ABL_Gnomad_analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-06-15

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: e57e36e

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 e57e36e. 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:
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    Modified:   output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/.DS_Store

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ABL_Gnomad_analysis.Rmd) and HTML (docs/ABL_Gnomad_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
Rmd	6b51aa2	haiderinam	2023-03-25	Added Lane 18 Data with ABL Region 1 SM Screen

---

source("code/cosmic_data_adder.R")

FUNCTION 1

# Now that I have the ALT Codon from each gnomad codon, I am going to figure out the subset of amino acids that are possible at an n_nuc of 1 from the alt codon and from the ref codon.
codon_table=read.csv("data/codon_table.csv",header = T,stringsAsFactors = F)
# Inputs: ref codon, alt_codon (which really is the new ref codon)
# Outputs: list of amino acids possible at an n_nuc of 1 with the ref codon but not with the alt codon
# refseq_codon="AAA"
# gnomad_codon="ATA"
# rm(ref_codon)
gnomad_unique_AAs=function(refseq_codon,gnomad_codon){
  refseq_codons=codon_table%>%
    # filter(Letter%in%alt_aa)%>%
    rowwise()%>%
    mutate(distance=as.numeric(!substr(refseq_codon,1,1)%in%substr(Codon,1,1))+
             as.numeric(!substr(refseq_codon,2,2)%in%substr(Codon,2,2))+
             as.numeric(!substr(refseq_codon,3,3)%in%substr(Codon,3,3)),
           subs_1=case_when(!substr(refseq_codon,1,1)%in%substr(Codon,1,1)~paste(substr(refseq_codon,1,1),">",substr(Codon,1,1),sep = ""),
                            T~""),
           subs_2=case_when(!substr(refseq_codon,2,2)%in%substr(Codon,2,2)~paste(substr(refseq_codon,2,2),">",substr(Codon,2,2),sep = ""),
                            T~""),
           subs_3=case_when(!substr(refseq_codon,3,3)%in%substr(Codon,3,3)~paste(substr(refseq_codon,3,3),">",substr(Codon,3,3),sep = ""),
                            T~""))%>%
    ungroup()

  refseq_codons=refseq_codons%>%filter(distance%in%1)
  sort(unique(refseq_codons$Letter))
  
  gnomad_codons=codon_table%>%
      # filter(Letter%in%alt_aa)%>%
      rowwise()%>%
      mutate(distance=as.numeric(!substr(gnomad_codon,1,1)%in%substr(Codon,1,1))+
               as.numeric(!substr(gnomad_codon,2,2)%in%substr(Codon,2,2))+
               as.numeric(!substr(gnomad_codon,3,3)%in%substr(Codon,3,3)),
             subs_1=case_when(!substr(gnomad_codon,1,1)%in%substr(Codon,1,1)~paste(substr(gnomad_codon,1,1),">",substr(Codon,1,1),sep = ""),
                              T~""),
             subs_2=case_when(!substr(gnomad_codon,2,2)%in%substr(Codon,2,2)~paste(substr(gnomad_codon,2,2),">",substr(Codon,2,2),sep = ""),
                              T~""),
             subs_3=case_when(!substr(gnomad_codon,3,3)%in%substr(Codon,3,3)~paste(substr(gnomad_codon,3,3),">",substr(Codon,3,3),sep = ""),
                              T~""))%>%
      ungroup()
  
  gnomad_codons=gnomad_codons%>%filter(distance%in%1)
  gnomad_unique=sort(unique(gnomad_codons$Letter))
  sort(unique(gnomad_codons$Letter))
  # Figuring out which gnomad amino acids are unique (i.e. not possible by refseq codon)
  gnomad_unique_norefseq=gnomad_unique[!sort(unique(gnomad_codons$Letter))%in%sort(unique(refseq_codons$Letter))]
  return(gnomad_unique_norefseq)
  
