ABL_cosmic_analysis

workflowr

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• Checks
• Past versions

Last updated: 2023-08-30

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.

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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: f9eea37

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 f9eea37. 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_cosmic_analysis.Rmd
    Modified:   analysis/ABL_kinasefunction_data_parser.Rmd
    Modified:   analysis/index.Rmd
    Modified:   code/compare_screens.R
    Modified:   data/.DS_Store
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    Modified:   data/Consensus_Data/novogene_lane18/tlane18a_sample6/.DS_Store
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    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_cosmic_analysis.Rmd) and HTML (docs/ABL_cosmic_analysis.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	60b906b	haiderinam	2023-04-10	Added ROC analyses on BE-SM data
Rmd	6b51aa2	haiderinam	2023-03-25	Added Lane 18 Data with ABL Region 1 SM Screen
Rmd	81a5af3	haiderinam	2023-02-25	Added shortest codon finder function
html	81a5af3	haiderinam	2023-02-25	Added shortest codon finder function

---

For the density plots:

library(viridisLite)
library("MASS")

Warning: package 'MASS' was built under R version 4.0.5

Attaching package: 'MASS'

The following object is masked from 'package:plotly':

    select

The following object is masked from 'package:dplyr':

    select

theme_set(theme_bw(base_size = 16))

# Get density of points in 2 dimensions.
# @param x A numeric vector.
# @param y A numeric vector.
# @param n Create a square n by n grid to compute density.
# @return The density within each square.
get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

set.seed(1)
dat <- data.frame(
  x = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0, sd = 0.1)
  ),
  y = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0.1, sd = 0.2)
  )
)

This piece of code picks up some of the analysis done in ABL_unevenness_analysis.Rmd

Seeing how well our imatinib resistance mutants work with cosmic mutants

# source("code")
source("code/res_residues_adder.R")
source("code/variants_parser.R")
source("code/compare_samples.R")
source("code/resmuts_adder.R")
source("code/cosmic_data_adder.R")

data=read.csv("output/ABLEnrichmentScreens/Imatinib_Enrichment_2.20.23_v2.csv",header = T,stringsAsFactors = F)
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<=494),aes(x=reorder(species,-score_mean),y=score_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"))

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-score_mean),y=score_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"))

Past versions of unnamed-chunk-3-2.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

plotly=ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-score_mean),y=score_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 = "Cosmic\n Observed \n Mutant"))+scale_fill_manual(values=c("gray","orange","red"))

ggplotly(plotly)

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=score_screen1,y=score_screen2))+
  geom_point(color="black",shape=21,aes(fill=resmut_cosmic))+
  geom_abline()+
  scale_x_continuous(name="Score D0 D2")+
  scale_y_continuous(name="Score D0 D4")+
  labs(fill="Cosmic\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=resmut_cosmic,y=score_mean))+geom_boxplot(aes(fill=resmut_cosmic))+scale_fill_manual(values=c("gray90","orange","red"))

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# a=data%>%filter(score_mean%in%c(NA,NaN))
# a=data_cosmic%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,score_mean>=0,cosmic_present%in%T)

Netgr Plots for putting in presentation

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=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")

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# 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")

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

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

ggplot(data%>%filter(!ct_screen1_after%in%.5,!ct_screen2_after%in%.5,protein_start>=242,protein_start<=494),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+
  geom_point(color="black",shape=21,aes(fill=resmut_cosmic))+
  geom_abline()+
  scale_x_continuous(name="Net Growth Rate D0 D2")+
  scale_y_continuous(name="Net Growth Rate D0 D4")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))+
  cleanup+theme(legend.position = "none")

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# ggsave("ImatinibEnrichment_Plot_netgr_2.21.23.pdf",width=4,height=4,units="in",useDingbats=F)


ggplot(data%>%filter(!species%in%"T315I",protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=resmut_cosmic,y=netgr_mean,fill=resmut_cosmic))+
  geom_violin(color="black")+
  geom_boxplot(color="black",width=.1)+
  # geom_boxplot(color="black")+
  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")

