Last updated: 2020-06-07
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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
ic50data=read.csv("data/heatmap_concat_data.csv",header = T,stringsAsFactors = F)
# ic50data=read.csv("../data/heatmap_concat_data.csv",header = T,stringsAsFactors = F)##Data Parsing– Dose Response Data
###Importing model with BCR-ABL mutant dose responses (Chuan’s data) ###Also applying the 2-parameter logistic. Upper and lower limits are fixed. May be good or bad.
#Deciding not to use nls() because it's a pain in the ...
#https://www.youtube.com/watch?v=aXpJE7IGiPY this has a nice overview of curve fitting
# library(dplyr)
# rm(list=ls())
####Getting effect of drug on growth rate####
ic50data=ic50data[c(1:10),]
ic50data_long=melt(ic50data,id.vars = "conc",variable.name = "species",value.name = "y")
#Removing useless mutants (for example keeping only maxipreps and removing low growth rate mutants)
ic50data_long=ic50data_long%>%filter(species%in%c("Wt","V299L_H","E355A","D276G_maxi","H396R","F317L","F359I","E459K","G250E","F359C","F359V","M351T","L248V","E355G_maxi","Q252H_maxi","Y253F","F486S_maxi","H396P_maxi","E255K","Y253H","T315I","E255V"))
#Making standardized names
ic50data_long$mutant=ic50data_long$species
ic50data_long=ic50data_long%>%
# filter(conc=="0.625")%>%
# filter(conc=="1.25")%>%
mutate(mutant=case_when(species=="F486S_maxi"~"F486S",
species=="H396P_maxi"~"H396P",
species=="Q252H_maxi"~"Q252H",
species=="E355G_maxi"~"E355G",
species=="D276G_maxi"~"D276G",
species=="V299L_H" ~ "V299L",
species==mutant ~as.character(mutant)))
# ic50data_long_625$species[order((ic50data_long_625$y),decreasing = T)]
#In the next step, I'm ordering mutants by decreasing resposne to the 625nM dose. Then I use this to change the levels of the species factor from more to less resistant. This helps with ggplot because now I can color the mutants with decreasing resistance
ic50data_long_625=ic50data_long%>%filter(conc==.625)
ic50data_long$species=factor(ic50data_long$species,levels = as.character(ic50data_long_625$species[order((ic50data_long_625$y),decreasing = T)]))
#Plotting the normalized dose response curves
getPalette = colorRampPalette(brewer.pal(9, "Spectral"))
plotly=ggplot(ic50data_long,aes(x=log(conc),y=y,color=factor(species)))+
facet_wrap(~factor(species))+
geom_line()+
geom_point()+
cleanup+
scale_color_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)########Four parameter logistic########
#Reference: https://journals.plos.org/plosone/article/file?type=supplementary&id=info:doi/10.1371/journal.pone.0146021.s001
#In short: For each dose in each species, get the response
# rm(list=ls())
ic50data_long_model=data.frame()
for (species_curr in sort(unique(ic50data_long$species))){
ic50data_species_specific=ic50data_long%>%filter(species==species_curr)
x=ic50data_species_specific$conc
y=ic50data_species_specific$y
#Next: Appproximating Response from dose (inverse of the prediction)
ic50.ll4=drm(y~conc,data=ic50data_long%>%filter(species==species_curr),fct=LL.3(fixed=c(NA,1,NA)))
b=coef(ic50.ll4)[1]
c=0
d=1
e=coef(ic50.ll4)[2]
###Getting predictions
ic50data_species_specific=ic50data_species_specific%>%group_by(conc)%>%mutate(y_model=c+((d-c)/(1+exp(b*(log(conc)-log(e))))))
