ABL_SM_CRISPR_Cut

Last updated: 2023-08-30

Checks: 6 1

Knit directory: duplex_sequencing_screen/

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R Markdown file: uncommitted changes

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Environment: empty

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Seed: set.seed(20200402)

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Session information: recorded

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Cache: none

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File paths: relative

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Repository version: f9eea37

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The results in this page were generated with repository version f9eea37. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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    Untracked:  analysis/figure/
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Unstaged changes:
    Modified:   .DS_Store
    Modified:   analysis/.DS_Store
    Modified:   analysis/ABL_SM_CRISPR_Cut_Analyses.Rmd
    Modified:   analysis/ABL_cosmic_analysis.Rmd
    Modified:   analysis/ABL_kinasefunction_data_parser.Rmd
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    Modified:   analysis/BCRABL_FunctionalKinaseAnalysis.Rmd
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    Modified:   code/compare_screens.R
    Modified:   data/.DS_Store
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    Modified:   data/Consensus_Data/novogene_lane18/tlane18a_sample6/.DS_Store
    Modified:   output/.DS_Store
    Modified:   output/ABLEnrichmentScreens/.DS_Store
    Modified:   output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/.DS_Store
    Modified:   output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/.DS_Store

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ABL_SM_CRISPR_Cut_Analyses.Rmd) and HTML (docs/ABL_SM_CRISPR_Cut_Analyses.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	f9eea37	haiderinam	2023-08-30	2023 Updates
html	f9eea37	haiderinam	2023-08-30	2023 Updates
Rmd	f04026b	haiderinam	2023-04-18	added further ROC analyses of BE screens
Rmd	60b906b	haiderinam	2023-04-10	Added ROC analyses on BE-SM data
html	60b906b	haiderinam	2023-04-10	Added ROC analyses on BE-SM data
Rmd	88dabff	haiderinam	2023-04-01	Added lane 18 data of ABL Region 1
Rmd	6b51aa2	haiderinam	2023-03-25	Added Lane 18 Data with ABL Region 1 SM Screen

#Cleanup code for plotting
source("code/plotting/cleanup.R")
source("code/compare_screens.R")
source("code/plotting/heatmap_plotting_function.R")

source("code/variantcaller/add_l298l.R")
for(i in c(1:18)){
  sample=paste("sample",i,sep = "")
  # sample="sample1"
  
  input_df_nol298l=read.csv(paste("data/Consensus_Data/novogene_lane18/",sample,"/nol298l/duplex/variant_caller_outputs/variants_unique_ann.csv",sep=""))
  input_df_l298l=read.csv(paste("data/Consensus_Data/novogene_lane18/",sample,"/l298l/duplex/variant_caller_outputs/variants_unique_ann.csv",sep=""))
  
  output_df=add_l298l(input_df_nol298l,input_df_l298l)
  write.csv(output_df,
            paste("data/Consensus_Data/novogene_lane18/",sample,"/duplex/variant_caller_outputs/variants_unique_ann.csv",sep = ""))  
}

input_df_nol298l=read.csv("data/Consensus_Data/novogene_lane18/sample9/nol298l/duplex/variant_caller_outputs/variants_unique_ann.csv")
input_df_l298l=read.csv("data/Consensus_Data/novogene_lane18/sample9/l298l/duplex/variant_caller_outputs/variants_unique_ann.csv")
  
output_df=add_l298l(input_df_nol298l,input_df_l298l)
write.csv(output_df,"data/Consensus_Data/novogene_lane18/sample9/duplex/variant_caller_outputs/variants_unique_ann.csv")

# rm(list=ls())

comparisons=read.csv("data/Consensus_Data/novogene_lane18/TwistRegion1Screen_Comparisons_Todo.csv")
comparisons=comparisons%>%filter(Completed%in%"FALSE")

for(i in 1:nrow(comparisons)){
  # i=10
  dirname=comparisons$dirname[i]
  pathname=paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,sep = "")
  # Create directory if it doesn't already exist
  if (!file.exists(pathname)){
    dir.create(pathname)
} 
before_screen1_identifier=unlist(strsplit(comparisons$before_screen1_identifier[i],","))
after_screen1_identifier=unlist(strsplit(comparisons$after_screen1_identifier[i],","))
before_screen2_identifier=unlist(strsplit(comparisons$before_screen2_identifier[i],","))
after_screen2_identifier=unlist(strsplit(comparisons$after_screen2_identifier[i],","))
  # length(after_screen1_identifier)
# screen_compare_means=compare_screens(comparisons$before_screen1_identifier[i],
#                                      comparisons$after_screen1_identifier[i],
#                                      comparisons$before_screen2_identifier[i],
#                                      comparisons$after_screen2_identifier[i])
screen_compare_means=compare_screens(before_screen1_identifier,
                                     after_screen1_identifier,
                                     before_screen2_identifier,
                                     after_screen2_identifier)

screen_compare_means_forexport=apply(screen_compare_means,2,as.character)
# write.csv(screen_compare_means_forexport,file = paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/screen_comparison_",dirname,".csv",sep=""))

# Plot 1. What does the heatmap look like from the average of the net growth rate?
heatmap_plotting_function(screen_compare_means,242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot1_heatmap.pdf",sep=""),width=10,height=6,units="in",useDingbats=F)
# screen_compare_means2=screen_compare_means%>%filter(alt_codon%in%twist$Codon)

# Plot 2a: What do the correlations look like for net growth rate (show mutants in text)?
ggplot(screen_compare_means,aes(x=netgr_obs_screen1,y=netgr_obs_screen2,color=resmuts,label=species))+geom_text(size=2.5)+geom_abline()+cleanup+stat_cor(method = "pearson")+labs(color="Known\nResistant\nMutant")+scale_x_continuous("Net growth rate screen 1")+scale_y_continuous("Net growth rate screen 2")
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot2a_Netgrowthrate_correlations_text.pdf",sep=""),width=6,height=4,units="in",useDingbats=F)

# Plot 2b: What do the correlations look like for enrichment scores (show mutants in points)?
ggplot(screen_compare_means,aes(x=netgr_obs_screen1,y=netgr_obs_screen2,label=species))+geom_point(color="black",shape=21,size=2,aes(fill=resmuts))+geom_abline()+cleanup+stat_cor(method = "pearson")+labs(fill="Known\nResistant\nMutant")+scale_x_continuous("Net growth rate screen 1")+scale_y_continuous("Net growth rate screen 2")
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot2b_Netgrowthrate_correlations_points.pdf",sep=""),width=6,height=4,units="in",useDingbats=F)
# Plot 2c: What do the correlations look like for enrichment scores (show mutants in text)?
ggplot(screen_compare_means,aes(x=score_screen1,y=score_screen2,color=resmuts,label=species))+geom_text(size=2.5)+geom_abline()+cleanup+ stat_cor(method = "pearson")+labs(color="Known\nResistant\nMutant")+scale_x_continuous("Enrichment score screen 1")+scale_y_continuous("Enrichment score screen 2")
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot2c_Enrichmentscores_correlations_text.pdf",sep=""),width=6,height=4,units="in",useDingbats=F)

# Plot 3a: Plots: what are the overall net growth rate distributions?
ggplot(screen_compare_means,aes(x=netgr_obs_mean,fill=resmuts))+geom_density(alpha=0.7)+cleanup+labs(fill="Known\nResistant\nMutant")+scale_x_continuous("Mean net growth rate of screens")
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot3a_Netgrowthrate_distributions_resmuts.pdf",sep=""),width=6,height=4,units="in",useDingbats=F)
# Plot 3b: Plots: what are the net growth rate distributions?

library(reshape2)
screen_compare_melt=melt(screen_compare_means%>%dplyr::select(species,netgr_obs_screen1,netgr_obs_screen2),id.vars = "species",measure.vars =c("netgr_obs_screen1","netgr_obs_screen2"),variable.name = "Condition",value.name = "netgr_obs")


ggplot(screen_compare_melt,aes(x=netgr_obs,fill=Condition))+
  geom_density(alpha=0.7)+
  cleanup+
  scale_x_continuous("Net growth rate observed")+
  scale_fill_discrete(labels=c("Screen 1","Screen 2"))
# ggsave(paste("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/",dirname,"/plot3b_Netgrowthrate_distributions.pdf",sep=""),width=6,height=4,units="in",useDingbats=F)

