggsave("maf_piechart_old.pdf",width=4,height=4,units="in")

maf_before_after=merge(maf_with_wasted_reads%>%filter(time_point=="D3")%>%ungroup()%>%dplyr::select(mutant,Percentage),maf_without_wasted_reads%>%filter(time_point=="D3")%>%ungroup()%>%dplyr::select(mutant,Percentage_new),by="mutant",all.x = T,all.y = T)
maf_before_after=maf_before_after%>%mutate(Percentage_new=case_when(Percentage_new%in%NA~0,
                                                     T~Percentage_new))
maf_before_after=maf_before_after%>%dplyr::select(mutant,NotSkipped=Percentage,Skipped=Percentage_new)
maf_before_after_melt=melt(maf_before_after,id.vars = "mutant",measure.vars = c("NotSkipped","Skipped"),variable.name = "Status",value.name = "Percentage")


ggplot(maf_before_after_melt%>%filter(!mutant%in%c("T315I","Y253H","G250E")),aes(x=mutant,y=Percentage,fill=Status))+geom_col(position=position_dodge(1))+cleanup+theme(axis.text.x = element_text(angle=90),legend.position = "bottom")

# ggsave("Residue_Skipping_Comparison.pdf",width=4,height=3,units="in")

twinstrand_maf=read.table("output/Twinstrand/prj00053-2019-12-02.deliverables/all.mut",sep="\t",header = T,stringsAsFactors = F)
# wt=twinstrand_maf_merge%>%filter(Sample%in%c("dna00762"))
wt=twinstrand_maf%>%filter(Sample%in%c("dna00762"))
# wt_snps=read.table("/Volumes/MyPassportforMac/duplexsequencing/twinstrand_september2020/wt2/variants_SNPs_filtered_sorted.tsv",header = F,stringsAsFactors = F)

# ggplot(wt_snps,aes(x=V1,y=V2*100/20000))+geom_col(width=3,position = position_dodge(1),fill="red")+cleanup+scale_x_continuous(name="Position in ABL1 Kinase Domain")+scale_y_continuous(name="Mutant Fraction %",limits=c(0,.15))+ggtitle("Minor Alleles Standard Sequencing")
# ggsave("wt_standardsequencing.pdf",width=3,height=2,units="in",useDingbats=F)

ggplot(twinstrand_maf%>%filter(Sample=="dna00762",!VariationType%in%c("indel"),!Start%in%c("130872199")),aes(x=Start,y=AltDepth*100/Depth))+geom_col(width=10,position=position_dodge(1),fill="red")+cleanup+scale_x_continuous(name="Position in ABL1 Kinase Domain")+scale_y_continuous(name="Mutant Fraction %",limits=c(0,.15))+ggtitle("Minor Alleles Duplex Sequencing")

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
86ce8ca	haiderinam	2022-08-22
0b9b87b	haiderinam	2020-04-02

# ggsave("wt_duplexconsensus.pdf",width=3,height=2,units="in",useDingbats=F)
# a=twinstrand_maf%>%filter(Sample=="dna00762",!VariationType%in%c("indel"))

ggplot(twinstrand_maf%>%filter(Sample=="dna00763",!VariationType%in%c("indel"),!Start%in%c("130872199")),aes(x=Start,y=AltDepth*100/Depth))+geom_col(width=10,position=position_dodge(1),fill="red")+cleanup+scale_x_continuous(name="Position in ABL1 Kinase Domain")+scale_y_continuous(name="Mutant Fraction %",limits=c(0,.15))+ggtitle("Minor Alleles Duplex Sequencing")

Warning: position_dodge requires non-overlapping x intervals

Warning: Removed 1 rows containing missing values (geom_col).

Past versions of unnamed-chunk-5-2.png

Version	Author	Date
86ce8ca	haiderinam	2022-08-22
0b9b87b	haiderinam	2020-04-02

# ggsave("m3d0_duplexconsensus.pdf",width=3,height=2,units="in",useDingbats=F)
# a=twinstrand_maf%>%filter(Sample=="dna00762",!VariationType%in%c("indel"))
# a=twinstrand_maf%>%filter(Sample=="dna00763",!VariationType%in%c("indel"),!Start%in%c("130872199"))

###Plotting 1 in 5,000

ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant),label=mutant))+geom_text()+geom_abline()+facet_wrap(~"experiment")+cleanup

Warning: Removed 13 rows containing missing values (geom_text).

plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(experiment),label=factor(mutant)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

#trying out the dotplot verison to show the individual datapoints
plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 10 rows containing missing values (stat_boxplot).

