spikeins_depthofcoverages
Haider Inam
4/3/2020
Last updated: 2020-04-03
Checks: 7 0
Knit directory: duplex_sequencing_screen/
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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
# 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 Spike-ins and ENU concurrently for Justin’s grant
###Making the figure for Justin and Simon's grant
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
# plotly=ggplot(enu_plots,aes(x=netgr_pred,y=netgr_obs,label=mutant,fill=factor(experiment)))+geom_text()+geom_abline()+cleanup
# ggplotly(plotly)
#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)plotly=ggplot(a,aes(x=netgr_pred,y=netgr_obs,label=mutant,color=factor(experiment)))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)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))
plotly=ggplot(a_sum,aes(x=mean_netgr_pred,y=mean_netgr_obs,label=mutant))+geom_text()+geom_abline()+cleanup
ggplotly(plotly)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)###Making just D0 frequencies:
# a=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))
# 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))
#######
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))
###Please note that you're probably inadvertantly kicking out other mutants here
plotly=ggplot(a,aes(x=factor(experiment),y=AltDepth/Depth,color=mutant))+geom_point(position=position_dodge(.5))+scale_y_continuous(trans="log10")+cleanup
ggplotly(plotly)ggplot(a,aes(x=AltDepth,y=expectedAltDepth,color=experiment))+geom_point()+scale_y_continuous(trans="log10",limits = c(1,1000),name = "Predicted read count")+scale_x_continuous(trans="log10",limits = c(1,1000),name="Measured read count")+geom_abline()+cleanup+theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| 6af2cdc | haiderinam | 2020-04-03 |
ggsave("grant_fig.pdf",width = 3,height = 3,units = "in",useDingbats=F)
ggplot(a,aes(x=AltDepth,y=expectedAltDepth))+geom_point()+scale_y_continuous(trans="log10",limits = c(.2,2000),name = "Predicted read count",breaks=c(.2,2,20,200,2000))+scale_x_continuous(trans="log10",limits = c(.2,2000),name="Measured read count",breaks=c(.2,20,200,2000))+geom_abline()+cleanup+theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| 6af2cdc | haiderinam | 2020-04-03 |
ggsave("grant_fig_v2.pdf",width = 3,height = 3,units = "in",useDingbats=F)
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")
| Version | Author | Date |
|---|---|---|
| 6af2cdc | haiderinam | 2020-04-03 |
# ggsave("grant_fig_v2.pdf",width = 3,height = 3,units = "in",useDingbats=F)
###Calculating correlation coefficient
a=a%>%filter(AltDepth>1)
cor(a$AltDepth,a$expectedAltDepth)^2[1] 0.9939285# c=a_sum%>%filter(Spike_in_freq==1000)%>%select(mutant,mean_netgr_obs)%>%mutate(a=.055-mean_netgr_obs)%>%mutate(y=exp(-a*72))%>%select(!mean_netgr_obs)
# write.csv(c,"pooled_fitness_with_drug_1250nM.csv")
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