ABL_SM_CRISPR_Cut

Last updated: 2023-04-02

Checks: 6 1

Knit directory: duplex_sequencing_screen/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200402)

The command set.seed(20200402) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 88dabff

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 88dabff. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


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Unstaged changes:
    Modified:   .DS_Store
    Modified:   analysis/ABL_BaseEditor_Analyses_v2.Rmd
    Modified:   analysis/ABL_SM_CRISPR_Cut_Analyses.Rmd
    Modified:   code/.DS_Store
    Modified:   data/.DS_Store
    Modified:   data/Consensus_Data/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane12/.DS_Store
    Modified:   data/Consensus_Data/novogene_lane16a/.DS_Store
    Modified:   data/Consensus_Data/novogene_lane17/.DS_Store
    Modified:   data/Consensus_Data/novogene_lane18/.DS_Store
    Modified:   data/Consensus_Data/novogene_lane18/sample9/.DS_Store
    Modified:   data/Consensus_Data/novogene_lane18/sample9/nol298l/.DS_Store
    Modified:   data/Refs/.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-dose/.DS_Store
    Modified:   output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-species/.DS_Store
    Deleted:    output/BE_SM_Plots/SM_BE_Analysis_03192023_HI.pptx

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	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

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")

source("code/compare_screens.R")
source("code/plotting/cleanup.R")
source("code/plotting/heatmap_plotting_function.R")
# 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)){
  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 analysis on buried hydrophobic vs exposed residues

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))
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%>%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"))
ggplot(il3_dssp,aes(x=ACC))+geom_histogram()

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

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"))

# 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

# 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"))

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

Doing some quick analysis on buried hydrophobic vs exposed residues

dssp=read.csv("data/DSSP_SolventAccessibility_ABL/2gqg_dspp.csv",header = T)
dssp=dssp%>%mutate(RESIDUE=as.numeric(RESIDUE),ACC=as.numeric(ACC),AA=gsub("<ca>","",AA))
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%>%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"))
ggplot(il3_dssp,aes(x=ACC))+geom_histogram()

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

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"))

# 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

# 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"))

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

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()

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] RColorBrewer_1.1-2 doParallel_1.0.15  iterators_1.0.12   foreach_1.5.0     
[5] tictoc_1.0         plotly_4.9.2.1     ggplot2_3.3.3      dplyr_1.0.6       
[9] stringr_1.4.0     

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.31         bslib_0.3.1       purrr_0.3.4      
 [5] colorspace_1.4-1  vctrs_0.3.8       generics_0.0.2    htmltools_0.5.2  
 [9] viridisLite_0.3.0 yaml_2.2.1        utf8_1.1.4        rlang_0.4.11     
[13] jquerylib_0.1.4   later_1.0.0       pillar_1.6.1      glue_1.4.1       
[17] withr_2.4.2       DBI_1.1.0         lifecycle_1.0.0   munsell_0.5.0    
[21] gtable_0.3.0      workflowr_1.6.2   htmlwidgets_1.5.1 codetools_0.2-16 
[25] evaluate_0.14     labeling_0.3      knitr_1.28        fastmap_1.1.0    
[29] httpuv_1.5.2      fansi_0.4.1       Rcpp_1.0.4.6      promises_1.1.0   
[33] backports_1.1.7   scales_1.1.1      jsonlite_1.7.2    farver_2.0.3     
[37] fs_1.4.1          digest_0.6.25     stringi_1.7.5     rprojroot_1.3-2  
[41] grid_4.0.0        tools_4.0.0       magrittr_2.0.1    sass_0.4.1       
[45] lazyeval_0.2.2    tibble_3.1.2      crayon_1.4.1      whisker_0.4      
[49] tidyr_1.1.3       pkgconfig_2.0.3   ellipsis_0.3.2    data.table_1.12.8
[53] assertthat_0.2.1  rmarkdown_2.14    httr_1.4.2        R6_2.4.1         
[57] git2r_0.27.1      compiler_4.0.0

ABL_SM_CRISPR_Cut_Analyses

Haider Inam

2023-03-22