4_7_20 Update

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Last updated: 2020-04-07

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Knit directory: duplex_sequencing_screen/

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Tasks:

Comparison E255k GFP versus duplex sequencing

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 rows containing missing values (geom_path).

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

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

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

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
11647b8	haiderinam	2020-04-07

Dilutions down to 1:15000 expected versus recovered

twinstrand_maf_merge=twinstrand_maf_merge%>%
  mutate(Spike_in_freq=as.numeric(Spike_in_freq))%>%
  mutate(Spike_in_freq=case_when(experiment=="Enu_4"~2000,
                                 experiment==experiment~Spike_in_freq))%>%
  mutate(actualDepth=Depth*3)%>% #To account for 2 mouse 1 human reads
  mutate(expectedAltDepth=case_when(time_point=="D0"&Spike_in_freq==1000~Depth/1000,
                                    time_point=="D0"&Spike_in_freq==5000~Depth/5000,
                                    time_point=="D0"&Spike_in_freq==2000~Depth/2000,
                                    time_point==time_point~NaN))

a=twinstrand_maf_merge%>%filter(time_point=="D0",experiment%in%c("M3","M6")&tki_resistant_mutation=="True"|experiment%in%"Enu_4",!mutant=="NA",!mutant=="D276G",!mutant=="V280syn")%>%
  mutate(expectedAltDepth=case_when(experiment=="Enu_4"&mutant=="F311L"~expectedAltDepth,
                                   experiment=="Enu_4"&mutant=="T315I"~expectedAltDepth*55,
                                   experiment=="Enu_4"&mutant=="F317L"~expectedAltDepth*6,
                                   experiment=="Enu_4"&mutant=="E355G"~expectedAltDepth*3,
                                   experiment=="Enu_4"&mutant=="F359V"~expectedAltDepth*13,
                                   experiment=="Enu_4"&mutant=="F359C"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="H396P"~expectedAltDepth*17,
                                   experiment=="Enu_4"&mutant=="A397P"~expectedAltDepth*12,
                                   experiment=="Enu_4"&mutant=="Y253H"~expectedAltDepth*63,
                                   experiment=="Enu_4"&mutant=="Q252H"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="G250E"~expectedAltDepth*11,
                                   experiment=="Enu_4"&mutant=="L248V"~expectedAltDepth*6,
                                   experiment=="Enu_4"&mutant=="H214R"~expectedAltDepth*4,
                                   experiment=="Enu_4"&mutant=="K285N"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="L324R"~expectedAltDepth*7,
                                   mutant==mutant~expectedAltDepth))
ggplot(a%>%filter(AltDepth>1),aes(x=AltDepth/60000,y=expectedAltDepth/60000))+geom_point()+scale_y_continuous(trans="log10",limits=c(.00001,.05),name = "Predicted frequency")+scale_x_continuous(trans="log10",limits=c(.00001,.05),name="Measured frequency")+geom_abline()+cleanup+theme(legend.position = "none")

a=a%>%filter(AltDepth>1)
cor(a$AltDepth,a$expectedAltDepth)^2

[1] 0.9939285

Replicate 1 vs Replicate 2 measured in pool side by side

#Plotting SP 1000 and 5000
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=="M6"~netgr_obs+.03,
                                   experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M3","M5","M4","M7")~netgr_obs))
plotly=ggplot(a,aes(x=netgr_pred,y=netgr_obs,label=mutant,color=factor(experiment)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

a=a%>%filter(experiment%in%c("M3","M5"),!mutant%in%"D276G")
spikeins_cast=dcast(a,formula = mutant~experiment,value.var = "netgr_obs")
ggplot(spikeins_cast,aes(x=M3,y=M5,label=mutant))+
  geom_text()+
  cleanup+
  geom_abline()+
  scale_x_continuous(limits = c(0,.065),name="Observed growth rate (1:5,000 replicate)")+
  scale_y_continuous(limits = c(0,.065),name="Observed growth rate (1:15,000 replicate)")

cor(spikeins_cast$M3,spikeins_cast$M5)^2

[1] 0.78928

#Therefore, R^2= 0.79

ENU Replicate 1 vs Replicate 2

enu_plots=twinstrand_simple_melt_merge%>%filter(experiment%in%c("Enu_4","Enu_3"),duration%in%"d3d6",conc==0.8,!netgr_obs%in%NA)
#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

enu_cast=dcast(enu_plots,formula = mutant~experiment,value.var = "netgr_obs")

ggplot(enu_cast,aes(x=Enu_3,y=Enu_4,label=mutant))+
  geom_text()+
  cleanup+
  geom_abline()+
  scale_x_continuous(limits = c(0,.065),name="Observed growth rate (Mutagenesis Replicate 1)")+
  scale_y_continuous(limits = c(0,.065),name="Observed growth rate (Mutagenesis replicate 2)")