}

FUNCTION 2 ####Reading and processing the reference txt and csv file

# #############For ABL#############
enst="ENST00000318560" #Ensembl transcript ID for the canonical transcript
chr=9 #The chromosome number on hg38. This argument is passed into the ensembl variant effect predictor
ref_txt_name="data/Refs/ABL/NM_005157.6_CDS.txt"
ref_csv_name="data/Refs/ABL/abl1_NM_005157.6_full_coordinates.csv"
mode="local"
ref_offset=0


#Reading the full sequence.
reference_seq=read.table(ref_txt_name)
reference_seq=as.character(reference_seq)

# substr(reference_seq,1,2)
#Ref_search is a function that returns the sequence of the cDNA, given start and stop coordinates
ref_search=function(start,stop){
  seachresult=substr(reference_seq,start,stop-1)
  seachresult
}

ref_genomic_coordinates=read.csv(ref_csv_name,header = T,stringsAsFactors = F)
# ref_genomic_coordinates=ref_genomic_coordinates%>%dplyr::select(-X)

####Adding sequence to each codon###
ref_genomic_coordinates=ref_genomic_coordinates%>%mutate(start=pos,end=start+3)%>%dplyr::select(!pos)
ref_genomic_coordinates=ref_genomic_coordinates%>%filter(!resi%in%NA)
ref_genomic_coordinates=ref_genomic_coordinates%>%
  rowwise()%>%
  mutate(codon=ref_search(start-ref_offset,end-ref_offset))
ref_genomic_coordinates=ref_genomic_coordinates%>%filter(!codon%in%"")

MAIN GNOMAD ANALYSIS

# rm(list=ls())
# gnomad=read.csv("data/Gnomad_ABL/gnomAD_v2.1.1_ENSG00000097007_2023_03_03_02_21_35.csv")
gnomad=read.csv("data/Gnomad_ABL/gnomAD_v3.1.2_ENSG00000097007_2023_03_03_14_02_24.csv")
# 1100 Gnomad SNPS total
gnomad=gnomad%>%filter(Position>=130835447,Position<=130884388)
# Half of the SNPS are within the ABL kinase
gnomad=gnomad%>%filter(!VEP.Annotation%in%c("intron_variant","frameshift_variant"))
# Half of the SNPS in the kinase are non-intron non-synonymous
gnomad=gnomad%>%rowwise()%>%mutate(residue=substr(gsub("p\\.","",Protein.Consequence),4,6),
                         residue=as.numeric(gsub("([0-9]+).*$", "\\1", residue)))

gnomad=gnomad%>%filter(!residue%in%NA,residue<=516)

# Therefore, there are 184 gnomad variants in the in the SH3 or SH2 or Kinase domain of ABL
# 84 out of the 184 gnomad variants are in the kinase domain of ABL
# Within the gnomad variants in the kinase, only 10 are allele frequency > 1 in 100,000
a=gnomad%>%filter(!ClinVar.Clinical.Significance%in%"")
a=gnomad%>%group_by(ClinVar.Clinical.Significance)%>%summarize(ct=n())
# Of the 184 ABL SNPs, 152 have no clinvar annotation, 12 are benign, 14 are likely benign
# 9 are uncertain significance
a=gnomad%>%filter(residue>=242,residue<=516)
a=a%>%arrange(desc(Allele.Count))
a=a%>%select(Protein.Consequence,VEP.Annotation,ClinVar.Clinical.Significance,Allele.Frequency,Allele.Count,Allele.Number)
# write.csv(a,"gnomad_abl_kinase_coding_mutants.csv")
b=a%>%filter(!VEP.Annotation%in%"synonymous_variant",!ClinVar.Clinical.Significance%in%"")
# There are 7 non-synonymous clinvar variants in the kinase domain in which there is a clinical annotation. Two of these are high frequency: R473Q/G (not provided clinvar significance), and K247R (benign). Both of these are neutral in our screen
data=read.csv("output/ABLEnrichmentScreens/IL3_Enrichment_bgmerged_2.20.23.csv",header = T,stringsAsFactors = F)
c=data%>%filter(protein_start%in%c(247,473))
#Of the clinvar variants 