Warning: Removed 3 rows containing non-finite values (stat_boxplot).

#Removing M5 and M6 makes these look better. My assumption is that things were growing slower for the M5, M6 conditions because of culture conditions.
#This may look bad. But this also represents an opportunity: You don't need to estimate the approximate growth rate in the absence of drug because it is dynamic (changing culture conditions etc). You could use counts from a no-drug control but even that isn't the best control because of plate to plate variability in growth rates. You could just look at the most resistant mutants and see how fast they're growing. From their growth rates, you can back-calculate what the exact w/o drug growth rate is.
plotly=ggplot(twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3","M4","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 6 rows containing missing values (stat_boxplot).

Warning: Removed 2 rows containing non-finite values (stat_boxplot).

#As suggested above, I will modify the observed growth rate so that the w/o drug growth rate essentially derives from the observed growth rate of the most resistant cell line. Please note that I am doing this in a very roundabout way here because I am looking at the growth rate of T315I in the slow-growing cell lines, comparing it to how slow it is growing vs the mean growth rate of T315I from the other experimental days, and adding that difference in growth rate to that slow-growing experimental day.
# library(dplyr)
a=twinstrand_simple_melt_merge%>%
  mutate(netgr_obs=case_when(experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M3","M6","M5","M4","M7")~netgr_obs))

plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(experiment),label=factor(mutant)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

a_new=a%>%filter(experiment%in%c("M3"),duration%in%("d3d6"))
plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 10 rows containing missing values (stat_boxplot).

Warning: Removed 3 rows containing non-finite values (stat_boxplot).

plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 10 rows containing missing values (stat_boxplot).

Warning: Removed 3 rows containing non-finite values (stat_boxplot).

##Plotting 1 in 5000 data Note that this is somewhat similar to what twinstrand sent to us.

ggplot(twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",Spike_in_freq==1000,!experiment%in%c("Enu_3","Enu_4")),aes(x=time_point,y=AltDepth,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+cleanup

ggplot(twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",Spike_in_freq==1000,!experiment%in%c("Enu_3","Enu_4")),aes(x=time_point,y=totalmutant,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+scale_y_continuous(trans = "log10")+cleanup

ggplot(twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",Spike_in_freq==1000,!experiment%in%c("Enu_3","Enu_4")),aes(x=time_point,y=totalmutant,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+cleanup

#Combining 1 to 1,000 and 1 to 5,000
ggplot(twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",!experiment%in%c("Enu_3","Enu_4")),aes(x=time_point,y=totalmutant,color=factor(experiment),shape=factor(Spike_in_freq)))+geom_point()+facet_wrap(~mutant)+scale_y_continuous(trans = "log10")+cleanup

# Looking at why 1 in 5,000 mutants look a little undersampled
#Turns out they look different because they started off with 5x less cells. Duh.
# a=twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",Spike_in_freq=="5000",experiment=="M6",mutant%in%c("T315I","G250E"))

Looking at whether removing ANY variants with less than 5 ALT reads makes the correlation plots look better Answer: Yes it makes it better but mutants that defy predicitons are still there.

twinstrand_maf_merge=twinstrand_maf_merge%>%filter(!AltDepth<=5)

twinstrand_simple=twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!is.na(mutant),!is.na(experiment))
twinstrand_simple=twinstrand_simple%>%dplyr::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$D0)/144
#Check if ln(final/initial)/time is the correct formula. Also notice how I'm using days not hours
twinstrand_simple_melt=melt(twinstrand_simple_cast[,-c(4:6)],id.vars=c("mutant","experiment","Spike_in_freq"),variable.name = "duration",value.name = "netgr_obs") 
twinstrand_simple_melt$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")
twinstrand_simple_melt_merge=twinstrand_simple_melt_merge%>%filter(conc%in%0.8)

a=twinstrand_simple_melt_merge%>%
  mutate(netgr_obs=case_when(experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M3","M5","M6","M4","M7")~netgr_obs))


plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant)))+geom_boxplot(position=position_dodge(1))+geom_jitter(shape=16, position=position_jitter())+geom_abline()+cleanup
ggplotly(plotly)