Past versions of unnamed-chunk-4-4.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# ggsave("ImatinibEnrichment_netgr_BoxPlot_2.21.23.pdf",width=4,height=4,units="in",useDingbats=F)

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"))

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Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# 
ggplot(data%>%filter(!ct_screen1_after%in%.5,!ct_screen2_after%in%.5,protein_start>=242,protein_start<=494),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+
  geom_point(color="black",shape=21,aes(fill=resmut_cosmic))+
  geom_abline()+
  scale_x_continuous(name="Net Growth Rate D0 D2")+
  scale_y_continuous(name="Net Growth Rate D0 D4")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))+
  theme(legend.position = "none")

Past versions of unnamed-chunk-4-6.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

Score plots for putting in presentation

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-score_mean),y=score_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")

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# ggsave("ImatinibEnrichment_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,-score_mean),y=score_mean,fill=resmut_cosmic))+geom_col()+
  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"))+
  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")

Past versions of unnamed-chunk-5-2.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

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


ggplot(data%>%filter(!ct_screen1_after%in%.5,!ct_screen2_after%in%.5,protein_start>=242,protein_start<=494),aes(x=score_screen1,y=score_screen2))+
  geom_point(color="black",shape=21,aes(fill=resmut_cosmic))+
  geom_abline()+
  scale_x_continuous(name="Enrichment Score D0 D2")+
  scale_y_continuous(name="Enrichment Score D0 D4")+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))+
  cleanup+theme(legend.position = "none")

Past versions of unnamed-chunk-5-3.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# ggsave("ImatinibEnrichment_Plot_2.21.23.pdf",width=4,height=4,units="in",useDingbats=F)

ggplot(data%>%filter(!species%in%"T315I",protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=resmut_cosmic,y=score_mean,fill=resmut_cosmic))+
  geom_violin(color="black")+
  geom_boxplot(color="black",width=.1)+
  # geom_boxplot(color="black")+
  scale_fill_manual(values=c("gray90","orange","red"))+theme(legend.position = "none")+scale_y_continuous("Enrichment Score")+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Sanger","Known\nResistant"))

Past versions of unnamed-chunk-5-4.png

Version	Author	Date
f9eea37	haiderinam	2023-08-30
81a5af3	haiderinam	2023-02-25

# ggsave("ImatinibEnrichment_BoxPlot_2.21.23.pdf",width=4,height=4,units="in",useDingbats=F)

# a=data%>%filter(protein_start>=242,protein_start<=494)
# a=data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1)
# b=a[order(a$score_mean,decreasing=TRUE),]
# b=b[c(1:200),]
# c=a%>%filter(!resmut_cosmic%in%"neither")

#The following function finds the shortest and the likeliest codon given a ref codon and an ALT amino acid

# codon_table
# inputs:
ref_codon="AAT"
alt_aa="A"

#outputs:
#shortest codon nucleotides
#shortest alt codn
codon_table=read.csv("data/codon_table.csv",header = T,stringsAsFactors = F)
mut_probs=read.csv("data/MutationProbabilities/Input_Mutsubmat_broadExac.csv",header = T,stringsAsFactors = F)
mut_probs=mut_probs%>%mutate(subs=paste(ref,">",var,sep = ""))
mut_probs_simple=mut_probs%>%dplyr::select(subs,prop)
shortest_codon_finder=function(ref_codon,alt_aa){
    # For a given reference codon and a given ALT residue, this function will find the shortest possible ALT codon. If multiple codons are available
  
  #What is the twist codon for the given alt amino acid?
  # twist_codons=codon_table%>%filter(Twist%in%T)
  # twist_alt_codon=twist_codons[twist_codons$Letter%in%alt_aa,"Codon"]
  