}

# dirname="K562_Medium_rep1vs2"

Doing some quick analyses on buried hydrophobic vs exposed residues

Quick analysis to do on the secondary structure: The buried hydrophobic core is probably going to be more susceptible to polar/hydrophilic substitutions, and not as much to hydrophobic substitutions… do you see that in the il3 independence data? Answer: yes, I see a slight singal but it’s not huge

dssp=read.csv("data/DSSP_SolventAccessibility_ABL/2hyy_dspp.csv",header = T)
dssp=dssp%>%mutate(RESIDUE=as.numeric(RESIDUE),
                   ACC=as.numeric(ACC),AA=gsub("<ca>","",AA),
                   SS=case_when(STRUCTURE%in%c("E","B")~"b-sheet",
                                STRUCTURE%in%c("H","G","I")~"a-helix",
                                STRUCTURE%in%"T"~"turn",
                                T~"undefined"))%>%dplyr::select(-"STRUCTURE")

dssp=dssp%>%rename(protein_start=RESIDUE)

il3=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
il3=il3%>%dplyr::select(species,protein_start,netgr_obs_mean)
il3=il3%>%group_by(protein_start)%>%summarize(netgr_obs_mean=mean(netgr_obs_mean))

il3_dssp=merge(il3,dssp,by="protein_start")
il3_dssp=il3_dssp%>%mutate(exposed=case_when(ACC>=40~"Exposed",
                                            T~"Buried"))
# il3_dssp=il3_dssp%>%filter(exposed%in%"Exposed")
ggplot(il3_dssp,aes(x=ACC))+geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Version	Author	Date
f9eea37	haiderinam	2023-08-30
60b906b	haiderinam	2023-04-10

ggplot(il3_dssp,aes(x=protein_start,y=netgr_obs_mean,color=exposed))+
  geom_point()+
  facet_wrap(~exposed,nrow=2)+
  scale_y_continuous("Mean net growth rate at residue")+
  scale_x_continuous("Residue on the ABL Kinase")+
  labs(color="Solvent \nAccessibility")+
  cleanup+theme(legend.position = c(.9,.85))+
  theme(
  strip.background = element_blank(),
  strip.text.x = element_blank()
)+theme(legend.background = element_rect(
                                  size=0.5, linetype="solid", 
                                  colour ="black"))

Version	Author	Date
f9eea37	haiderinam	2023-08-30
60b906b	haiderinam	2023-04-10

# ggsave("data/DSSP_SolventAccessibility_ABL/il3solvent_accessibility_v1.pdf",width=6,height = 4,units="in",useDingbats=F)


ggplot(il3_dssp,aes(x=protein_start,y=netgr_obs_mean,color=exposed))+
  geom_point()+
  # facet_wrap(~exposed,nrow=2)+
  scale_y_continuous("Mean net growth rate at residue")+
  scale_x_continuous("Residue on the ABL Kinase")+
  labs(color="Solvent \nAccessibility")+cleanup

Version	Author	Date
f9eea37	haiderinam	2023-08-30
60b906b	haiderinam	2023-04-10

# ggsave("data/DSSP_SolventAccessibility_ABL/il3solvent_accessibility_v2.pdf",width=6,height = 4,units="in",useDingbats=F)


ggplot(il3_dssp,aes(x=ACC,y=netgr_obs_mean,color=exposed))+
  geom_point()+
  scale_y_continuous("Mean net growth rate at residue")+
  scale_x_continuous("DSSP Solvent Accessibility at Residue")+
  cleanup+
  labs(color="Solvent \nAccessibility")+
  theme(legend.position = c(.9,.25))+
  theme(legend.background = element_rect(
                                  size=0.5, linetype="solid", 
                                  colour ="black"))

Version	Author	Date
f9eea37	haiderinam	2023-08-30
60b906b	haiderinam	2023-04-10

# ggsave("data/DSSP_SolventAccessibility_ABL/il3solvent_accessibility_v3.pdf",width=6,height = 4,units="in",useDingbats=F)



il3=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
il3=il3%>%dplyr::select(species,ref_aa,protein_start,alt_aa,netgr_obs_mean)


il3_dssp=merge(il3,dssp,by.x=c("protein_start","ref_aa"),by.y=c("protein_start","AA"))
il3_dssp=il3_dssp%>%mutate(exposed=case_when(ACC>=40~"Exposed",
                                            T~"Buried"))

# il3_dssp=il3_dssp%>%mutate(hydrophobic=case_when(alt_aa%in%c("A","V","I","M","L","F","Y","W")~"hydrophobic",
#                                                  T~"other"))
ggplot(il3_dssp,aes(x=alt_aa,y=netgr_obs_mean))+geom_boxplot()+facet_wrap(~exposed)

ggplot(il3_dssp,aes(x=exposed,y=netgr_obs_mean))+geom_boxplot()+facet_wrap(~exposed)

ggplot(il3_dssp,aes(x=factor(protein_start),y=netgr_obs_mean,fill=exposed))+
  geom_boxplot()+
  # facet_wrap(~exposed,nrow=2)+
  scale_y_continuous("Mean net growth rate at residue")+
  scale_x_discrete("Residue on the ABL Kinase")+
  labs(fill="Solvent \nAccessibility")+cleanup+
  theme(axis.text.x=element_text(angle=90, hjust=1))

Doing some quick analyses on buried hydrophobic vs exposed residues

Doing some quick DDG analysis

ddg=read.table("data/DDG_ABL/2hyy_ddg.tsv")
colnames(ddg)=c("species","ddg","ddg_sd")
ddg=ddg%>%mutate(protein_start=234+as.numeric(gsub("[^0-9]","",species)),
                 ref_aa=substr(species,1,1),
                 alt_aa=sub(".+(.)$", "\\1", species),
                 species=paste(ref_aa,protein_start,alt_aa,sep = ""))
# class(ddg$protein_start)
ddg=ddg%>%filter(protein_start%in%c(242:322))

il3=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
il3=il3%>%select(species,protein_start,netgr_obs_mean)
ddgil3=merge(ddg,il3,by=c("species","protein_start"))
ggplot(ddgil3,aes(x=ddg,y=netgr_obs_mean))+geom_point()

ggplot(ddgil3,aes(x=ddg,y=netgr_obs_mean,label=species))+geom_text()

Comparing Fitness Scores with IC50 predictions

# ,include=F,eval=F
source("code/resmuts_adder.R")
ic50data_all_sum=read.csv("output/ic50data_all_confidence_intervals_raw_data.csv",row.names = 1)
imat=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2_ft/screen_comparison_baf3_Imat_low_rep1vsrep2_ft.csv")
# imat=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_Imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv")
imat=imat%>%select(species,ref_aa,protein_start,alt_aa,netgr_obs_mean)
# x=imat%>%filter(species%in%"V299L")
imat=resmuts_adder(imat)
resmuts_merged=merge(imat%>%filter(resmuts%in%"TRUE"),ic50data_all_sum%>%dplyr::select(species,netgr_pred_model,netgr_pred_mean),by="species")


ggplot(resmuts_merged,aes(x=netgr_pred_mean,y=netgr_obs_mean,label=species))+geom_text()+theme_bw()+geom_abline()

#######################Making Correlation Plots for Paper################

###########Cross Species Correlations###########
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-species/baf3_Imat_Lowvsk562_Imat_Medium/screen_comparison_baf3_Imat_Lowvsk562_Imat_Medium.csv",header = T,stringsAsFactors = F)
source("code/res_residues_adder.R")
source("code/cosmic_data_adder.R")
# data=read.csv("output/ABLEnrichmentScreens/Imat_Enrichment_bgmerged_2.22.23.csv",header = T,stringsAsFactors = F)
# class(data)
data=res_residues_adder(data)
data=cosmic_data_adder(data)
# data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs.x,netgr_obs.y))
data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
data$resmut_cosmic="neither"
data[data$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
data[data$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data$resmut_cosmic=factor(data$resmut_cosmic,levels=c("neither","cosmic","resmut"))

ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+
  geom_point(color="black",shape=21,size=.75,aes(fill=resmuts))+
  # geom_abline()+
  scale_x_continuous(name="Net Growth Rate Baf3")+
  scale_y_continuous(name="Net Growth Rate K562")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","red"))+
  cleanup+theme(legend.position = "none")+
  stat_cor(method="pearson")

# ggsave("output/SM_Imatinib_Plots/Baf3vsK562_ImatinibEnrichment_Plot_Medium_4.13.23.pdf",width=3,height=3,units="in",useDingbats=F)

