Last updated: 2020-06-07
Checks: 7 0
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.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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.
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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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 491b602. 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:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: analysis/bcrabl_hill_ic50s.csv
Untracked: analysis/column_definitions_for_twinstrand_data_06062020.csv
Untracked: analysis/m351t_deviation.pdf
Untracked: analysis/multinomial_sims.Rmd
Untracked: analysis/pooled_growth_fig_cifrom4paramlogistic_060420.pdf
Untracked: analysis/pooled_growth_fig_cifromrawic50_060420.pdf
Untracked: analysis/simple_data_generation.Rmd
Untracked: analysis/twinstrand_growthrates_simple.csv
Untracked: analysis/twinstrand_maf_merge_simple.csv
Untracked: analysis/wildtype_growthrates_sequenced.csv
Untracked: clinicalabundancepredictions_BMES_abstract_51320.pdf
Untracked: data/Combined_data_frame_IC_Mutprob_abundance.csv
Untracked: data/IC50HeatMap.csv
Untracked: data/Twinstrand/
Untracked: data/gfpenrichmentdata.csv
Untracked: data/heatmap_concat_data.csv
Untracked: enrichment_simulations_3mutants.pdf
Untracked: output/archive/
Untracked: output/bmes_abstract_51220.pdf
Untracked: output/clinicalabundancepredictions_BMES_abstract_51320.pdf
Untracked: output/clinicalabundancepredictions_BMES_abstract_52020.pdf
Untracked: output/enrichment_simulations_3mutants_52020.pdf
Untracked: output/grant_fig.pdf
Untracked: output/grant_fig_v2.pdf
Untracked: output/grant_fig_v2updated.pdf
Untracked: output/ic50data_all_conc.csv
Untracked: shinyapp/
Unstaged changes:
Modified: analysis/E255K_alphas_figure.Rmd
Modified: analysis/clinical_abundance_predictions.Rmd
Modified: analysis/dosing_normalization.Rmd
Modified: analysis/index.Rmd
Modified: analysis/misc.Rmd
Modified: analysis/nonlinear_growth_analysis.Rmd
Modified: analysis/spikeins_depthofcoverages.Rmd
Deleted: data/README.md
Modified: output/twinstrand_maf_merge.csv
Modified: output/twinstrand_simple_melt_merge.csv
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.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/twinstrand_spikeins_data_generation.Rmd) and HTML (docs/twinstrand_spikeins_data_generation.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 491b602 | haiderinam | 2020-06-07 | wflow_publish(“analysis/twinstrand_spikeins_data_generation.Rmd”) |
| html | eaca616 | haiderinam | 2020-04-20 | Build site. |
| Rmd | 2bba93e | haiderinam | 2020-04-20 | wflow_publish("analysis/*.Rmd") |
| html | c2930d5 | haiderinam | 2020-04-03 | Build site. |
| html | 6af2cdc | haiderinam | 2020-04-03 | Build site. |
| Rmd | 5cda9d3 | haiderinam | 2020-04-03 | wflow_publish("analysis/*.Rmd") |
| html | 0b9b87b | haiderinam | 2020-04-02 | Build site. |
| Rmd | fc5b9c0 | haiderinam | 2020-04-02 | wflow_publish("analysis/*.Rmd") |
| html | 2bce927 | haiderinam | 2020-04-02 | Build site. |
| html | 4ed9b35 | haiderinam | 2020-04-02 | Build site. |
| html | 1e2f469 | haiderinam | 2020-04-02 | Build site. |
| Rmd | 50aa720 | haiderinam | 2020-04-02 | wflow_publish("analysis/*.Rmd") |
| html | e11eec5 | haiderinam | 2020-04-02 | Build site. |
| html | b1cbbfa | haiderinam | 2020-04-02 | Build site. |
| Rmd | 4d5a7ce | haiderinam | 2020-04-02 | wflow_publish("analysis/*.Rmd") |
####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_all_conc=read.csv("output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
# ic50data_all_conc=read.csv("../output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
ic50data_long=ic50data_all_conc%>%mutate(conc=="conc_for_predictions")
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)##Data Parsing– Duplex Sequencing Data ###Importing Twinstrand Mutation calls dataframe ####The twinstrand dataframe has sampleIDs. I’m merging this dataframe with a ‘names’ df that has details on what those sample IDs mean #####Here I also converted genomic coordinates and nucleotide changes to residue changes. I did all of our 20 spike-in mutants and others that I could find.
#####Other mutants included unique mutants found in the ENU data. i.e. A397P, F311L, F359C, H214R, H396P, K285N, L324R.
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$CustomerName%in%c("M3D6","M4D6","M5D6","M6D6","M7D6","Enu3_D6","Enu4_D6")]="D6"####Converting MAFs of all mutants to counts by using the flow cytometry count data for each experiment.
#To start off converting MAFs into 'Total number of mutant cell' numbers, we will use only mixing experiment 3 as an example.