  #Calculating hamming distance
  #Below, I calculate hamming distance by looking for a mismatch between the REF codon and the alt codon at each one of the 3 positions of the codon and adding them up.
  # alt_codons=codon_table%>%
  #   filter(Letter%in%alt_aa)%>%
  #   rowwise()%>%
  #   mutate(distance=as.numeric(!substr(ref_codon,1,1)%in%substr(Codon,1,1))+
  #            as.numeric(!substr(ref_codon,2,2)%in%substr(Codon,2,2))+
  #            as.numeric(!substr(ref_codon,3,3)%in%substr(Codon,3,3)),
  #          substitutions=)%>%
  #   ungroup()
  
  
  alt_codons=codon_table%>%
    filter(Letter%in%alt_aa)%>%
    rowwise()%>%
    mutate(distance=as.numeric(!substr(ref_codon,1,1)%in%substr(Codon,1,1))+
             as.numeric(!substr(ref_codon,2,2)%in%substr(Codon,2,2))+
             as.numeric(!substr(ref_codon,3,3)%in%substr(Codon,3,3)),
           subs_1=case_when(!substr(ref_codon,1,1)%in%substr(Codon,1,1)~paste(substr(ref_codon,1,1),">",substr(Codon,1,1),sep = ""),
                            T~""),
           subs_2=case_when(!substr(ref_codon,2,2)%in%substr(Codon,2,2)~paste(substr(ref_codon,2,2),">",substr(Codon,2,2),sep = ""),
                            T~""),
           subs_3=case_when(!substr(ref_codon,3,3)%in%substr(Codon,3,3)~paste(substr(ref_codon,3,3),">",substr(Codon,3,3),sep = ""),
                            T~""))%>%
    ungroup()
  
  # alt_codons=alt_codons%>%mutate(
  #   prob_1=
  # )
  # mut_probs_simple=mut_probs%>%dplyr::select(subs_1=subs,prop_1=prop)
  a=mut_probs_simple
  
  alt_codons=merge(alt_codons,a,by.x="subs_1",by.y="subs",all.x = T,)
  alt_codons=merge(alt_codons,a,by.x="subs_2",by.y="subs",all.x = T)
  alt_codons=merge(alt_codons,a,by.x="subs_3",by.y="subs",all.x = T)
  alt_codons=alt_codons%>%mutate(prop_1=prop,prop_2=prop.x,prop_3=prop.y)%>%dplyr::select(-c("prop","prop.x","prop.y"))
# if alt codon is a 3 nucleotide variant, then 
  alt_codons[alt_codons$prop_1%in%NA,"prop_1"]=1
  alt_codons[alt_codons$prop_2%in%NA,"prop_2"]=1
  alt_codons[alt_codons$prop_3%in%NA,"prop_3"]=1
  alt_codons=alt_codons%>%mutate(prob=prop_1*prop_2*prop_3)
  
  #################Choosing the shortest ALT codon ###############
  #################Method 1: Choose based on shortest hamming distance###############
            # #Sorting through the shortest hamming distance
            # mindist=min(alt_codons$distance)
            # alt_codons=alt_codons%>%filter(distance==mindist)
            # #If there are multiple rows at the shortest distance, and one of the codons is the twist codon, choose the twist codon. Aka if no codon is shorter than twist, choose twist
            # #Otherwise if there are multiple shortest codons and none of them are the twist codon, then return both of them
            # if(sum(as.numeric(alt_codons$Twist%in%T))!=0){
            #   #The above boolean is saying "if you find twist codon amongst the shortest codons"
            #   shortest_alt_codon=as.character(alt_codons[alt_codons$Twist%in%T,"Codon"])
            #   # shortest_alt_codon=alt_codons$Codon
            #   # class(shortest_alt_codon)
            # } else{
            #   shortest_alt_codon=alt_codons$Codon[1]
            #   # Note that when there are multiple options for non-twist shortest ALT codons that code for the same residue, I just pick one of them. This happens for the following:
            #   # "AGG,CGG"
            #   # "CTC,TTA,TTG"
            #   # "CTA,TTA"
            #   # "AGT,TCT"
            #   # To view all shortest alt codons, use:
            #   # shortest_alt_codon=paste(alt_codons$Codon,collapse = ",")
          # }
    #################Method 2: Choose based on mutation probabilities###############
    #Choose the codon that yields the shortest hamming distance and the highest probability amongst the shortest codons.
    mindist=min(alt_codons$distance)
  alt_codons=alt_codons%>%filter(distance==mindist)
  alt_codons=alt_codons%>%filter(prob==max(prob))
  shortest_alt_codon=alt_codons$Codon
  maxprob=alt_codons$prob
    #Optional method 3: nother way of choosing shortest codons is to choose the most likeliest codons. This method does chooses the most likeliest codons, even if they're a higher hamming distance away. This is the mutant with the shortest hamming distance in most cases. Sometimes there multiple rows at the shortest hamming distance, so going by probability is fair. For example, two C>T or G>A substitutions are more likely than a single A>T substitution. However, these mutation biases are based on a ABL broad exac dataset with 224 mutations, so I still like the idea of giving higher precedence to hamming distance.
  #To execute the optional method 3, comment out the two mindist lines of code
    ####################################################################
    