###########K562s###########
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
source("code/res_residues_adder.R")
source("code/cosmic_data_adder.R")
# data=read.csv("output/ABLEnrichmentScreens/Imat_Enrichment_bgmerged_2.22.23.csv",header = T,stringsAsFactors = F)
# class(data)
data=res_residues_adder(data)
data=cosmic_data_adder(data)
# data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs.x,netgr_obs.y))
data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
data$resmut_cosmic="neither"
data[data$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
data[data$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data$resmut_cosmic=factor(data$resmut_cosmic,levels=c("neither","cosmic","resmut"))

# !ct_screen1_after%in%.5,!ct_screen2_after%in%.5,
ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+
  geom_point(color="black",shape=21,size=.75,aes(fill=resmuts))+
  geom_abline()+
  scale_x_continuous(name="Net Growth Rate Rep 1")+
  scale_y_continuous(name="Net Growth Rate Rep 2")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","red"))+
  cleanup+theme(legend.position = "none")+
  stat_cor(method="pearson")

# ggsave("output/SM_Imatinib_Plots/K562_ImatinibEnrichment_Plot_Medium_4.13.23.pdf",width=3,height=3,units="in",useDingbats=F)

ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=netgr_obs_mean,fill=resmuts))+
  geom_density(alpha=.7)+scale_x_continuous(bquote('Net Growth Rate '(Hours^-1)))+scale_y_continuous("Density")+scale_fill_manual(values=c("gray","red"))+cleanup+theme(legend.position = "none")

# ggsave("output/SM_Imatinib_Plots/K562_Density_Imat_netgr_4.19.23.pdf",width=4,height=3,units="in",useDingbats=F)

ggplot(data%>%filter(!species%in%"V299L",protein_start>=242,protein_start<=322,n_nuc_min%in%1),aes(x=resmut_cosmic,y=netgr_mean,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  geom_jitter(color="black", size=1,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Sanger","Known\nResistant"))+cleanup+theme(legend.position = "none")

# ggsave("output/SM_Imatinib_Plots/ImatinibEnrichment_netgr_boxplot_k562s_4.21.23.pdf",width=3,height=3,units="in",useDingbats=F)
###########Baf3s###########

# data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2_ft/screen_comparison_baf3_Imat_low_rep1vsrep2_ft.csv",header = T,stringsAsFactors = F)
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_high_rep1vsrep2_ft/screen_comparison_baf3_Imat_high_rep1vsrep2_ft.csv",header = T,stringsAsFactors = F)
# data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
source("code/res_residues_adder.R")
source("code/cosmic_data_adder.R")
# data=read.csv("output/ABLEnrichmentScreens/Imat_Enrichment_bgmerged_2.22.23.csv",header = T,stringsAsFactors = F)
# class(data)
data=res_residues_adder(data)
data=cosmic_data_adder(data)
# data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs.x,netgr_obs.y))
data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
data$resmut_cosmic="neither"
data[data$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
data[data$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data$resmut_cosmic=factor(data$resmut_cosmic,levels=c("neither","cosmic","resmut"))

# !ct_screen1_after%in%.5,!ct_screen2_after%in%.5,
ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+
  geom_point(color="black",shape=21,size=.75,aes(fill=resmuts))+
  geom_abline()+
  scale_x_continuous(name="Net Growth Rate Rep 1")+
  scale_y_continuous(name="Net Growth Rate Rep 2")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","red"))+
  cleanup+theme(legend.position = "none")+
  stat_cor(method="pearson")

# ggsave("output/SM_Imatinib_Plots/BaF3_ImatinibEnrichment_Plot_Medium_4.13.23.pdf",width=3,height=3,units="in",useDingbats=F)

ggplot(data%>%filter(!species%in%"V299L",protein_start>=242,protein_start<=322),aes(x=netgr_obs_mean,fill=resmuts))+
  geom_density(alpha=.7)+scale_x_continuous(bquote('Net Growth Rate '(Hours^-1)))+scale_y_continuous("Density")+scale_fill_manual(values=c("gray","red"))+cleanup+theme(legend.position = "none")

# ggsave("output/SM_Imatinib_Plots/Baf3_Density_Imat_netgr_4.19.23.pdf",width=4,height=3,units="in",useDingbats=F)





ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-netgr_obs_mean),y=netgr_obs_mean,fill=resmut_cosmic))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name="Enrichcment Score")+scale_x_discrete(name="Mutant")+scale_fill_manual(name="Resistance Status",labels=c("Never Seen","Sanger Mutant","Known Resistant Mutant"),values=c("gray","orange","red"))+cleanup+theme(legend.position = "none")

ggplot(data%>%group_by(species)%>%mutate(n_species=n())%>%filter(protein_start>=242,protein_start<=494,n_species%in%1),aes(x=reorder(species,-netgr_mean),y=netgr_mean,fill=resmut_cosmic))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete(name="Mutant")+scale_fill_manual(name="Resistance Status",labels=c("Never Seen","Sanger Mutant","Known Resistant Mutant"),values=c("gray","orange","red"))+cleanup+theme(legend.position = "none")

# ggsave("ImatinibEnrichment_Netgr_Distribution.pdf",width=8,height=4,units="in",useDingbats=F)

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,score_mean>=0),aes(x=reorder(species,-netgr_mean),y=netgr_mean,fill=resmut_cosmic))+geom_col()+
  scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+
  scale_x_discrete(name="Mutant")+
  scale_fill_manual(name="Resistance Status",labels=c("Never Seen","Sanger Mutant","Known Resistant Mutant"),values=c("gray","orange","red"))+
  theme(axis.text.x=element_text(angle=90, hjust=1),
        axis.text=element_text(size=6),
        panel.grid.major = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.background = element_blank())+
  theme(legend.position = "none")

# ggsave("ImatinibEnrichment_Netgr_Distribution_zoom.pdf",width=6,height=4,units="in",useDingbats=F)


ggplot(data%>%filter(!species%in%"V299L",protein_start>=242,protein_start<=322,n_nuc_min%in%1),aes(x=resmut_cosmic,y=netgr_mean,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  geom_jitter(color="black", size=1,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Sanger","Known\nResistant"))+cleanup+theme(legend.position = "none")

# ggsave("output/SM_Imatinib_Plots/ImatinibEnrichment_netgr_boxplot_baf3s_4.21.23.pdf",width=3,height=3,units="in",useDingbats=F)

plotly=ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-netgr_mean),y=netgr_mean,fill=resmut_cosmic))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name="Enrichcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))+scale_fill_manual(values=c("gray","orange","red"))
ggplotly(plotly)

Looking at important sanger mutants across BaF3s and K562s

# K562
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
# BAf3
# data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_high_rep1vsrep2_ft/screen_comparison_baf3_Imat_high_rep1vsrep2_ft.csv",header = T,stringsAsFactors = F)
source("code/res_residues_adder.R")
source("code/cosmic_data_adder.R")
# data=read.csv("output/ABLEnrichmentScreens/Imat_Enrichment_bgmerged_2.22.23.csv",header = T,stringsAsFactors = F)
# class(data)
data=res_residues_adder(data)
data=cosmic_data_adder(data)
# data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs.x,netgr_obs.y))
data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
data$resmut_cosmic="neither"
data[data$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
data[data$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data$resmut_cosmic=factor(data$resmut_cosmic,levels=c("neither","cosmic","resmut"))
# x=data%>%filter(resmut_cosmic%in%"cosmic")

ggplot(data%>%filter(!species%in%"V299L",protein_start>=242,protein_start<=322,n_nuc_min%in%1),aes(x=resmut_cosmic,y=netgr_mean,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  geom_jitter(color="black", size=1,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Sanger","Known\nResistant"))+cleanup+theme(legend.position = "none")

# ggsave("output/SM_Imatinib_Plots/ImatinibEnrichment_netgr_boxplot_baf3s_4.21.23.pdf",width=3,height=3,units="in",useDingbats=F)

# Analyzing the Sanger data 4.22.2023
sanger=read.csv("data/Cosmic_ABL/ABL_Cosmic_Gene_mutations.csv",header = T)
sanger=sanger%>%dplyr::rename(protein_start=Position,species=AA.Mutation,sanger.count=Count)%>%filter(Type%in%"Substitution - Missense")%>%dplyr::select(protein_start,species,sanger.count)
sanger=sanger%>%mutate(species=gsub("p.","",species),ref_aa=substring(species,1,1),
                       alt_aa=substring(species, nchar(species), nchar(species)))
sanger=sanger%>%mutate(sanger.present=T)
sanger=sanger%>%filter(protein_start%in%242:520)
# sanger=resmuts_adder(sanger)
sum(sanger[sanger$resmuts%in%T,"sanger.count"])