##########M3##########
twinstrand_maf_merge$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"]=1959000
##########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$time_point=="D6"]=5457000
##########ENU3##########
twinstrand_maf_merge$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$time_point=="D6"]=40000000
########Converting MAF to Total Count##########
twinstrand_maf_merge=twinstrand_maf_merge%>%mutate(totalmutant=AltDepth/Depth*totalcells)####Deriving growthrates from twinstrand_maf_merge
detach("package:dplyr", character.only = TRUE)
library("dplyr", character.only = TRUE)
Attaching package: 'dplyr'The following object is masked from 'package:MASS':
selectThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, uniontwinstrand_simple=twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!is.na(mutant),!is.na(experiment))
twinstrand_simple=twinstrand_simple%>%dplyr::select("mutant","experiment","Spike_in_freq","time_point","totalmutant")
twinstrand_simple_cast=dcast(twinstrand_simple,mutant+experiment+Spike_in_freq~time_point,value.var="totalmutant")
twinstrand_simple_cast$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") #!!!!!!!!!!!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")
twinstrand_simple_melt_merge=merge(twinstrand_simple_melt,ic50data_long%>%filter(conc==conc_for_predictions),all.x = T)####Saving Dataframes
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 X conc
1 T315I M4 5000 d0d3 NA NA 191 0.8
2 T315I M5 1000 d0d3 NA NA 191 0.8
3 T315I M3 1000 d0d3 0.06165569 -0.011655692 191 0.8
4 T315I Enu_4 1000 d3d6 0.05375515 -0.003755150 191 0.8
5 T315I M3 1000 d3d6 0.05565321 -0.005653211 191 0.8
6 T315I M4 5000 d3d6 0.05776078 -0.007760782 191 0.8
y_model drug_effect conc == "conc_for_predictions"
1 0.8162648 0.002819673 FALSE
2 0.8162648 0.002819673 FALSE
3 0.8162648 0.002819673 FALSE
4 0.8162648 0.002819673 FALSE
5 0.8162648 0.002819673 FALSE
6 0.8162648 0.002819673 FALSE# write.csv(twinstrand_maf_merge,"twinstrand_maf_merge.csv")
# write.csv(twinstrand_simple_melt_merge,"twinstrand_simple_melt_merge.csv")
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
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] dplyr_0.8.5 drc_3.0-1 MASS_7.3-51.5
[4] BiocManager_1.30.10 plotly_4.9.2.1 ggsignif_0.6.0
[7] devtools_2.3.0 usethis_1.6.1 RColorBrewer_1.1-2
[10] reshape2_1.4.4 ggplot2_3.3.0 doParallel_1.0.15
[13] iterators_1.0.12 foreach_1.5.0 VennDiagram_1.6.20
[16] futile.logger_1.4.3 tictoc_1.0 knitr_1.28
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.4.1 httr_1.4.1 rprojroot_1.3-2
[4] tools_4.0.0 backports_1.1.7 R6_2.4.1
[7] lazyeval_0.2.2 colorspace_1.4-1 withr_2.2.0
[10] tidyselect_1.1.0 prettyunits_1.1.1 processx_3.4.2
[13] curl_4.3 compiler_4.0.0 git2r_0.27.1
[16] cli_2.0.2 formatR_1.7 sandwich_2.5-1
[19] desc_1.2.0 scales_1.1.1 mvtnorm_1.1-0
[22] callr_3.4.3 stringr_1.4.0 digest_0.6.25
[25] foreign_0.8-78 rmarkdown_2.1 rio_0.5.16
[28] pkgconfig_2.0.3 htmltools_0.4.0 sessioninfo_1.1.1
[31] plotrix_3.7-8 htmlwidgets_1.5.1 rlang_0.4.6
[34] readxl_1.3.1 zoo_1.8-8 jsonlite_1.6.1
[37] gtools_3.8.2 zip_2.0.4 car_3.0-7
[40] magrittr_1.5 Matrix_1.2-18 Rcpp_1.0.4.6
[43] munsell_0.5.0 fansi_0.4.1 abind_1.4-5
[46] lifecycle_0.2.0 stringi_1.4.6 multcomp_1.4-13
[49] whisker_0.4 yaml_2.2.1 carData_3.0-3
[52] pkgbuild_1.0.8 plyr_1.8.6 promises_1.1.0
[55] forcats_0.5.0 crayon_1.3.4 lattice_0.20-41
[58] splines_4.0.0 haven_2.2.0 hms_0.5.3
[61] ps_1.3.3 pillar_1.4.4 codetools_0.2-16
[64] pkgload_1.0.2 futile.options_1.0.1 glue_1.4.1
[67] evaluate_0.14 lambda.r_1.2.4 data.table_1.12.8
[70] remotes_2.1.1 vctrs_0.3.0 httpuv_1.5.2
[73] testthat_2.3.2 cellranger_1.1.0 gtable_0.3.0
[76] purrr_0.3.4 tidyr_1.0.3 assertthat_0.2.1
[79] xfun_0.13 openxlsx_4.1.5 later_1.0.0
[82] survival_3.1-12 viridisLite_0.3.0 tibble_3.0.1
[85] memoise_1.1.0 TH.data_1.0-10 ellipsis_0.3.1