  
  # return(list(shortest_alt_codon))
  return(list(shortest_alt_codon,mindist,maxprob))
}
# source("code/shortest_codon_finder.R")
shortest_codon_finder("AAT","A")[[1]][1]

[1] "GCT"

shortest_codon_finder("AAT","A")[[2]][1]

[1] 2

shortest_codon_finder("AAT","A")[[3]][1]

[1] 0.001694037

#The following codon combination is for T315I
shortest_codon_finder("ACT","I")

[[1]]
[1] "ATT"

[[2]]
[1] 1

[[3]]
[1] 0.3928571

#For the following codon combination, there are two non twist shortest codon
shortest_codon_finder("GGG","R")

[[1]]
[1] "AGG"

[[2]]
[1] 1

[[3]]
[1] 0.3035714

library(tictoc)

# tic()
# a=data%>%
#   rowwise()%>%
#   mutate(alt_codon_shortest=shortest_codon_finder(ref_codon,alt_aa)[[1]][1],
#          n_nuc_min=shortest_codon_finder(ref_codon,alt_aa)[[2]][1],
#          prob_max=shortest_codon_finder(ref_codon,alt_aa)[[3]][1])
# toc()

# b=a%>%filter(grepl(",",alt_codon_shortest)%in%T)
# unique(b$alt_codon_shortest)
# b=a%>%filter(alt_codon%in%alt_codon_shortest)

# T315I twist ACT to "ATC"
# T315I natural ACT to "ATT"

Are the score non-hits the improbable mutations?

#Among the SNPs in the dataset, what is their nucleotide substitution signature?
#For substitutions with more than 1 possible way of getting a snp, choose the SNP that's more probable in terms of substitution bias
tic()
data=data%>%
  rowwise()%>%
  mutate(alt_codon_shortest=shortest_codon_finder(ref_codon,alt_aa)[[1]][1],
         n_nuc_min=shortest_codon_finder(ref_codon,alt_aa)[[2]][1],
         prob_max=shortest_codon_finder(ref_codon,alt_aa)[[3]][1])
toc()

53.171 sec elapsed

data=data%>%mutate(prob_status=case_when(prob_max>=.05&&prob_max<.2~"medium",
                                         prob_max>=.2~"high",
                                         T~"low"))
data$prob_status=factor(data$prob_status,levels=c("low","medium","high"))

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,group=resmut_cosmic,fill=resmut_cosmic,alpha=prob_status))+geom_col()+
  scale_y_continuous(name="Net Growth Rate")+
  scale_x_discrete(name="Mutant")+
  scale_fill_manual(name=c("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())

Warning: Using alpha for a discrete variable is not advised.

