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Formatting the data to have mutant and observed net growth rate Also involves growth rate corrections

twinstrand_simple_melt_merge=twinstrand_simple_melt_merge%>%
  mutate(drug_effect_obs=case_when(experiment=="M5"~drug_effect_obs+.015,
                                   experiment=="Enu_3"~drug_effect_obs-.011,
                                   experiment%in%c("M3","M6","M5","M4","M7")~drug_effect_obs)) #Therefore including both spike-in experiments and ENU mutagenized conditions.

Predicting clinical abundance using a glm around mutation bias and netgr

#Using the mean of all experiments:
mean_netgr=twinstrand_simple_melt_merge%>%group_by(mutant)%>%summarize(netgr_mean=mean(netgr_obs,na.rm=T))

compicmut2=merge(compicmut,mean_netgr,by.x ="Compound",by.y="mutant")
# compicmut2$IC50=compicmut2$netgr
compicmut=compicmut2
#Notice that value here is your fitted IC50s
################Predictions using pooled IC50s################
fit5_pooled<-glm.nb(Abundance ~ netgr_mean+log10(Mutation.Probability), data=compicmut)
summary(fit5_pooled)

Call:
glm.nb(formula = Abundance ~ netgr_mean + log10(Mutation.Probability), 
    data = compicmut, init.theta = 8.795332235, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2054  -1.1023   0.2404   0.6654   1.3448  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                   4.7876     0.8235   5.813 6.12e-09 ***
netgr_mean                   15.2134     6.4144   2.372 0.017705 *  
log10(Mutation.Probability)   0.7783     0.2047   3.802 0.000144 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(8.7953) family taken to be 1)

    Null deviance: 49.106  on 17  degrees of freedom
Residual deviance: 19.964  on 15  degrees of freedom
AIC: 117.21

Number of Fisher Scoring iterations: 1

              Theta:  8.80 
          Std. Err.:  5.90 

 2 x log-likelihood:  -109.207 
Data_fit=data.frame(cbind(compicmut,fit5_pooled$fitted.values)) ###Need to check that doing a cbind this way makes the right mutants go to the right row in compicmut. Pretty sure it does.
x=ggplot(data =Data_fit,aes(fit5_pooled.fitted.values,Abundance))
x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_point(aes(size=1))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))

Version Author Date
c2930d5 haiderinam 2020-04-03 
# ggplot(compicmut,aes(x=IC50,y=(drug_effect_obs^.5),label=Compound))+geom_text()

################Predictions using individual IC50s################
fit5<-glm.nb(Abundance ~ IC50+log10(Mutation.Probability), data=compicmut) #Best 2 variable model
summary(fit5)

Call:
glm.nb(formula = Abundance ~ IC50 + log10(Mutation.Probability), 
    data = compicmut, init.theta = 8.493014642, link = log)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.88350  -0.86451   0.08648   0.42176   1.67167  

Coefficients:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 5.247e+00  7.707e-01   6.809 9.84e-12 ***
IC50                        1.045e-04  4.022e-05   2.598  0.00939 ** 
log10(Mutation.Probability) 8.310e-01  2.040e-01   4.074 4.62e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(8.493) family taken to be 1)

    Null deviance: 48.126  on 17  degrees of freedom
Residual deviance: 18.618  on 15  degrees of freedom
AIC: 116.2

Number of Fisher Scoring iterations: 1

              Theta:  8.49 
          Std. Err.:  5.17 

 2 x log-likelihood:  -108.201 
Data_fit=data.frame(cbind(Data_fit,fit5$fitted.values))
x=ggplot(data =Data_fit,aes(fit5.fitted.values,Abundance))
x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_point(aes(size=1))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))

Version Author Date
c2930d5 haiderinam 2020-04-03 
#########Plotting predictions from inidivual IC50s and pooled experiments on the same plane#########
x=ggplot(data =Data_fit,aes(fit5.fitted.values,Abundance,label=Compound))
plotly=x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_text(aes(size=1,color="blue"))+geom_text((aes(x=fit5_pooled.fitted.values,size=1.0,color="green")))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))
ggplotly(plotly)
####Pearson's correlations
cor(Data_fit$fit5_pooled.fitted.values,Data_fit$Abundance,method="pearson")
[1] 0.8805886
cor(Data_fit$fit5.fitted.values,Data_fit$Abundance,method="pearson")
[1] 0.8312331