ic50data_species_specific=data.frame(ic50data_species_specific) #idk why I have to end up doing this
ic50data_long_model=rbind(ic50data_long_model,ic50data_species_specific)
}
ic50data_long=ic50data_long_model
#In the next step, I'm ordering mutants by decreasing resposne to the 625nM dose. Then I use this to change the levels of the species factor from more to less resistant. This helps with ggplot because now I can color the mutants with decreasing resistance
ic50data_long_625=ic50data_long%>%filter(conc==.625)
ic50data_long$species=factor(ic50data_long$species,levels = as.character(ic50data_long_625$species[order((ic50data_long_625$y_model),decreasing = T)]))
#Adding drug effect
##########Changed this on 2/20. Using y from 4 parameter logistic rather than raw values
ic50data_long=ic50data_long%>%
# filter(!species=="Wt")%>%
mutate(drug_effect=-log(y_model)/72)
#Adding Net growth rate
ic50data_long$netgr_pred=.05-ic50data_long$drug_effect####Plotting modeled dose responses
getPalette = colorRampPalette(brewer.pal(9, "Spectral"))
plotly=ggplot(ic50data_long,aes(x=log(conc),color=factor(species)))+
facet_wrap(~factor(species))+
geom_line(aes(y=y_model))+
geom_point(aes(y=y))+
cleanup+
scale_color_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)plotly=ggplot(ic50data_long,aes(x=species,y=y_model))+
facet_wrap(~factor(conc))+
geom_col(aes(fill=factor(species)))+
cleanup+
scale_fill_manual(values = getPalette(length(unique(ic50data_long$species))))+
theme(axis.text = element_blank(),
axis.ticks = element_blank())
ggplotly(plotly)###Dose response curve fitting with 4-parameter logistic ####Second iteration: Get y_model for predefined concentration ranges of interest
conc.list=seq(.5,1.5,by=.1)
ic50.model.pred=data.frame(matrix(NA,nrow=length(conc.list)*length(unique(ic50data_long$species)),ncol=0))
for(species_curr in sort(unique(ic50data_long$mutant))){
ic50data_species_specific=ic50data_long%>%filter(mutant==species_curr)
#Next: Appproximating Response from dose (inverse of the prediction)
ic50.ll4=drm(y~conc,data=ic50data_species_specific,fct=LL.3(fixed=c(NA,1,NA)))
#Extracting coefficients
b=coef(ic50.ll4)[1]
c=0
d=1
e=coef(ic50.ll4)[2]
rm(ic50.model.pred.species.specific)
ic50.model.pred.species.specific=data.frame(matrix(NA,nrow=length(conc.list),ncol=0))
i=1
ic50.model.pred.species.specific$mutant=species_curr
#For loop for the unique concentrations
for(conc.curr in conc.list){
ic50.model.pred.species.specific$conc[i]=conc.curr
ic50.model.pred.species.specific$y_model[i]=c+((d-c)/(1+exp(b*(log(conc.curr)-log(e)))))
i=i+1
}
ic50.model.pred=rbind(ic50.model.pred,ic50.model.pred.species.specific)
}Warning in rm(ic50.model.pred.species.specific): object
'ic50.model.pred.species.specific' not found#Adding drug effect
ic50.model.pred=ic50.model.pred%>%
# filter(!mutant=="Wt")%>%
mutate(drug_effect=-log(y_model)/72)
#Adding Net growth rate
# ic50.model.pred$netgr_pred=.05-ic50.model.pred$drug_effect
ic50data_long=ic50.model.pred
ic50data_all_conc=ic50data_long####Changing the format of the IC50s dataframe so that it matches twinstrand data labeling etc
####Also converting dose response to expected change in growth rate ####This requires estimating a growth rate without drug. Note that I am using k=0.05 or 14 hours right now.