[1] 0

sum(sanger[sanger$resmuts%in%F,"sanger.count"])

[1] 0

# There are a total of 1884 patients with mutations in the kianse. 1193 of these patients have known clinically resistant ImatR mutations. However, 691 of these patients fall into the other category

# K562
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv",header = T,stringsAsFactors = F)
data=data%>%rowwise()%>%mutate(netgr_obs_mean.k562=mean(netgr_obs_screen1,netgr_obs_screen2),netgr_obs_sd.k562=sd(c(netgr_obs_screen1,netgr_obs_screen2)))
data.k562=data%>%dplyr::select(species,protein_start,ref_aa,alt_aa,n_nuc_min,netgr_obs_mean.k562,netgr_obs_sd.k562)
# Baf3
data=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_medium_rep1vsrep2_ft/screen_comparison_baf3_Imat_medium_rep1vsrep2_ft.csv",header = T,stringsAsFactors = F)
data=data%>%rowwise()%>%mutate(netgr_obs_mean.baf3=mean(netgr_obs_screen1,netgr_obs_screen2),netgr_obs_sd.baf3=sd(c(netgr_obs_screen1,netgr_obs_screen2)))
data.baf3=data%>%dplyr::select(species,protein_start,ref_aa,alt_aa,n_nuc_min,netgr_obs_mean.baf3,netgr_obs_sd.baf3)

data=merge(data.k562,data.baf3,by=c("species","protein_start","ref_aa","alt_aa","n_nuc_min"),all=T)
# data$sanger.present=F
# data$sanger.count=0
data=merge(data,sanger,by=c("species","protein_start","ref_aa","alt_aa"),all=T)

source("code/resmuts_adder.R")
data=resmuts_adder(data)

data=data%>%mutate(cosmic_present=sanger.present)
# data=data%>%mutate(alt_aa=substring(species, nchar(species), nchar(species)))
data$resmut_cosmic="neither"
data[data$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
data[data$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data$resmut_cosmic=factor(data$resmut_cosmic,levels=c("neither","cosmic","resmut"))


ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=resmut_cosmic,y=netgr_obs_mean.baf3,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  geom_jitter(color="black", size=.5,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Rare\n VUDR","Known\n Resistant\n Variant"))+cleanup+theme(legend.position = "none")

Warning: Removed 33 rows containing non-finite values (stat_boxplot).

Warning: Removed 33 rows containing missing values (geom_point).

# ggsave("output/SM_Imatinib_Plots/ImatinibEnrichment_netgr_boxplot_baf3s_4.21.23.pdf",width=3,height=3.5,units="in",useDingbats=F)

ggplot(data%>%filter(protein_start>=242,protein_start<=322),aes(x=resmut_cosmic,y=netgr_obs_mean.k562,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  geom_jitter(color="black", size=.5,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Rare\n VUDR","Known\n Resistant\n Variant"))+cleanup+theme(legend.position = "none")

Warning: Removed 26 rows containing non-finite values (stat_boxplot).

Warning: Removed 26 rows containing missing values (geom_point).

# ggsave("output/SM_Imatinib_Plots/ImatinibEnrichment_netgr_boxplot_k562s_4.21.23.pdf",width=3,height=3.5,units="in",useDingbats=F)

data.forttest=data%>%filter(protein_start>=242,protein_start<=322,n_nuc_min%in%1)
t.test(data.forttest[data.forttest$resmut_cosmic%in%"neither","netgr_obs_mean.baf3"],
       data.forttest[data.forttest$resmut_cosmic%in%"cosmic","netgr_obs_mean.baf3"])


    Welch Two Sample t-test

data:  data.forttest[data.forttest$resmut_cosmic %in% "neither", "netgr_obs_mean.baf3"] and data.forttest[data.forttest$resmut_cosmic %in% "cosmic", "netgr_obs_mean.baf3"]
t = -2.1581, df = 158.9, p-value = 0.03242
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.0079228121 -0.0003510056
sample estimates:
 mean of x  mean of y 
0.01752699 0.02166390

t.test(data.forttest[data.forttest$resmut_cosmic%in%"neither","netgr_obs_mean.baf3"],
       data.forttest[data.forttest$resmut_cosmic%in%"resmut","netgr_obs_mean.baf3"])


    Welch Two Sample t-test

data:  data.forttest[data.forttest$resmut_cosmic %in% "neither", "netgr_obs_mean.baf3"] and data.forttest[data.forttest$resmut_cosmic %in% "resmut", "netgr_obs_mean.baf3"]
t = -25.642, df = 81.671, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.03481670 -0.02980322
sample estimates:
 mean of x  mean of y 
0.01752699 0.04983696

t.test(data.forttest[data.forttest$resmut_cosmic%in%"neither","netgr_obs_mean.k562"],
       data.forttest[data.forttest$resmut_cosmic%in%"cosmic","netgr_obs_mean.k562"])


    Welch Two Sample t-test

data:  data.forttest[data.forttest$resmut_cosmic %in% "neither", "netgr_obs_mean.k562"] and data.forttest[data.forttest$resmut_cosmic %in% "cosmic", "netgr_obs_mean.k562"]
t = -1.3254, df = 161.51, p-value = 0.1869
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.0027061696  0.0005325136
sample estimates:
  mean of x   mean of y 
0.004696657 0.005783485

t.test(data.forttest[data.forttest$resmut_cosmic%in%"neither","netgr_obs_mean.k562"],
       data.forttest[data.forttest$resmut_cosmic%in%"resmut","netgr_obs_mean.k562"])


    Welch Two Sample t-test

data:  data.forttest[data.forttest$resmut_cosmic %in% "neither", "netgr_obs_mean.k562"] and data.forttest[data.forttest$resmut_cosmic %in% "resmut", "netgr_obs_mean.k562"]
t = -9.6932, df = 29.511, p-value = 1.114e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.01740694 -0.01134497
sample estimates:
  mean of x   mean of y 
0.004696657 0.019072614

ggplot(data%>%filter(sanger.present%in%T,n_nuc_min%in%1),aes(x=netgr_obs_mean.baf3,y=netgr_obs_mean.k562))+
  geom_point()+
  stat_cor(method="pearson")

Warning: Removed 2 rows containing non-finite values (stat_cor).

Warning: Removed 2 rows containing missing values (geom_point).

ggplot(data%>%filter(sanger.present%in%T,n_nuc_min%in%1),aes(x=netgr_obs_mean.baf3,y=netgr_obs_mean.k562,label=species,color=resmuts))+
  geom_text(size=1.6)+
  # stat_cor(method="pearson")+
  cleanup+
  scale_x_continuous("Net Growth Rate Baf3")+
  scale_y_continuous("Net Growth Rate K562")+
  scale_color_manual(values=c("orange","red"))+
  theme(legend.position = "none")

Warning: Removed 2 rows containing missing values (geom_text).