Warning: Removed 8 rows containing non-finite values (stat_boxplot).

plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(mutant),label=mutant))+geom_text()+geom_abline()+facet_wrap(~"experiment")+cleanup
ggplotly(plotly)

Warning: `group_by_()` was deprecated in dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help

plotly=ggplot(a%>%filter(experiment%in%c("M3","M4","M5","M6","M7"),duration=="d3d6"),aes(x=netgr_pred,y=netgr_obs,color=factor(experiment),label=factor(mutant)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

names=read.table("output/Twinstrand/prj00053-2019-12-02.deliverables/manifest.tsv",sep="\t",header = T,stringsAsFactors = F)
twinstrand_maf_names=merge(twinstrand_maf,names,by.x="Sample",by.y="TwinstrandId")
a=twinstrand_maf_names%>%filter(tki_resistant_mutation=="True",CustomerName%in%c("M3D0","M3D3","M3D6"))

Session information

sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] drc_3.0-1           MASS_7.3-55         BiocManager_1.30.10
 [4] plotly_4.9.2.1      ggsignif_0.6.0      devtools_2.3.0     
 [7] usethis_1.6.1       RColorBrewer_1.1-2  reshape2_1.4.4     
[10] ggplot2_3.3.3       doParallel_1.0.15   iterators_1.0.12   
[13] foreach_1.5.0       dplyr_1.0.6         VennDiagram_1.6.20 
[16] futile.logger_1.4.3 workflowr_1.6.2     tictoc_1.0         
[19] knitr_1.28         

loaded via a namespace (and not attached):
 [1] fs_1.4.1             httr_1.4.2           rprojroot_1.3-2     
 [4] tools_4.0.0          backports_1.1.7      bslib_0.3.1         
 [7] utf8_1.1.4           R6_2.4.1             DBI_1.1.0           
[10] lazyeval_0.2.2       colorspace_1.4-1     withr_2.4.2         
[13] tidyselect_1.1.0     prettyunits_1.1.1    processx_3.5.2      
[16] curl_4.3             compiler_4.0.0       git2r_0.27.1        
[19] cli_2.5.0            formatR_1.7          sandwich_2.5-1      
[22] desc_1.2.0           labeling_0.3         sass_0.4.1          
[25] scales_1.1.1         mvtnorm_1.1-0        callr_3.7.0         
[28] stringr_1.4.0        digest_0.6.25        foreign_0.8-78      
[31] rmarkdown_2.14       rio_0.5.16           pkgconfig_2.0.3     
[34] htmltools_0.5.2      sessioninfo_1.1.1    plotrix_3.8-1       
[37] fastmap_1.1.0        readxl_1.3.1         htmlwidgets_1.5.1   
[40] rlang_0.4.11         farver_2.0.3         jquerylib_0.1.4     
[43] generics_0.0.2       zoo_1.8-8            jsonlite_1.7.2      
[46] crosstalk_1.1.0.1    gtools_3.8.2         zip_2.0.4           
[49] car_3.0-7            magrittr_2.0.1       Matrix_1.2-18       
[52] Rcpp_1.0.4.6         munsell_0.5.0        fansi_0.4.1         
[55] abind_1.4-5          lifecycle_1.0.0      multcomp_1.4-13     
[58] stringi_1.7.5        whisker_0.4          yaml_2.2.1          
[61] carData_3.0-3        pkgbuild_1.0.8       plyr_1.8.6          
[64] promises_1.1.0       forcats_0.5.1        crayon_1.4.1        
[67] lattice_0.20-41      splines_4.0.0        haven_2.4.1         
[70] hms_1.1.0            ps_1.3.3             pillar_1.6.1        
[73] codetools_0.2-16     pkgload_1.0.2        futile.options_1.0.1
[76] glue_1.4.1           evaluate_0.14        lambda.r_1.2.4      
[79] data.table_1.14.8    remotes_2.1.1        vctrs_0.3.8         
[82] httpuv_1.5.2         cellranger_1.1.0     testthat_2.3.2      
[85] gtable_0.3.0         purrr_0.3.4          tidyr_1.1.3         
[88] assertthat_0.2.1     xfun_0.31            openxlsx_4.1.5      
[91] later_1.0.0          survival_3.1-12      viridisLite_0.3.0   
[94] tibble_3.1.2         memoise_1.1.0        TH.data_1.0-10      
[97] ellipsis_0.3.2