# source("code/variantcaller/vep_fromquery.R")
# b=vep_fromquery("130863033:130863036/TAC",9,"ENST00000318560")
# sort(unique(gnomad$VEP.Annotation))
# a=gnomad%>%filter(!Transcript%in%"ENST00000318560.5")
# a=gnomad%>%filter(!Transcript%in%"ENST00000372348.2")
# sort(unique(gnomad$Transcript))
# sort(unique(gnomad$VEP.Annotation))

# ABL kinase Spans from 130835447 to 130884388

#Aim is to create a list of gnomad enabled mutants, i.e. mutants that would have required multi-nucleotide variants before, but are now a single nucleotide away
# First: Make a function that calculates all possible amino acid substitutions for a given ref codon at a hamming distance of 1.
#A key pessimistic question to ask is: for any given residue, how many amino acid substitution types are only possible with two nucleotide substitutions? If basically every amino acid susbtitution is possible with a single nucleotide change, then it doesn't really matter if you make more possible with MNVs.
gnomad_simple=gnomad%>%select(Position,Reference,Alternate,Protein.Consequence,residue,Transcript.Consequence,VEP.Annotation,Allele.Count)
gnomad_simple=gnomad_simple%>%
  rowwise()%>%
  mutate(residue=substr(gsub("p\\.","",Protein.Consequence),4,6),
         residue=as.numeric(gsub("([0-9]+).*$", "\\1", residue)),
         position=as.numeric(gsub("([0-9]+).*$", "\\1",strsplit(Transcript.Consequence,"\\.")[[1]][2])))

gnomad_simple=gnomad_simple%>%
  # rowwise()%>%
  mutate(ref=ref_search(position-ref_offset,position+1-ref_offset))

#The following set of functions takes a position, ref, and and alt nucleotide, figures out which codon was mutated for each mutant, which index within the codon, and makes the alt_codon based on the new index 
# Alt offset: If alt_offset is 1, the mutation happens at position 1 in the codon, and so on
# Please note that a lot of this code relies on there being a single nucleotide substitution, i.e. it ignores mnvs and frameshifting indels. If frameshifts are included, then you have to think about variants that have long alt_offset lengths (not just 1 or 2 or 3).
# If its position 1: paste ALT with Second two Nucleotides
# If its position 2: paste first nucleotide, ALT nucleotide, and third nucleotide
# If it's position 3: paste second two nucleotides with the ALT nucleotide
gnomad_simple=gnomad_simple%>%
  rowwise()%>%
  mutate(ref_codon=(ref_genomic_coordinates[(position-ref_genomic_coordinates$start)%in%c(0,1,2),"codon"])[[1]],
         position_codon_end=(ref_genomic_coordinates[(position-ref_genomic_coordinates$start)%in%c(0,1,2),"end"])[[1]],
         alt_offset=position_codon_end-position,
         alt_codon=case_when(alt_offset%in%3~paste(Alternate,substr(ref_codon,2,3),sep=""),
                             alt_offset%in%1~paste(substr(ref_codon,1,2),Alternate,sep = ""),
                             alt_offset%in%2~paste(substr(ref_codon,1,1),Alternate,substr(ref_codon,3,3),sep = "")))
# ref_genomic_coordinates(130835462)
# ref_genomic_coordinates$start

gnomad_simple=gnomad_simple%>%dplyr::rename("refseq_codon"="ref_codon",
                                            "gnomad_codon"="alt_codon")

a=gnomad_simple%>%filter(refseq_codon%in%gnomad_codon)