# ggsave("output/SM_Imatinib_Plots/baf3_k562_sanger_correlation.pdf",width=3,height=3,units = "in",useDingbats=F)



plotly=ggplot(data%>%filter(sanger.present%in%T,n_nuc_min%in%1),aes(x=netgr_obs_mean.baf3,y=netgr_obs_mean.k562,label=species,color=resmuts))+
  geom_text()+
  # stat_cor(method="pearson")+
  cleanup+
  scale_x_continuous("Net Growth Rate Baf3")+
  scale_y_continuous("Net Growth Rate K562")+
  scale_color_manual(values=c("orange","red"))+
  theme(legend.position = "none")
ggplotly(plotly)

x=data%>%filter(protein_start%in%c(242:322),n_nuc_min%in%1)%>%filter(resmut_cosmic%in%"neither")
x=data%>%filter(protein_start%in%c(242:322),n_nuc_min%in%1)%>%filter(resmut_cosmic%in%"cosmic")
x=data%>%filter(protein_start%in%c(242:322),n_nuc_min%in%1)%>%filter(resmut_cosmic%in%"cosmic")

# Making piechart that shows that VUDR take up 36%
sanger_forpiechart=resmuts_adder(sanger)
sanger_forpiechart$category="VUDR"
sanger_forpiechart=sanger_forpiechart%>%mutate(category=case_when(resmuts%in%T~species,
                                                                  T~category))



sanger_forpiechart=sanger_forpiechart%>%group_by(category)%>%summarize(count=sum(sanger.count))
sanger_forpiechart=sanger_forpiechart%>%arrange(desc(count))
sanger_forpiechart$category=factor(sanger_forpiechart$category,levels = as.character(sanger_forpiechart$category[order((sanger_forpiechart$count),decreasing = T)]))

#Plotting the normalized dose response curves
getPalette = colorRampPalette(brewer.pal(22, "Spectral"))

Warning in brewer.pal(22, "Spectral"): n too large, allowed maximum for palette Spectral is 11
Returning the palette you asked for with that many colors

library(RColorBrewer)
library("NatParksPalettes")
ggplot(sanger_forpiechart,aes(x="",y=count,fill=category))+
  geom_bar(width=1,stat="identity")+
  coord_polar("y", start=0)+
  # theme_minimal()+
  theme_void()+
  # scale_fill_manual(values=natparks.pals("Yellowstone", 3))
  scale_fill_manual(values = getPalette(length(unique(sanger_forpiechart$category))))

# ggsave("output/SM_Imatinib_Plots/sanger_piechart.pdf",width=4,height=3,units="in",useDingbats=F)

pie(sanger_forpiechart$count, labels = sanger_forpiechart$category)

ggplot(data%>%filter(species%in%c("L273M","F311L","V260L","P296R","D276E", "K247W")),aes(x=netgr_obs_mean.baf3,y=netgr_obs_mean.k562,label=species,color=resmuts))+
  geom_text(size=1.6)+
  # stat_cor(method="pearson")+
  cleanup+
  scale_x_continuous("Net Growth Rate Baf3")+
  scale_y_continuous("Net Growth Rate K562")+
  scale_color_manual(values=c("orange","red"))+
  theme(legend.position = "none")

Making histogram of top clinically prevalent sanger mutants

library(forcats)

Warning: package 'forcats' was built under R version 4.0.2

# Analyzing the Sanger data 4.22.2023
sanger=read.csv("data/Cosmic_ABL/ABL_Cosmic_Gene_mutations.csv",header = T)
sanger=sanger%>%dplyr::rename(protein_start=Position,species=AA.Mutation,sanger.count=Count)%>%filter(Type%in%"Substitution - Missense")%>%dplyr::select(protein_start,species,sanger.count)
sanger=sanger%>%mutate(species=gsub("p.","",species),ref_aa=substring(species,1,1),
                       alt_aa=substring(species, nchar(species), nchar(species)))
sanger=sanger%>%mutate(sanger.present=T)

sanger=sanger%>%group_by(species)%>%summarize(protein_start=mean(protein_start),sanger.count=sum(sanger.count))
sanger$resmuts=F
sanger[sanger$species%in%c("E355A","D276G","H396R","F317L","F359I","E459K","G250E","F359C","F359V","M351T","L248V","E355G","Q252H","Y253F","F486S","H396P","E255K","Y253H","T315I","E255V","M244V","V299L","L387M"),"resmuts"]=T


ggplot(sanger%>%filter(resmuts%in%T),aes(fct_reorder(species, -sanger.count),y=sanger.count,fill=resmuts))+
  geom_col(color="black",alpha=0.8)+
  scale_x_discrete("Mutant Name")+
  scale_y_continuous("Clinical Abundance")+
  scale_fill_manual(values=c("red"))+
  cleanup+
  theme(legend.position = "none",
        axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),
        axis.title.x=element_blank())

# ggsave("output/SM_Imatinib_Plots/clinical_abundance_zoom.pdf",width=5,height=3,units="in",useDingbats=F)

ggplot(sanger,aes(fct_reorder(species, -sanger.count),y=sanger.count,fill=resmuts))+
  geom_col(alpha=0.8)+
  scale_x_discrete("Mutant Name")+
  scale_y_continuous("Clinical Abundance")+
  scale_fill_manual(values=c("gray90","red"))+
  # cleanup+
  theme(legend.position = "none",
        axis.text.x=element_text(size=2,angle=90,hjust=.5,vjust=.5),
        axis.title.x=element_blank())

# ggsave("output/SM_Imatinib_Plots/clinical_abundance.pdf",width=14,height=3,units="in",useDingbats=F)

ggplot(sanger,aes(fct_reorder(species, -sanger.count),y=sanger.count,fill=resmuts))+
  geom_col()+
  scale_x_discrete("Mutant Name")+
  scale_y_continuous("Clinical Abundance")+
  scale_fill_manual(values=c("gray90","red"))+
  theme(legend.position = "none",
        axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),
        axis.title.x=element_blank())

Heatmaps

smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region123_MergedwithOlddata/IL3_Enrichment_bgmerged_4.19.23.csv")
# Baf3s
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2_ft/screen_comparison_baf3_Imat_low_rep1vsrep2_ft.csv")
smdata=smdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))


heatmap_plotting_function(smdata,290,297,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="",expand=c(0,0),breaks=c(290:297),labels=c("290","291","292","293","294","295","296","297"))+
  scale_fill_gradient(low ="darkblue", high ="red",name="Net growth rate")+theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.

Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.

heatmap_plotting_function(smdata%>%mutate(netgr_obs_mean=case_when(netgr_obs_mean>=.06~.06,
                                                    T~netgr_obs_mean)),242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,321),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=.035, high ="red",name="Net growth rate")+theme(legend.position = "none")

Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.

# ggsave("output/SM_Imatinib_Plots/heatmap_Imatinib_baf3.pdf",width=6,height = 4,units = "in",useDingbats=F)

####### K562 Low#######
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_low_rep1vsrep2/screen_comparison_k562_imat_low_rep1vsrep2.csv")
smdata=smdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))

heatmap_plotting_function(smdata%>%mutate(netgr_obs_mean=case_when(netgr_obs_mean>=.045~.045,
                                                    T~netgr_obs_mean)),242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,321),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=.02, high ="red",name="Net growth rate")+theme(legend.position = "none")

Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.

# ggsave("output/SM_Imatinib_Plots/heatmap_Imatinib_k562low.pdf",width=6,height = 4,units = "in",useDingbats=F)


####### K562 Medium#######
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv")
smdata=smdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))


heatmap_plotting_function(smdata,242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")

heatmap_plotting_function(smdata,242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,321),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=0, high ="red",name="Net growth rate")+theme(legend.position = "none")

Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.

# ggsave("output/SM_Imatinib_Plots/heatmap_Imatinib_k562med.pdf",width=6,height = 4,units = "in",useDingbats=F)


####### K562 High#######
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_high_rep1vsrep2/screen_comparison_k562_imat_high_rep1vsrep2.csv")
smdata=smdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))


heatmap_plotting_function(smdata,242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")

heatmap_plotting_function(smdata,242,321,fill_variable = "netgr_obs_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,321),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=-0.005, high ="red",name="Net growth rate")+theme(legend.position = "none")

Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.

# ggsave("output/SM_Imatinib_Plots/heatmap_Imatinib_k562high.pdf",width=6,height = 4,units = "in",useDingbats=F)

smdata.k562=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_high_rep1vsrep2/screen_comparison_k562_imat_high_rep1vsrep2.csv")
# smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_low_rep1vsrep2/screen_comparison_k562_imat_low_rep1vsrep2.csv")
# smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/k562_imat_medium_rep1vsrep2/screen_comparison_k562_imat_medium_rep1vsrep2.csv")
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_imat_low_rep1vsrep2_ft/screen_comparison_baf3_imat_low_rep1vsrep2_ft.csv")
# smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_imat_medium_rep1vsrep2_ft/screen_comparison_baf3_imat_medium_rep1vsrep2_ft.csv")
smdata.baf3=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_imat_high_rep1vsrep2_ft/screen_comparison_baf3_imat_high_rep1vsrep2_ft.csv")
smdata.baf3=cosmic_data_adder(smdata.baf3)
smdata.k562=cosmic_data_adder(smdata.k562)

smdata.baf3=smdata%>%filter(!species%in%"V299L")
smdata.baf3=smdata.baf3%>%filter(n_nuc_min%in%1)
smdata.k562=smdata.k562%>%filter(n_nuc_min%in%1)

smdata.baf3=smdata.baf3%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
smdata.baf3=smdata.baf3%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
# Demonstrating that the SM data works better at predicting residue exposure than the BE data
# smdata_roc=smdata%