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=resmut_cosmic,y=prob_max,fill=resmut_cosmic))+
  geom_violin(color="black")+
  geom_boxplot(color="black",width=.1)+
  # geom_boxplot(color="black")+
  scale_fill_manual(values=c("gray90","orange","red"))+theme(legend.position = "none")+scale_y_continuous("Enrichment Score")+scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Sanger","Known\nResistant"))

plotly=ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=prob_max,fill=resmut_cosmic))+
  geom_density(aes(alpha=.7))
ggplotly(plotly)

a=data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,prob_max>=.2,resmut_cosmic%in%"neither")
mean(a$netgr_mean)

[1] 0.009656468

a=data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,prob_max>=.2,!resmut_cosmic%in%"neither")
# a=data%>%filter(protein_start>=242,protein_start<=494,!resmut_cosmic%in%"neither")
mean(a$netgr_mean)

[1] 0.01258991

# data_snps=data%>%filter(n_nuc_min%in%1,resmut_cosmic%in%"neither")%>%dplyr::select(ref_codon,alt_aa,score_mean)
# a=data%>%filter(!resmut_cosmic%in%"neither")

As it can be seen from the plots, there are a good number of unseen hits that are low probability. However, some resistant mutants are also low probability…

We can imagine that some mutants are scoring higher because of an IL3 independence fitness advantage. Below, I remove the IL3 independence fitness advantage of a mutant. But there are number of mutants that are growing faster under imatinib than under IL3. These starting scoring high.

# dataimat=data
# data=dataimat
datail3=read.csv("output/ABLEnrichmentScreens/IL3_Enrichment_bgmerged_2.20.23.csv",header = T,stringsAsFactors = F)
datail3=datail3%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
# class(data)
# datail3=res_residues_adder(datail3)
# datail3=cosmic_data_adder(datail3)
# # datail3=datail3%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs.x,netgr_obs.y))
# datail3=datail3%>%rowwise()%>%mutate(netgr_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
# datail3$resmut_cosmic="neither"
# datail3[datail3$cosmic_present%in%TRUE,"resmut_cosmic"]="cosmic"
# datail3[datail3$resmuts%in%TRUE,"resmut_cosmic"]="resmut"
# datail3$resmut_cosmic=factor(datail3$resmut_cosmic,levels=c("neither","cosmic","resmut"))
ggplot(datail3%>%filter(protein_start<=494,protein_start>=242,consequence_terms%in%"missense_variant"),aes(x=netgr_obs_screen1,y=netgr_obs_screen2))+geom_point()+geom_abline()

datail3_simple=datail3%>%dplyr::select(ref_aa,protein_start,alt_aa,netgr_obs_screen1,netgr_obs_screen2,netgr_mean)


data_merged=merge(data,datail3_simple,by=c("ref_aa","protein_start","alt_aa"),suffixes = c("_imat","_il3"))
data_merged=data_merged%>%rowwise()%>%mutate(netgr_adj=.055-(netgr_mean_il3-netgr_mean_imat),
                                             alpha=netgr_mean_il3-netgr_mean_imat)

ggplot(data_merged%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,netgr_mean_il3>=0),aes(x=reorder(species,-netgr_adj),y=netgr_adj,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")

a=data_merged%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1,netgr_mean_il3>=0)

ggplot(data%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-netgr_obs_screen2),y=netgr_obs_screen2,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")

Session information

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] MASS_7.3-55        viridisLite_0.3.0  RColorBrewer_1.1-2 doParallel_1.0.15 
 [5] iterators_1.0.12   foreach_1.5.0      tictoc_1.0         plotly_4.9.2.1    
 [9] ggplot2_3.3.3      dplyr_1.0.6        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] yaml_2.2.1        utf8_1.1.4        rlang_0.4.11      jquerylib_0.1.4  
[13] later_1.0.0       pillar_1.6.1      glue_1.4.1        withr_2.4.2      
[17] DBI_1.1.0         lifecycle_1.0.0   munsell_0.5.0     gtable_0.3.0     
[21] workflowr_1.6.2   htmlwidgets_1.5.1 codetools_0.2-16  evaluate_0.14    
[25] labeling_0.3      knitr_1.28        fastmap_1.1.0     crosstalk_1.1.0.1
[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