#Variables when making predictions:
#Your assumed fitness without drug
ic50data_long$netgr_pred=net_gr_wodrug-ic50data_long$drug_effect
#Your assumed concentration
ic50data_long=ic50data_long%>%filter(conc==conc_for_predictions) ###Can remove this filter if you wanna look at how well predictions would match up if there was a systematic difference in the concentrations Chuan used and you used in your IC50s
##########Changed this on 2/20. Using y from 4 parameter logistic rather than raw values
# ic50data_formerge=ic50data_long%>%filter(!species=="Wt")%>%mutate(drug_effect=-log(y)/72)
# ic50data_formerge=ic50data_long%>%filter(!species=="Wt")%>%mutate(drug_effect=-log(y_model)/72)head(ic50data_all_conc) mutant conc y_model drug_effect
1 D276G 0.5 0.22194952 0.02090702
2 D276G 0.6 0.17731828 0.02402512
3 D276G 0.7 0.14534373 0.02678686
4 D276G 0.8 0.12165238 0.02925816
5 D276G 0.9 0.10359142 0.03149029
6 D276G 1.0 0.08948725 0.03352304# write.csv(ic50data_all_conc,"ic50data_all_conc.csv")
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] 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_4.9.2.1 ggsignif_0.6.0 devtools_2.3.0
[7] usethis_1.6.1 RColorBrewer_1.1-2 reshape2_1.4.4
[10] ggplot2_3.3.0 doParallel_1.0.15 iterators_1.0.12
[13] foreach_1.5.0 dplyr_0.8.5 VennDiagram_1.6.20
[16] futile.logger_1.4.3 tictoc_1.0 knitr_1.28
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.4.1 httr_1.4.1 rprojroot_1.3-2
[4] tools_4.0.0 backports_1.1.7 R6_2.4.1
[7] lazyeval_0.2.2 colorspace_1.4-1 withr_2.2.0
[10] tidyselect_1.1.0 prettyunits_1.1.1 processx_3.4.2
[13] curl_4.3 compiler_4.0.0 git2r_0.27.1
[16] cli_2.0.2 formatR_1.7 sandwich_2.5-1
[19] desc_1.2.0 labeling_0.3 scales_1.1.1
[22] mvtnorm_1.1-0 callr_3.4.3 stringr_1.4.0
[25] digest_0.6.25 foreign_0.8-78 rmarkdown_2.1
[28] rio_0.5.16 pkgconfig_2.0.3 htmltools_0.4.0
[31] sessioninfo_1.1.1 plotrix_3.7-8 htmlwidgets_1.5.1
[34] rlang_0.4.6 readxl_1.3.1 zoo_1.8-8
[37] jsonlite_1.6.1 crosstalk_1.1.0.1 gtools_3.8.2
[40] zip_2.0.4 car_3.0-7 magrittr_1.5
[43] Matrix_1.2-18 Rcpp_1.0.4.6 munsell_0.5.0
[46] fansi_0.4.1 abind_1.4-5 lifecycle_0.2.0
[49] stringi_1.4.6 multcomp_1.4-13 whisker_0.4
[52] yaml_2.2.1 carData_3.0-3 pkgbuild_1.0.8
[55] plyr_1.8.6 promises_1.1.0 forcats_0.5.0
[58] crayon_1.3.4 lattice_0.20-41 splines_4.0.0
[61] haven_2.2.0 hms_0.5.3 ps_1.3.3
[64] pillar_1.4.4 codetools_0.2-16 pkgload_1.0.2
[67] futile.options_1.0.1 glue_1.4.1 evaluate_0.14
[70] lambda.r_1.2.4 data.table_1.12.8 remotes_2.1.1
[73] vctrs_0.3.0 httpuv_1.5.2 testthat_2.3.2
[76] cellranger_1.1.0 gtable_0.3.0 purrr_0.3.4
[79] tidyr_1.0.3 assertthat_0.2.1 xfun_0.13
[82] openxlsx_4.1.5 later_1.0.0 survival_3.1-12
[85] viridisLite_0.3.0 tibble_3.0.1 memoise_1.1.0
[88] TH.data_1.0-10 ellipsis_0.3.1