# glm.fit.sm=glm(as.numeric(smdata$cosmic_present)~smdata$netgr_obs_mean,family=binomial)

glm.fit.baf3=glm(as.numeric(smdata.baf3$resmuts)~smdata.baf3$netgr_obs_mean,family=binomial)
glm.fit.k562=glm(as.numeric(smdata.k562$resmuts)~smdata.k562$netgr_obs_mean,family=binomial)
smdata.baf3$glm_fits=glm.fit.baf3$fitted.values
smdata.k562$glm_fits=glm.fit.k562$fitted.values
# roc(as.numeric(smdata$cosmic_present),smdata$glm_fits,plot=T,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True
#     Positive Percentage",print.auc=T)
roc(as.numeric(smdata.baf3$resmuts),smdata.baf3$glm_fits,plot=T,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T)

Setting levels: control = 0, case = 1

Setting direction: controls < cases


Call:
roc.default(response = as.numeric(smdata.baf3$resmuts), predictor = smdata.baf3$glm_fits,     percent = T, plot = T, legacy.axes = T, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", print.auc = T)

Data: smdata.baf3$glm_fits in 434 controls (as.numeric(smdata.baf3$resmuts) 0) < 9 cases (as.numeric(smdata.baf3$resmuts) 1).
Area under the curve: 87.79%

roc(as.numeric(smdata.k562$resmuts),smdata.k562$glm_fits,plot=T,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T)

Setting levels: control = 0, case = 1
Setting direction: controls < cases


Call:
roc.default(response = as.numeric(smdata.k562$resmuts), predictor = smdata.k562$glm_fits,     percent = T, plot = T, legacy.axes = T, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", print.auc = T)

Data: smdata.k562$glm_fits in 452 controls (as.numeric(smdata.k562$resmuts) 0) < 10 cases (as.numeric(smdata.k562$resmuts) 1).
Area under the curve: 98.38%

# The GLM in this case looks a little weird, probably because there are so many negative datapoints
# ggplot(smdata.baf3,aes(x=netgr_obs_mean,y=as.numeric(resmuts)))+geom_point()+geom_line(aes(x=netgr_obs_mean,y=glm_fits))
# ggplot(smdata.k562,aes(x=netgr_obs_mean,y=as.numeric(resmuts)))+geom_point()+geom_line(aes(x=netgr_obs_mean,y=glm_fits))

par(pty="s")
roc(as.numeric(smdata.baf3$resmuts),smdata.baf3$glm_fits,plot=T,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T,col="#3937E3",lwd=4)

Setting levels: control = 0, case = 1
Setting direction: controls < cases


Call:
roc.default(response = as.numeric(smdata.baf3$resmuts), predictor = smdata.baf3$glm_fits,     percent = T, plot = T, legacy.axes = T, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", print.auc = T, col = "#3937E3",     lwd = 4)

Data: smdata.baf3$glm_fits in 434 controls (as.numeric(smdata.baf3$resmuts) 0) < 9 cases (as.numeric(smdata.baf3$resmuts) 1).
Area under the curve: 87.79%

# plot.roc(as.numeric(be_lfc.sm$DSSP.Buried),be_lfc.sm$glm_fits,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T,col="#378BE3",add=T,lwd=4,print.auc.y=45)
plot.roc(as.numeric(smdata.k562$resmuts),smdata.k562$glm_fits,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T,col="#378BE3",add=T,lwd=4,print.auc.y=45)

Setting levels: control = 0, case = 1
Setting direction: controls < cases

par(pty="m")

Doing quick correlation of lane 18b sample 4 NGS vs SSCS

ngs=read.csv("data/Consensus_Data/novogene_lane18/sample4/ngs/variant_caller_outputs/variants_unique_ann.csv")
ngs=ngs%>%filter(protein_start%in%c(242:272))%>%mutate(maf=ct/depth)%>%dplyr::select(ref_aa,protein_start,alt_aa,alt_codon,maf)
sscs=read.csv("data/Consensus_Data/novogene_lane18/sample4/sscs/variant_caller_outputs/variants_unique_ann.csv")
sscs=sscs%>%filter(protein_start%in%c(242:272))%>%mutate(maf=ct/depth)%>%dplyr::select(ref_aa,protein_start,alt_aa,alt_codon,maf)

ngs_sscs=merge(ngs,sscs,by=c("ref_aa","protein_start","alt_aa","alt_codon"))
ngs_sscs=ngs_sscs%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep=""))
# ngs_sscs=ngs_sscs%>%filter(alt_codon%in%twist_alt_codon)

# plotly=ggplot(ngs_sscs,aes(x=maf.x,y=maf.y))+geom_point()+scale_x_continuous("NGS",trans="log10")+scale_y_continuous("SSCS",trans="log10")
# ggplotly(plotly)

plotly=ggplot(ngs_sscs,aes(x=maf.x,y=maf.y,label=species))+geom_text()+scale_x_continuous("NGS",trans="log10")+scale_y_continuous("SSCS",trans="log10")+geom_abline()
ggplotly(plotly)

plotly=ggplot(ngs_sscs,aes(x=maf.x,y=maf.y,label=species))+geom_text()+scale_x_continuous("NGS")+scale_y_continuous("SSCS")+geom_abline()
ggplotly(plotly)

x=ngs%>%filter(protein_start%in%255,alt_aa%in%"L",alt_codon%in%"CTG")
x=ngs_sscs%>%filter(species%in%"E255L")
codon_table=read.csv("data/codon_table.csv",header = T,stringsAsFactors = F)

  twist_codons=codon_table%>%filter(Twist%in%T)
  # twist_alt_codon=twist_codons[twist_codons$Letter%in%alt_aa,"Codon"]
twist_alt_codon=twist_codons[,"Codon"]

sample10=read.csv("data/Consensus_Data/novogene_lane18/sample10/ngs/variant_caller_outputs/variants_unique_ann.csv")
# sample10=sample10%>%filter(protein_start%in%c(242:272))%>%mutate(maf=ct/depth)%>%dplyr::select(ref_aa,protein_start,alt_aa,alt_codon,maf.d0=maf)
sample10=sample10%>%
  # filter(protein_start%in%c(242:272))%>%
  filter(protein_start%in%c(242:322))%>%
  mutate(maf=ct/depth,
         species=paste(ref_aa,protein_start,alt_aa,sep=""))%>%
  dplyr::select(type,ref_aa,protein_start,alt_aa,alt_codon,species,maf.d0=maf)
sample10=sample10%>%filter(alt_codon%in%twist_alt_codon)%>%
  group_by(type,ref_aa,protein_start,alt_aa,alt_codon,species)%>%
  summarize(maf.d0=max(maf.d0))

`summarise()` has grouped output by 'type', 'ref_aa', 'protein_start', 'alt_aa', 'alt_codon'. You can override using the `.groups` argument.

sample4=read.csv("data/Consensus_Data/novogene_lane18/sample4/ngs/variant_caller_outputs/variants_unique_ann.csv")
sample4=sample4%>%
  # filter(protein_start%in%c(242:272))%>%
  filter(protein_start%in%c(242:322))%>%
  mutate(maf=ct/depth,
         species=paste(ref_aa,protein_start,alt_aa,sep=""))%>%
  dplyr::select(type,ref_aa,protein_start,alt_aa,alt_codon,species,maf.screen1=maf)
sample4=sample4%>%filter(alt_codon%in%twist_alt_codon)%>%
  group_by(type,ref_aa,protein_start,alt_aa,alt_codon,species)%>%
  summarize(maf.screen1=max(maf.screen1))

`summarise()` has grouped output by 'type', 'ref_aa', 'protein_start', 'alt_aa', 'alt_codon'. You can override using the `.groups` argument.

sample5=read.csv("data/Consensus_Data/novogene_lane18/sample5/ngs/variant_caller_outputs/variants_unique_ann.csv")
sample5=sample5%>%
  # filter(protein_start%in%c(242:272))%>%
  filter(protein_start%in%c(242:322))%>%
  mutate(maf=ct/depth,
         species=paste(ref_aa,protein_start,alt_aa,sep=""))%>%
  dplyr::select(type,ref_aa,protein_start,alt_aa,alt_codon,species,maf.screen2=maf)
sample5=sample5%>%filter(alt_codon%in%twist_alt_codon)%>%
  group_by(type,ref_aa,protein_start,alt_aa,alt_codon,species)%>%
  summarize(maf.screen2=max(maf.screen2))

`summarise()` has grouped output by 'type', 'ref_aa', 'protein_start', 'alt_aa', 'alt_codon'. You can override using the `.groups` argument.