Predicting clinical abundances using the best spike-in mix

#Using just the experiment that ended up giving us the best growth rates.
mean_netgr=twinstrand_simple_melt_merge%>%filter(experiment%in%c("M3"),duration%in%("d3d6"))
mean_netgr=mean_netgr%>%filter(!netgr_obs%in%NA)%>%
  select(mutant,netgr_obs)



compicmut2=merge(compicmut,mean_netgr,by.x ="Compound",by.y="mutant")
# compicmut2$IC50=compicmut2$netgr
compicmut=compicmut2
#Notice that value here is your fitted IC50s
################Predictions using pooled IC50s################
fit5_pooled<-glm.nb(Abundance ~ netgr_mean+log10(Mutation.Probability), data=compicmut)
summary(fit5_pooled)

Call:
glm.nb(formula = Abundance ~ netgr_mean + log10(Mutation.Probability), 
    data = compicmut, init.theta = 11.21444455, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2680  -0.5825   0.2918   0.5813   1.5319  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                   4.9595     0.7851   6.317 2.67e-10 ***
netgr_mean                   16.2201     6.7385   2.407   0.0161 *  
log10(Mutation.Probability)   0.8354     0.1962   4.258 2.07e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(11.2144) family taken to be 1)

    Null deviance: 49.929  on 15  degrees of freedom
Residual deviance: 18.420  on 13  degrees of freedom
AIC: 105.39

Number of Fisher Scoring iterations: 1

              Theta:  11.21 
          Std. Err.:  9.05 

 2 x log-likelihood:  -97.387 
Data_fit=data.frame(cbind(compicmut,fit5_pooled$fitted.values)) ###Need to check that doing a cbind this way makes the right mutants go to the right row in compicmut. Pretty sure it does.
x=ggplot(data =Data_fit,aes(fit5_pooled.fitted.values,Abundance))
x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_point(aes(size=1))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))

Version Author Date
c2930d5 haiderinam 2020-04-03 
# ggplot(compicmut,aes(x=IC50,y=(drug_effect_obs^.5),label=Compound))+geom_text()

################Predictions using individual IC50s################
fit5<-glm.nb(Abundance ~ IC50+log10(Mutation.Probability), data=compicmut) #Best 2 variable model
summary(fit5)

Call:
glm.nb(formula = Abundance ~ IC50 + log10(Mutation.Probability), 
    data = compicmut, init.theta = 9.531288067, link = log)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.06327  -0.56673   0.08711   0.34958   1.59965  

Coefficients:
                             Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 5.398e+00  7.590e-01   7.111 1.15e-12 ***
IC50                        9.498e-05  3.963e-05   2.397   0.0165 *  
log10(Mutation.Probability) 8.578e-01  2.026e-01   4.235 2.29e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(9.5313) family taken to be 1)

    Null deviance: 45.705  on 15  degrees of freedom
Residual deviance: 16.734  on 13  degrees of freedom
AIC: 105.04

Number of Fisher Scoring iterations: 1

              Theta:  9.53 
          Std. Err.:  6.35 

 2 x log-likelihood:  -97.039 
Data_fit=data.frame(cbind(Data_fit,fit5$fitted.values))
x=ggplot(data =Data_fit,aes(fit5.fitted.values,Abundance))
x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_point(aes(size=1))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))

Version Author Date
c2930d5 haiderinam 2020-04-03 
#########Plotting predictions from inidivual IC50s and pooled experiments on the same plane#########
x=ggplot(data =Data_fit,aes(fit5.fitted.values,Abundance,label=Compound))
plotly=x+stat_function(fun=function(x)x, geom="line", aes(colour="square",size=1.0))+geom_text(aes(size=1,color="blue"))+geom_text((aes(x=fit5_pooled.fitted.values,size=1.0,color="green")))+theme_classic()+theme(axis.text.x = element_text(size=15),axis.text.y = element_text(size=15))
ggplotly(plotly)
####Pearson's correlations
cor(Data_fit$fit5_pooled.fitted.values,Data_fit$Abundance,method="pearson")
[1] 0.8939072
cor(Data_fit$fit5.fitted.values,Data_fit$Abundance,method="pearson")
[1] 0.8379013

