Last updated: 2020-04-03

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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("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)

# 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)

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 2 rows containing missing values (geom_text).

Version Author Date
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 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 2 rows containing non-finite values (stat_boxplot).

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 3 rows containing missing values (geom_text).

Version Author Date
0b9b87b haiderinam 2020-04-02 
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 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 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=="M6"~netgr_obs+.03,
                                   experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M3","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 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 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

Version Author Date
0b9b87b haiderinam 2020-04-02 
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

Version Author Date
0b9b87b haiderinam 2020-04-02 
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

Version Author Date
0b9b87b haiderinam 2020-04-02 
#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

Version Author Date
0b9b87b haiderinam 2020-04-02 
# 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"))

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] drc_3.0-1           MASS_7.3-51.5       BiocManager_1.30.10
 [4] plotly_4.9.1        ggsignif_0.6.0      devtools_2.2.1     
 [7] usethis_1.5.1       RColorBrewer_1.1-2  reshape2_1.4.3     
[10] ggplot2_3.2.1       doParallel_1.0.15   iterators_1.0.12   
[13] foreach_1.4.7       dplyr_0.8.4         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          farver_2.0.3         zoo_1.8-7           
[40] jsonlite_1.6         crosstalk_1.0.0      gtools_3.8.1        
[43] zip_2.0.4            car_3.0-6            magrittr_1.5        
[46] Matrix_1.2-18        Rcpp_1.0.3           munsell_0.5.0       
[49] fansi_0.4.1          abind_1.4-5          lifecycle_0.1.0     
[52] multcomp_1.4-12      stringi_1.4.5        whisker_0.4         
[55] yaml_2.2.1           carData_3.0-3        pkgbuild_1.0.6      
[58] plyr_1.8.5           promises_1.1.0       forcats_0.4.0       
[61] crayon_1.3.4         lattice_0.20-38      splines_3.5.2       
[64] haven_2.2.0          hms_0.5.3            ps_1.3.0            
[67] pillar_1.4.3         codetools_0.2-16     pkgload_1.0.2       
[70] futile.options_1.0.1 glue_1.3.1           evaluate_0.14       
[73] lambda.r_1.2.4       data.table_1.12.8    remotes_2.1.0       
[76] vctrs_0.2.2          httpuv_1.5.2         testthat_2.3.1      
[79] cellranger_1.1.0     gtable_0.3.0         purrr_0.3.3         
[82] tidyr_1.0.2          assertthat_0.2.1     xfun_0.12           
[85] openxlsx_4.1.4       mime_0.8             xtable_1.8-4        
[88] later_1.0.0          survival_3.1-8       viridisLite_0.3.0   
[91] tibble_2.1.3         memoise_1.1.0        TH.data_1.0-10      
[94] ellipsis_0.3.0