# sample5=sample5%>%filter(protein_start%in%c(242:272))%>%mutate(maf=ct/depth)%>%dplyr::select(ref_aa,protein_start,alt_aa,alt_codon,maf.screen2=maf)

baf3_low=merge(sample10,sample4,by=c("type","ref_aa","protein_start","alt_aa","alt_codon"))
baf3_low=merge(baf3_low,sample5,by=c("type","ref_aa","protein_start","alt_aa","alt_codon"))
# baf3_low=baf3_low%>%filter(alt_codon%in%twist_alt_codon)
baf3_low=baf3_low%>%mutate(netgr_screen1=log(maf.screen1*25546/(maf.d0*54))/144,
                           netgr_screen2=log(maf.screen2*22471/(maf.d0*54))/144)

plotly=ggplot(baf3_low,aes(x=netgr_screen1,y=netgr_screen2))+geom_point()+scale_x_continuous(trans="log10")+scale_y_continuous(trans="log10")+facet_wrap(~type)
ggplotly(plotly)

Warning in self$trans$transform(x): NaNs produced

Warning: Transformation introduced infinite values in continuous x-axis

Warning in self$trans$transform(x): NaNs produced

Warning: Transformation introduced infinite values in continuous y-axis

Warning: `group_by_()` was deprecated in dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help

source("code/resmuts_adder.R")
baf3_low=resmuts_adder(baf3_low)
plotly=ggplot(baf3_low,aes(x=netgr_screen1,y=netgr_screen2,label=species,color=resmuts))+geom_text()+scale_x_continuous(trans="log10")+scale_y_continuous(trans="log10")
ggplotly(plotly)

Warning in self$trans$transform(x): NaNs produced

Warning: Transformation introduced infinite values in continuous x-axis

Warning in self$trans$transform(x): NaNs produced

Warning: Transformation introduced infinite values in continuous y-axis

cor(baf3_low$netgr_screen1,baf3_low$netgr_screen2)

[1] 0.9508483

class(baf3_low$netgr_screen1)

[1] "numeric"

baf3_low=baf3_low%>%mutate(netgr_obs_mean=(netgr_screen1+netgr_screen2)/2)

glm.fit.baf3=glm(as.numeric(baf3_low$resmuts)~baf3_low$netgr_obs_mean,family=binomial)
baf3_low$glm_fits=glm.fit.baf3$fitted.values
roc(as.numeric(baf3_low$resmuts),baf3_low$glm_fits,plot=T,legacy.axes=T,percent=T,xlab="False Positive Percentage",ylab="True Positive Percentage",print.auc=T)

Setting levels: control = 0, case = 1

Setting direction: controls < cases


Call:
roc.default(response = as.numeric(baf3_low$resmuts), predictor = baf3_low$glm_fits,     percent = T, plot = T, legacy.axes = T, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", print.auc = T)

Data: baf3_low$glm_fits in 1248 controls (as.numeric(baf3_low$resmuts) 0) < 12 cases (as.numeric(baf3_low$resmuts) 1).
Area under the curve: 75.85%

baf3_low2=baf3_low%>%filter(!protein_start%in%c(289:306))%>%dplyr::select(ref_aa,protein_start,alt_aa,species,netgr_obs_mean)
# baf3_low=
  
smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_imat_low_rep1vsrep2_ft/screen_comparison_baf3_imat_low_rep1vsrep2_ft.csv")
smdata=smdata%>%filter(!ct_screen1_after%in%.5,!ct_screen1_after%in%.5)
smdata=smdata%>%filter(!protein_start%in%c(289:306))%>%dplyr::select(ref_aa,protein_start,alt_aa,species,netgr_obs_mean)

  
baf3_low2=merge(baf3_low2,smdata,by=c("ref_aa","protein_start","alt_aa","species"),all = T)
baf3_low2=baf3_low2%>%filter(!ref_aa==alt_aa)
baf3_low2$ngsonly=F
baf3_low2[baf3_low2$netgr_obs_mean.y%in%NA,"ngsonly"]=T
baf3_low2[baf3_low2$netgr_obs_mean.y%in%NA,"netgr_obs_mean.y"]=.01
  
ggplot(baf3_low2,aes(x=netgr_obs_mean.x,y=netgr_obs_mean.y,label=species,color=ngsonly))+geom_text()+stat_cor(method = "pearson")

Warning: Removed 3 rows containing non-finite values (stat_cor).

Warning: Removed 3 rows containing missing values (geom_text).

x=baf3_low2%>%filter(netgr_obs_mean.y%in%NA)

smdata=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_imat_low_rep1vsrep2_ft/screen_comparison_baf3_imat_low_rep1vsrep2_ft.csv")
ggplot(smdata,aes(x=ct_screen1_after,y=netgr_obs_mean))+geom_point()+scale_x_continuous(trans="log10")

Merging ABL Lane 18 dataset

Plotting the short et al data for Twinstrand

#################################
short_mutants=read.csv("data/Short_et_al_fig1/short_et_al_3.12.23.csv",header = T)
short_mutants$preexisting_all=T
short_mutants=short_mutants%>%dplyr::select(-Index)%>%rename(species=Species)
data_merged=merge(data_merged,short_mutants,by="species",all.x = T)
data_merged[!data_merged$preexisting_all%in%T,"preexisting_all"]=F
nrow(data_merged%>%filter(preexisting_all%in%T))

[1] 43

# source("code/resmuts_adder.R")
data_merged=resmuts_adder(data_merged)
data_merged$resmut_cosmic="neither"
data_merged[data_merged$preexisting_all%in%TRUE,"resmut_cosmic"]="preexisting"
data_merged[data_merged$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
data_merged$resmut_cosmic=factor(data_merged$resmut_cosmic,levels=c("neither","preexisting","resmut"))


ggplot(data_merged%>%filter(n_nuc_min%in%1),aes(x=resmut_cosmic,y=netgr_obs_mean,fill=resmut_cosmic))+
  geom_violin(color="black")+
  # geom_boxplot(color="black")+
  geom_jitter(color="black", size=.5,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","gray50","red"))+scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+scale_x_discrete("",labels=c("Undetected","Pre-existing\n ALL","Known\nResistant\n Variant"))+cleanup+theme(legend.position = "none")

# ggsave("data/Short_et_al_fig1/shortetal_netgr_BoxPlot_4.14.23.pdf",width=4,height=4,units="in",useDingbats=F)
# ggsave("output/SM_Imatinib_Plots/shortetal_netgr_BoxPlot_4.14.23.pdf",width=3,height=3,units="in",useDingbats=F)
x=short_mutants%>%filter(!Classification%in%"Silent")
x=data_merged%>%filter(protein_start%in%c(242:322),preexisting_all%in%T)
x=data_merged%>%filter(protein_start>=242,protein_start<=322,n_nuc_min%in%1,preexisting_all%in%T,resmuts%in%F)
x=data_merged%>%filter(n_nuc_min%in%1,preexisting_all%in%T,resmuts%in%F)

bi=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
bi$dose=0
bi=bi%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_mean)

bl=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2/screen_comparison_baf3_Imat_low_rep1vsrep2.csv")
bl$dose=300
bl=bl%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_mean)

bm=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_medium_rep1vsrep2/screen_comparison_baf3_Imat_medium_rep1vsrep2.csv")
bm$dose=600
bm=bm%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_mean)


bh=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_high_rep1vsrep2/screen_comparison_baf3_Imat_high_rep1vsrep2.csv")
bh$dose=1200
bh=bh%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_mean)

b_merged=merge(bl,bm,by = c("ref_aa","protein_start","alt_aa","species"),suffixes = c(".low",".medium"))

b_merged=merge(b_merged,bh%>%rename(dose.high=dose,netgr_obs_mean.high=netgr_obs_mean),by = c("ref_aa","protein_start","alt_aa","species"))

b_merged=merge(b_merged,bi%>%rename(dose.il3=dose,netgr_obs_mean.il3=netgr_obs_mean),by = c("ref_aa","protein_start","alt_aa","species"))
source("code/cosmic_data_adder.R")
b_merged=cosmic_data_adder(b_merged)
b_merged=resmuts_adder(b_merged)