Supplemental Analysis

enu_plots=twinstrand_simple_melt_merge%>%filter(experiment%in%c("Enu_4","Enu_3"),duration%in%"d3d6")
#hardcoding adjustments to the growth rates
enu_plots$netgr_obs[enu_plots$experiment=="Enu_3"]=enu_plots$netgr_obs[enu_plots$experiment=="Enu_3"]-.011

a=twinstrand_simple_melt_merge%>%
  filter(!experiment%in%c("Enu_4","Enu_3"),duration%in%"d3d6",conc=="0.8")%>%
  mutate(netgr_obs=case_when(experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M3","M6","M5","M4","M7")~netgr_obs))

a_sum=a%>%group_by(mutant,Spike_in_freq)%>%summarize(mean_netgr_pred=mean(netgr_pred),mean_netgr_obs=mean(netgr_obs),sd_netgr_obs=sd(netgr_obs),mean_drug_effect=mean(drug_effect_obs),sd_drug_effect=sd(drug_effect_obs),mean_drug_effect_pred=mean(drug_effect),sd_drug_effect_pred=sd(drug_effect))

plotly=ggplot(a_sum,aes(x=mean_netgr_pred,y=mean_netgr_obs,color=factor(Spike_in_freq)))+geom_errorbar(aes(ymin=mean_netgr_obs-sd_netgr_obs,ymax=mean_netgr_obs+sd_netgr_obs))+geom_point()+geom_point(data=enu_plots%>%filter(experiment%in%("Enu_3")),aes(x=netgr_pred,y=netgr_obs,color="red"))+geom_abline()+cleanup
ggplotly(plotly)
####Plotting drug effect on growth rate rather than net growth rate
plotly=ggplot(a_sum,aes(x=mean_drug_effect_pred,y=mean_drug_effect,color=factor(Spike_in_freq)))+geom_errorbar(aes(ymin=mean_drug_effect-sd_drug_effect,ymax=mean_drug_effect+sd_drug_effect))+geom_point()+geom_abline()+cleanup
ggplotly(plotly)
plotly=ggplot(a_sum%>%filter(!Spike_in_freq%in%c(5000)),aes(x=mean_drug_effect_pred,y=mean_drug_effect,color=factor(Spike_in_freq)))+geom_errorbar(aes(ymin=mean_drug_effect-sd_drug_effect,ymax=mean_drug_effect+sd_drug_effect))+geom_point()+geom_abline()+cleanup
ggplotly(plotly)
plotly=ggplot(a_sum,aes(x=mean_drug_effect_pred,y=mean_drug_effect,color=factor(mutant)))+geom_errorbar(aes(ymin=mean_drug_effect-sd_drug_effect,ymax=mean_drug_effect+sd_drug_effect))+geom_point()+geom_abline()+cleanup
ggplotly(plotly)

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plotly_4.9.1        dplyr_0.8.4         boot_1.3-24        
 [4] lme4_1.1-21         Matrix_1.2-18       fitdistrplus_1.0-14
 [7] npsurv_0.4-0        lsei_1.2-0          survival_3.1-8     
[10] MASS_7.3-51.5       ggplot2_3.2.1       lmtest_0.9-37      
[13] zoo_1.8-7           workflowr_1.6.0    

loaded via a namespace (and not attached):
 [1] tidyselect_1.0.0  xfun_0.12         purrr_0.3.3       splines_3.5.2    
 [5] lattice_0.20-38   vctrs_0.2.2       colorspace_1.4-1  viridisLite_0.3.0
 [9] htmltools_0.4.0   yaml_2.2.1        rlang_0.4.4       later_1.0.0      
[13] pillar_1.4.3      nloptr_1.2.1      glue_1.3.1        withr_2.1.2      
[17] lifecycle_0.1.0   stringr_1.4.0     munsell_0.5.0     gtable_0.3.0     
[21] htmlwidgets_1.5.1 evaluate_0.14     labeling_0.3      knitr_1.27       
[25] fastmap_1.0.1     crosstalk_1.0.0   httpuv_1.5.2      Rcpp_1.0.3       
[29] xtable_1.8-4      promises_1.1.0    scales_1.1.0      backports_1.1.5  
[33] jsonlite_1.6      mime_0.8          farver_2.0.3      fs_1.3.1         
[37] digest_0.6.23     stringi_1.4.5     shiny_1.4.0       grid_3.5.2       
[41] rprojroot_1.3-2   tools_3.5.2       magrittr_1.5      lazyeval_0.2.2   
[45] tibble_2.1.3      tidyr_1.0.2       crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.3   ellipsis_0.3.0    data.table_1.12.8 httr_1.4.1       
[53] assertthat_0.2.1  minqa_1.2.4       rmarkdown_2.1     R6_2.4.1         
[57] nlme_3.1-143      git2r_0.26.1      compiler_3.5.2