Last updated: 2020-06-02

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Rmd 2d4b7d0 haiderinam 2020-06-02 wflow_publish(“analysis/E255K_alphas_figure.Rmd”) 
# rm(list=ls())

conc_for_predictions=0.8
net_gr_wodrug=0.05

# 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)

# 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)

# 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

gfpdata=read.table("data/gfpenrichmentdata.csv",stringsAsFactors = F,header = T,sep=',')
# gfpdata=read.table("../data/gfpenrichmentdata.csv",stringsAsFactors = F,header = T,sep=',')


# ic50_heatmap=read.csv("../data/IC50HeatMap.csv",header = T,stringsAsFactors = F)
ic50_heatmap=read.csv("data/IC50HeatMap.csv",header = T,stringsAsFactors = F)

cleanup=theme_bw() +
  theme(plot.title = element_text(hjust=.5),
        panel.grid.major = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.background = element_blank(),
        axis.line = element_line(color = "black"),
        axis.text = element_text(face="bold",color="black",size="11"),
        text=element_text(size=11,face="bold"),
        axis.title=element_text(face="bold",size="11"))

This boxplot will compare the net growth rates of E255K observed across IC50s, FACs studies, and pooled studies

###########Netgr from pooled approach###########
pooled_data=twinstrand_simple_melt_merge%>%filter(mutant=="E255K",!netgr_obs%in%NA,duration%in%c("d0d3","d0d6"))%>%dplyr::select(mutant,experiment,netgr=netgr_obs,Spike_in_freq)%>%mutate(measurement="Pooled")
pooled_data=pooled_data%>%dplyr::select(measurement,netgr)
# pooled_data=twinstrand_simple_melt_merge%>%filter(mutant=="E255K",!netgr_obs%in%NA)
#Why does the GFP FACs data seem to have the same growth rates?

###########Netgr from IC50s###########
ic50_data=ic50_heatmap%>%filter(species=="E255K")%>%dplyr::select(mutant=species,y=X1.25)
ic50_data=ic50_data%>%mutate(alpha=-log(y)/72)
ic50_data=ic50_data%>%mutate(netgr=net_gr_wodrug-alpha,measurement="IC50s")
ic50_data=ic50_data%>%dplyr::select(measurement,netgr)

###########Netgr from FACs experiment###########
#This data originally sits in the FACs entries in the "Mixing Experiment 4, 8/20/18, directory on benchling"
#These are for both 1:1000 and 1:100 data. Growth rates looked similar at low frequencies with and without WT effects but there was a slowering of growth without WT spike-ins. We haven't focused on that here though
facs_data=data.frame(c(0.046483696,0.0450497374,0.0197837978,0.0437170827,0.04520188,0.0440885505))
colnames(facs_data)="netgr"
facs_data=facs_data%>%mutate(mutant="E255K",Spike_in_freq=c(1000,1000,1000,10000,10000,10000),measurement="FACS")
facs_data=facs_data%>%dplyr::select(measurement,netgr)


###########Plotting###########
###Please note that 625nM imatinib was used in all conditions
e255k_gr=rbind(facs_data,pooled_data,ic50_data)
ggplot(e255k_gr,aes(x=factor(measurement),y=netgr,fill=factor(measurement)))+geom_boxplot()+scale_fill_manual(values = c("#63A088","#1CCE16","#206A36"))+cleanup+scale_y_continuous(name="Net Grwoth Rate Observed")

ggplot(e255k_gr,aes(x=factor(measurement),y=log(2)/netgr,fill=factor(measurement)))+geom_boxplot()+scale_fill_manual(values = c("#63A088","#1CCE16","#206A36"))+cleanup+scale_y_continuous(limits=c(0,30),name="Doubling Time")
Warning: Removed 1 rows containing non-finite values (stat_boxplot).

a=twinstrand_simple_melt_merge%>%filter(mutant=="E255K",!netgr_obs%in%NA)