# write.csv(b_merged,"output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/baf3_smscreen_netgrowthrates_582023.csv")
# x=b_merged%>%filter(cosmic_present%in%T)
# kl=kl%>%mutate(netgr_obs_mean=netgr_obs_screen1)

# kl=cosmic_data_adder(kl)

6.2.2022 I am adding replicate level growth rates for Scott to use

bi=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
bi$dose=0
bi=bi%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_rep1=netgr_obs_screen1,netgr_obs_rep2=netgr_obs_screen2)

bl=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2/screen_comparison_baf3_Imat_low_rep1vsrep2.csv")
bl$dose=300
bl=bl%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_rep1=netgr_obs_screen1,netgr_obs_rep2=netgr_obs_screen2)

bm=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_medium_rep1vsrep2/screen_comparison_baf3_Imat_medium_rep1vsrep2.csv")
bm$dose=600
bm=bm%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_rep1=netgr_obs_screen1,netgr_obs_rep2=netgr_obs_screen2)


bh=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_high_rep1vsrep2/screen_comparison_baf3_Imat_high_rep1vsrep2.csv")
bh$dose=1200
bh=bh%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,netgr_obs_rep1=netgr_obs_screen1,netgr_obs_rep2=netgr_obs_screen2)

b_merged=merge(bl,bm,by = c("ref_aa","protein_start","alt_aa","species"),suffixes = c(".low",".medium"))

b_merged=merge(b_merged,bh%>%rename(dose.high=dose,netgr_obs_rep1.high=netgr_obs_rep1,netgr_obs_rep2.high=netgr_obs_rep2),by = c("ref_aa","protein_start","alt_aa","species"))

b_merged=merge(b_merged,bi%>%rename(dose.il3=dose,netgr_obs_rep1.il3=netgr_obs_rep1,netgr_obs_rep2.il3=netgr_obs_rep2),by = c("ref_aa","protein_start","alt_aa","species"))
source("code/cosmic_data_adder.R")
source("code/resmuts_adder.R")
b_merged=cosmic_data_adder(b_merged)
b_merged=resmuts_adder(b_merged)

# write.csv(b_merged,"output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/baf3_smscreen_netgrowthrates_622023.csv")
# x=b_merged%>%filter(cosmic_present%in%T)
# kl=kl%>%mutate(netgr_obs_mean=netgr_obs_screen1)

# kl=cosmic_data_adder(kl)

bi=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_IL3_rep1vsrep2_ft/screen_comparison_baf3_IL3_low_rep1vsrep2_ft.csv")
bi$dose=0
bi=bi%>%mutate(lfc_rep1=log2(maf_screen1_after/maf_screen1_before),lfc_rep2=log2(maf_screen2_after/maf_screen2_before))
bi=bi%>%dplyr::select(ref_aa,protein_start,alt_aa,species,n_nuc_min,dose,lfc_rep1,lfc_rep2)

bl=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_low_rep1vsrep2/screen_comparison_baf3_Imat_low_rep1vsrep2.csv")
bl$dose=300
bl=bl%>%mutate(lfc_rep1=log2(maf_screen1_after/maf_screen1_before),lfc_rep2=log2(maf_screen2_after/maf_screen2_before))
bl=bl%>%dplyr::select(ref_aa,protein_start,alt_aa,species,n_nuc_min,dose,lfc_rep1,lfc_rep2)
# bl=bl%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,lfc_obs_rep1=lfc_obs_screen1,lfc_obs_rep2=lfc_obs_screen2)

bm=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_medium_rep1vsrep2/screen_comparison_baf3_Imat_medium_rep1vsrep2.csv")
bm$dose=600
bm=bm%>%mutate(lfc_rep1=log2(maf_screen1_after/maf_screen1_before),lfc_rep2=log2(maf_screen2_after/maf_screen2_before))
bm=bm%>%dplyr::select(ref_aa,protein_start,alt_aa,species,n_nuc_min,dose,lfc_rep1,lfc_rep2)
# bm=bm%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,lfc_obs_rep1=lfc_obs_screen1,lfc_obs_rep2=lfc_obs_screen2)


bh=read.csv("output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/baf3_Imat_high_rep1vsrep2/screen_comparison_baf3_Imat_high_rep1vsrep2.csv")
bh$dose=1200
bh=bh%>%mutate(lfc_rep1=log2(maf_screen1_after/maf_screen1_before),lfc_rep2=log2(maf_screen2_after/maf_screen2_before))
bh=bh%>%dplyr::select(ref_aa,protein_start,alt_aa,species,n_nuc_min,dose,lfc_rep1,lfc_rep2)
# bh=bh%>%dplyr::select(ref_aa,protein_start,alt_aa,species,dose,lfc_obs_rep1=lfc_obs_screen1,lfc_obs_rep2=lfc_obs_screen2)

b_merged=merge(bl,bm,by = c("ref_aa","protein_start","alt_aa","species","n_nuc_min"),suffixes = c(".low",".medium"))

b_merged=merge(b_merged,bh%>%rename(dose.high=dose,lfc_rep1.high=lfc_rep1,lfc_rep2.high=lfc_rep2),by = c("ref_aa","protein_start","alt_aa","species","n_nuc_min"))

b_merged=merge(b_merged,bi%>%rename(dose.il3=dose,lfc_rep1.il3=lfc_rep1,lfc_rep2.il3=lfc_rep2),by = c("ref_aa","protein_start","alt_aa","species","n_nuc_min"))
source("code/cosmic_data_adder.R")
source("code/resmuts_adder.R")
b_merged=cosmic_data_adder(b_merged)
b_merged=resmuts_adder(b_merged)

# write.csv(b_merged,"output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/baf3_smscreen_lfc_682023.csv")
# x=b_merged%>%filter(cosmic_present%in%T)
# kl=kl%>%mutate(netgr_obs_mean=netgr_obs_screen1)

# kl=cosmic_data_adder(kl)

sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

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  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] forcats_0.5.1          NatParksPalettes_0.2.0 pROC_1.16.2           
 [4] ggpubr_0.4.0           RColorBrewer_1.1-2     doParallel_1.0.15     
 [7] iterators_1.0.12       foreach_1.5.0          tictoc_1.0            
[10] plotly_4.9.2.1         ggplot2_3.3.3          dplyr_1.0.6           
[13] stringr_1.4.0         

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.1        tidyr_1.1.3       jsonlite_1.7.2   
 [5] viridisLite_0.3.0 carData_3.0-3     bslib_0.3.1       assertthat_0.2.1 
 [9] cellranger_1.1.0  yaml_2.2.1        pillar_1.6.1      backports_1.1.7  
[13] glue_1.4.1        digest_0.6.25     promises_1.1.0    ggsignif_0.6.0   
[17] colorspace_1.4-1  htmltools_0.5.2   httpuv_1.5.2      plyr_1.8.6       
[21] pkgconfig_2.0.3   broom_0.7.6       haven_2.4.1       purrr_0.3.4      
[25] scales_1.1.1      whisker_0.4       openxlsx_4.1.5    later_1.0.0      
[29] rio_0.5.16        git2r_0.27.1      tibble_3.1.2      generics_0.0.2   
[33] farver_2.0.3      car_3.0-7         ellipsis_0.3.2    withr_2.4.2      
[37] lazyeval_0.2.2    magrittr_2.0.1    crayon_1.4.1      readxl_1.3.1     
[41] evaluate_0.14     fs_1.4.1          fansi_0.4.1       rstatix_0.6.0    
[45] foreign_0.8-78    tools_4.0.0       data.table_1.12.8 hms_1.1.0        
[49] lifecycle_1.0.0   munsell_0.5.0     zip_2.0.4         compiler_4.0.0   
[53] jquerylib_0.1.4   rlang_0.4.11      grid_4.0.0        htmlwidgets_1.5.1
[57] crosstalk_1.1.0.1 labeling_0.3      rmarkdown_2.14    gtable_0.3.0     
[61] codetools_0.2-16  abind_1.4-5       DBI_1.1.0         curl_4.3         
[65] R6_2.4.1          knitr_1.28        fastmap_1.1.0     utf8_1.1.4       
[69] workflowr_1.6.2   rprojroot_1.3-2   stringi_1.7.5     Rcpp_1.0.4.6     
[73] vctrs_0.3.8       tidyselect_1.1.0  xfun_0.31

ABL_SM_CRISPR_Cut_Analyses

Haider Inam

2023-03-22