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

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

Version	Author	Date
11647b8	haiderinam	2020-04-07

cor(enu_cast$Enu_3,enu_cast$Enu_4)^2

[1] NA

#This has an R^2 of 0.97. i.e. Low replicate to replicate variability

ENU vs spike in

all_cast=merge(spikeins_cast,enu_cast,by="mutant",all.x=T)
ggplot(all_cast,aes(x=Enu_4,y=M3,label=mutant))+
  geom_text()+
  cleanup+
  geom_abline()+
  scale_x_continuous(limits = c(0,.065),name="Observed growth rate (Mutagenesis Replicate)")+
  scale_y_continuous(limits = c(0,.065),name="Observed growth rate (Spike-in replicate)")

Warning: Removed 12 rows containing missing values (geom_text).

c=all_cast%>%filter(!Enu_3%in%NA)
cor(c$Enu_3,c$M3)^2

[1] 0.5088264

#This shows a worse R^2 if 0.5

Everything sequencing-observed vs IC50s-predicted

# 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(duration%in%"d3d6",conc=="0.8",!mutant%in%"D276G")%>%
  mutate(netgr_obs=case_when(experiment=="M6"~netgr_obs+.03,
                             experiment=="M5"~netgr_obs+.015,
                             experiment=="Enu_3"~netgr_obs-0.011,
                             experiment%in%c("M3","M5","M4","M7","Enu_4")~netgr_obs))%>%
  mutate(condition=case_when(experiment%in%c("M3","M5","M7")~"1:5000 spike-in",
                             experiment%in%c("M4","M6")~"1:15000 spike-in",
                             experiment%in%c("Enu_3","Enu_4")~"mutagenized population"))

# 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))
a_sum=a%>%group_by(mutant,condition)%>%summarize(mean_netgr_pred=mean(netgr_pred),mean_netgr_obs=mean(netgr_obs),sd_netgr_obs=sd(netgr_obs))

a_sum=a_sum%>%filter(!mean_netgr_obs%in%NA,!mean_netgr_pred%in%NA)
cor(a_sum$mean_netgr_obs,a_sum$mean_netgr_pred,method = "pearson")

[1] 0.6953644

#Therefore, pearson's correlation between observed and predicted is 0.69
a_sum_woENU=a_sum%>%filter(!condition%in%"mutagenized population")
#Pearson's correlation does not improve when removing ENU readings
cor(a_sum_woENU$mean_netgr_obs,a_sum_woENU$mean_netgr_pred,method = "pearson")

[1] 0.6983554

#Pearson's correlation with our best spike-in: 0.82
c=a%>%filter(experiment=="M3")
cor(c$netgr_obs,c$netgr_pred,method="pearson")

[1] 0.8181379

ggplot(a_sum,aes(x=mean_netgr_pred,y=mean_netgr_obs,color=factor(condition)))+geom_errorbar(aes(ymin=mean_netgr_obs-sd_netgr_obs,ymax=mean_netgr_obs+sd_netgr_obs))+geom_point()+geom_abline()+cleanup+
  scale_x_continuous(name="IC50-Predicted Net Growth Rate")+
  scale_y_continuous(name="Pooled Observed Net Growth Rate")

#Other way of looking at observed vs predicted
plotly=ggplot(a,aes(x=netgr_pred,y=netgr_obs,label=mutant,color=factor(experiment)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)

Archive Does removing low count data improve results?

Session information

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] reshape2_1.4.3      plotly_4.9.1        dplyr_0.8.4        
 [4] boot_1.3-24         lme4_1.1-21         Matrix_1.2-18      
 [7] fitdistrplus_1.0-14 npsurv_0.4-0        lsei_1.2-0         
[10] survival_3.1-8      MASS_7.3-51.5       ggplot2_3.2.1      
[13] lmtest_0.9-37       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] plyr_1.8.5        lifecycle_0.1.0   stringr_1.4.0     munsell_0.5.0    
[21] gtable_0.3.0      htmlwidgets_1.5.1 evaluate_0.14     labeling_0.3     
[25] knitr_1.27        fastmap_1.0.1     crosstalk_1.0.0   httpuv_1.5.2     
[29] Rcpp_1.0.3        xtable_1.8-4      promises_1.1.0    scales_1.1.0     
[33] backports_1.1.5   jsonlite_1.6      mime_0.8          farver_2.0.3     
[37] fs_1.3.1          digest_0.6.23     stringi_1.4.5     shiny_1.4.0      
[41] grid_3.5.2        rprojroot_1.3-2   tools_3.5.2       magrittr_1.5     
[45] lazyeval_0.2.2    tibble_2.1.3      tidyr_1.0.2       crayon_1.3.4     
[49] whisker_0.4       pkgconfig_2.0.3   ellipsis_0.3.0    data.table_1.12.8
[53] httr_1.4.1        assertthat_0.2.1  minqa_1.2.4       rmarkdown_2.1    
[57] R6_2.4.1          nlme_3.1-143      git2r_0.26.1      compiler_3.5.2