#There seems to be a bit of a signal with the growth rates from D0 to D3 and D3 to D6. D0D3 seems to be a little higher. Will look at that discrepancy later.
gfpdata$ttotal_sequenced=c(0,3,6)
gfpdata$xtotal_sequenced=c(129,3323,37023)
# gfpdata_simple=gfpdata%>%dplyr::select(ttotal_4,xtotal_4_e255k)
# e255k=twinstrand_maf_merge%>%filter(mutant=="E255K",experiment=="M3")

gfpplotlog=ggplot(gfpdata)+
  geom_line(aes(x=t_out_4,y=log10(x_out_4_e255k)),color="#1cce16",size=1.5)+geom_ribbon(aes(x = t_out_4_conintub,ymax=log10(x_out_4_e255k_ciub),ymin=log10(x_out_4_e255k_cilb)),fill="#1cce16",alpha=.3)+
  geom_line(aes(x=t_out_3,y=log10(x_out_3_e255k)),color="#206A36",size=1.5)+geom_ribbon(aes(x = t_out_3_conintub,ymax=log10(x_out_3_e255k_ciub),ymin=log10(x_out_3_e255k_cilb)),fill="#206A36",alpha=.3)+
  geom_point(aes(x=ttotal_4,y=log10(xtotal_4_e255k)),size=3)+
  geom_point(aes(x=ttotal_3,y=log10(xtotal_3_e255k)),size=3)+
  geom_point(aes(x=ttotal_sequenced,y=log10(xtotal_sequenced)),size=3,shape="square",color="blue")+
  theme_bw()+theme(plot.title = element_text(hjust=.5),text = element_text(size=24),axis.title = element_text(face="bold",size="19"),axis.text=element_text(face="bold",size="19"))+
  xlim(0,7)+
  scale_y_continuous(labels=parse(text = c("10^3","10^5")),limits = c(2,6.5),breaks = c(3,5))+
  # scale_y_continuous(labels = function(x) format(x, scientific = TRUE), limits = c(1e2,5e6),trans='log10',labels=parse(text = c("10^3","10^5"))+
  # scale_y_continuous(labels = parse(text = c("10^3","10^5")), limits = c(1e2,5e6),trans='log10')+
  ylab('Resistant Population')+
  xlab('Time (Days)')
gfpplotlog
Warning: Removed 13 row(s) containing missing values (geom_path).

Warning: Removed 13 row(s) containing missing values (geom_path).
Warning: Removed 40 rows containing missing values (geom_point).

Warning: Removed 40 rows containing missing values (geom_point).

Looking at the growth rate changes with dates

a=twinstrand_simple_melt_merge%>%filter(duration%in%c("d0d3","d3d6"),experiment%in%c("M3","M6"))
plotly=ggplot(a,aes(x=factor(mutant),y=netgr_obs,fill=duration))+geom_col(position="dodge",stat="identity")+facet_wrap(~experiment)
Warning: Ignoring unknown parameters: stat
ggplotly(plotly)
#Realizing that the signal I'm seeing here might be confounded by erroneous flow cytometry counts... 

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reshape2_1.4.4      plotly_4.9.2.1      dplyr_0.8.5        
 [4] boot_1.3-24         lme4_1.1-23         Matrix_1.2-18      
 [7] fitdistrplus_1.0-14 npsurv_0.4-0.1      lsei_1.2-0.1       
[10] survival_3.1-12     MASS_7.3-51.5       ggplot2_3.3.0      
[13] lmtest_0.9-37       zoo_1.8-8           workflowr_1.6.2    

loaded via a namespace (and not attached):
 [1] statmod_1.4.34    tidyselect_1.1.0  xfun_0.13         purrr_0.3.4      
 [5] splines_4.0.0     lattice_0.20-41   colorspace_1.4-1  vctrs_0.3.0      
 [9] viridisLite_0.3.0 htmltools_0.4.0   yaml_2.2.1        rlang_0.4.6      
[13] later_1.0.0       pillar_1.4.4      nloptr_1.2.2.1    glue_1.4.1       
[17] withr_2.2.0       plyr_1.8.6        lifecycle_0.2.0   stringr_1.4.0    
[21] munsell_0.5.0     gtable_0.3.0      htmlwidgets_1.5.1 evaluate_0.14    
[25] labeling_0.3      knitr_1.28        crosstalk_1.1.0.1 httpuv_1.5.2     
[29] Rcpp_1.0.4.6      promises_1.1.0    scales_1.1.1      backports_1.1.7  
[33] jsonlite_1.6.1    farver_2.0.3      fs_1.4.1          digest_0.6.25    
[37] stringi_1.4.6     grid_4.0.0        rprojroot_1.3-2   tools_4.0.0      
[41] magrittr_1.5      lazyeval_0.2.2    tibble_3.0.1      tidyr_1.0.3      
[45] crayon_1.3.4      whisker_0.4       pkgconfig_2.0.3   ellipsis_0.3.1   
[49] data.table_1.12.8 httr_1.4.1        assertthat_0.2.1  minqa_1.2.4      
[53] rmarkdown_2.1     R6_2.4.1          nlme_3.1-147      git2r_0.27.1     
[57] compiler_4.0.0