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#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)
Changes I’ve made so far: added annotations for the mutants that we didn’t otherwise see in the normal data. Added some annodations for the ENU experiment time point and spike in frequencies.
# ###In this section, all I'm doing is importing the twinstrand_maf_merge dataframe and annotating mutants. This is essentially copied over from the sections above but is present in the same chunks so that I don't have to go looking at chunks...
# twinstrand_maf=read.table("Twinstrand/prj00053-2019-12-02.deliverables/all.mut",sep="\t",header = T,stringsAsFactors = F)
# names=read.table("Twinstrand/prj00053-2019-12-02.deliverables/manifest.tsv",sep="\t",header = T,stringsAsFactors = F)
# twinstrand_maf_merge=merge(twinstrand_maf,names,by.x = "Sample",by.y = "TwinstrandId")
# ###These mutations include the mutations found in the normal data and ALSO mutants found only in the ENU data
# twinstrand_maf_merge=twinstrand_maf_merge%>%
# mutate(mutant=case_when(End==130872896 & ALT=="T" ~ "T315I",
# End==130862970 & ALT=="C" ~ "Y253H",
# End==130862977 & ALT=="T" ~ "E255V",
# End==130873004 & ALT=="C" ~ "M351T",
# End==130862962 & ALT=="A" ~ "G250E",
# End==130874969 & ALT=="C" ~ "H396P",
# End==130862955 & ALT=="G" ~ "L248V",
# End==130874969 & ALT=="G" ~ "H396R",
# End==130862971 & ALT=="T" ~ "Y253F",
# End==130862969 & ALT=="T" ~ "Q252H",
# End==130862976 & ALT=="A" ~ "E255K",
# End==130872901 & ALT=="C" ~ "F317L",
# End==130873027 & ALT=="C" ~ "F359L",
# End==130873027 & ALT=="G" ~ "F359V",
# End==130873027 & ALT=="A" ~ "F359I",
# End==130873016 & ALT=="G" ~ "E355G",
# End==130873016 & ALT=="C" ~ "E355A",
# End==130878519 & ALT=="A" ~ "E459K",
# End==130872911 & ALT=="G" ~ "Y320C",
# End==130872133 & ALT=="G" ~ "D276G",
# End==130862969 & ALT=="C" ~ "Q252H", ###The mutants below were found only in the ENU mutagenized pools
# End==130872885 & ALT=="G" ~ "F311L",
# End==130873028 & ALT=="G" ~ "F359C",
# End==130874971 & ALT=="C" ~ "A397P",
# End==130862854 & ALT=="G" ~ "H214R",
# End==130872146 & ALT=="C" ~ "V280syn",
# End==130872161 & ALT=="T" ~ "K285N",
# End==130872923 & ALT=="G" ~ "L324R",
# End==130872983 & ALT=="T" ~ "A344D"))
#
# twinstrand_maf_merge$totalcells=0
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D0"]=493000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D3"]=1295000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M3"&twinstrand_maf_merge$time_point=="D6"]=13600000
# ##########M5##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D0"]=588000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D3"]=1299000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M5"&twinstrand_maf_merge$time_point=="D6"]=11294000
# ##########M7##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D0"]=611000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D3"]=857000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M7"&twinstrand_maf_merge$time_point=="D6"]=14568000
# ##########M4##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D0"]=405000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D3"]=980000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M4"&twinstrand_maf_merge$time_point=="D6"]=1783000
# ##########M6##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D0"]=510000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D3"]=798000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="M6"&twinstrand_maf_merge$time_point=="D6"]=842000
# ##########ENU3##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D0"]=166000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D3"]=1282000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_3"&twinstrand_maf_merge$time_point=="D6"]=97200000
# ##########ENU4##########
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D0"]=316000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D3"]=1264000
# twinstrand_maf_merge$totalcells[twinstrand_maf_merge$experiment=="Enu_4"&twinstrand_maf_merge$time_point=="D6"]=40000000
#
#
# # a=twinstrand_maf_merge%>%select(Annotation,CustomerName,experiment,REF,ALT,Depth,AltDepth,mutant,time_point)%>%filter(grepl("ENU",Annotation,ignore.case = T),time_point%in%c("D0","D3","D6"))%>%arrange(desc(AltDepth))
#
#
#
# #Adding columns for experiment names, experiment frequencies, and time
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M3D0","M3D3","M3D6")]="M3"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M4D0","M4D3","M4D6")]="M4"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M5D0","M5D3","M5D6")]="M5"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M6D0","M6D3","M6D6")]="M6"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("M7D0","M7D3","M7D6")]="M7"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("Enu3_D3","Enu3_D6")]="Enu_3"
# twinstrand_maf_merge$experiment[twinstrand_maf_merge$CustomerName%in%c("Enu4_D0","Enu4_D3","Enu4_D6")]="Enu_4" ##Updated this line for ENU
#
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M3D0","M3D3","M3D6")]=1000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M4D0","M4D3","M4D6")]=5000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M5D0","M5D3","M5D6")]=1000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M6D0","M6D3","M6D6")]=5000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("M7D0","M7D3","M7D6")]=1000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("Enu3_D3","Enu3_D6")]=1000
# twinstrand_maf_merge$Spike_in_freq[twinstrand_maf_merge$CustomerName%in%c("Enu4_D0","Enu4_D3","Enu4_D6")]=1000 ##Updated this line for ENU
#
# twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D0","M6D0","Enu4_D0")]="D0"
# twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D3","M4D3","M5D3","M6D3","M7D3","Enu3_D3","Enu4_D3")]="D3"
# twinstrand_maf_merge$time_point[twinstrand_maf_merge$CustomerName%in%c("M3D6","M4D6","M5D6","M6D6","M7D6","Enu3_D6","Enu4_D6")]="D6"
#
#
# twinstrand_maf_merge=twinstrand_maf_merge%>%mutate(totalmutant=AltDepth/Depth*totalcells)
ggplot(twinstrand_maf_merge%>%filter(!mutant=="NA",!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 |
---|---|---|
d6a53d9 | haiderinam | 2020-04-02 |
#!!!
a=twinstrand_maf_merge%>%dplyr::select(Annotation,CustomerName,experiment,REF,ALT,Depth,AltDepth,mutant)%>%filter(grepl("ENU",Annotation,ignore.case = T),grepl("D0",Annotation,ignore.case = T),experiment=="Enu_4")%>%arrange(desc(AltDepth))
# ggplot(a,aes(x=mutant,y=))#!!!!!!!!Continuefromhere!
# b=twinstrand_maf_merge%>%select(Annotation,CustomerName,experiment,REF,ALT,Depth,AltDepth,mutant)%>%filter(grepl("M6D0",CustomerName,ignore.case = T))%>%arrange(desc(AltDepth))
plotly=ggplot(twinstrand_maf_merge%>%filter(grepl("ENU",Annotation,ignore.case = T),experiment=="Enu_4"),aes(x=time_point,y=AltDepth,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+cleanup
ggplotly(plotly)
sum(a$AltDepth)-20134 ###reads that were resistant clones.
[1] 2543
plotly=ggplot(twinstrand_maf_merge%>%filter(grepl("ENU",Annotation,ignore.case = T),experiment=="Enu_3"),aes(x=time_point,y=AltDepth,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+cleanup
ggplotly(plotly)
###The main thing I'm realizing from the ENU data is that the more frequent mutants DEFINITELY start to dominate during the latter days. Most resistant mutants have a lower depth of coverage later into the days. Only the mutants of highest resistance like T315I and G250E actually have an increase in MAF.
a=twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",Spike_in_freq==1000,experiment%in%c("Enu_3","Enu_4"))
plotly=ggplot(twinstrand_maf_merge%>%filter(tki_resistant_mutation=="True",!mutant=="NA",Spike_in_freq==1000,experiment%in%c("Enu_3","Enu_4")),aes(x=time_point,y=totalmutant,color=factor(experiment)))+geom_point()+facet_wrap(~mutant)+scale_y_continuous(trans = "log10")+cleanup
ggplotly(plotly)
Generating mean growth rate across mutants to see if that improves clinical abundance predictions. Short answer: no it doesn’t based on our current specs
a=twinstrand_simple_melt_merge%>%
mutate(netgr_obs=case_when(experiment=="M5"~netgr_obs+.015,
experiment%in%c("M3","M6","M5","M4","M7")~netgr_obs))
a_new=a%>%filter(experiment%in%c("M3"),duration%in%("d3d6"))
a_new=a_new%>%filter(!netgr_obs%in%NA)%>%
dplyr::select(mutant,netgr_obs)
# mean_netgr=twinstrand_simple_melt_merge%>%group_by(mutant)%>%summarize(netgr_pred=mean(netgr_obs,na.rm=T))
# a_new=mean_netgr
mean_netgr=twinstrand_simple_melt_merge%>%group_by(mutant)%>%summarize(netgr_mean=mean(netgr_obs,na.rm=T))
a_new=mean_netgr
Checking if ENU mutants follow expected IC50s
enu_plots=twinstrand_simple_melt_merge%>%filter(experiment%in%c("Enu_4","Enu_3"),duration%in%"d3d6")
#hardcoding adjustments to the growth rates
enu_plots$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))+geom_text()+facet_wrap(~experiment)+geom_abline()+cleanup
ggplotly(plotly)
plotly=ggplot(enu_plots,aes(x=netgr_pred,y=netgr_obs,label=mutant,fill=factor(experiment)))+geom_text()+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] 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