########## Next figure out all unique gnomad codons by ABL residue.
# If a given residue has no gnomad SNPs, then there are no unique gnomad codons of course
#   What if a given position has two gnomad codons: figure out all possible gnomad codons and which ones are unique
# i=1
# gnomad_simple$gnomad_enabled_AAs=NA
for(i in seq(1:nrow(gnomad_simple))){
  # gnomad_bypos=gnomad_simple%>%filter(residue%in%residue_i)
  # i=1
  # i=168
  # 167, 168
  gnomad_byiter=gnomad_simple[i,]
  resi_i=gnomad_byiter$residue
  enabled_i=gnomad_unique_AAs(gnomad_byiter$refseq_codon,gnomad_byiter$gnomad_codon)
  if(length(enabled_i)==0){
    # i.e. if there are no unique gnomad AAs, then skip to the next iteration in the loop
    next
  }
  # a=gnomad_simple%>%group_by(residue)%>%summarize(ct=n())
  # gnomad_simple[i,"gnomad_enabled_AAs"]=as.list(gnomad_unique_AAs(gnomad_byiter$refseq_codon,gnomad_byiter$gnomad_codon))
  gnomad_enabled_i=data.frame(enabled_i,resi_i)
  if(i==1){
    gnomad_enabled=gnomad_enabled_i
  }
  else if(!i==1){
    gnomad_enabled=rbind(gnomad_enabled,gnomad_enabled_i)
  }
}

gnomad_enabled=gnomad_enabled%>%distinct(enabled_i,resi_i)
gnomad_enabled=gnomad_enabled%>%dplyr::select(protein_start=resi_i,alt_aa=enabled_i)
gnomad_enabled$gnomad_enabled=T
# a=gnomad_enabled%>%fi lter(protein_start%in%c(242:494))
# There are a total of 166 gnomad enabled ABL mutants. We see 72 of these mutants in our imatinib screens
# I have the gnomad enabled alternate amino acid by residue, but it might also be nice ot get a residue specific gnomad probability.
gnomad_pos_probabilities=gnomad%>%
  group_by(residue)%>%
  summarize(Allele.Count.Total=sum(Allele.Count),
                                         Allele.Number.Total=sum(Allele.Number))%>%
  mutate(Allele.Frequency.Gnomad=Allele.Count.Total/Allele.Number.Total)%>%
  # dplyr::select(-c("Allele.Count.Total","Allele.Number.Total"))%>%
  rename(protein_start=residue)

gnomad_enabled=merge(gnomad_enabled,gnomad_pos_probabilities,by=c("protein_start"),all.x = T)

########## Then merge with Imatinib dataset by adding a "gnomad enabled" flag to the IL3 dataset###########
# New CRISPR-DS imatinib data (conducted at 300nM, 600nM, 1200nM)
imatdata=read.csv("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ABLfullkinase_allconditions_growthrates_1.2.23.csv",header = T,stringsAsFactors = F)
imatdata=imatdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(c(netgr.imat.high.1,netgr.imat.high.2)))

imatdata=imatdata%>%filter(!(species=="R332M"& alt =="ATG"),!(species=="R460N"& alt =="AAC"))

# Old enzymatic fragmentation imatinib data (conducted at 300nM)
# imatdata=read.csv("output/ABLEnrichmentScreens/Imatinib_Enrichment_2.20.23_v2.csv",header = T,stringsAsFactors = F)
# imatdata=imatdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs.x,netgr_obs.y))

imatdata=merge(imatdata,gnomad_enabled,by=c("protein_start","alt_aa"),all.x=T)
imatdata[imatdata$gnomad_enabled%in%NA,"gnomad_enabled"]=F

imatdata_gnomad=imatdata%>%filter(gnomad_enabled%in%T)%>%dplyr::select(ref_aa,protein_start,alt_aa,netgr_obs_mean,gnomad_enabled,Allele.Frequency.Gnomad)
# imatdata_gnomad=imatdata_gnomad%>%filter(protein_start%in%c(247,473))

# The following line of code gives you the percentile rankings of the net growth rate distribution
quantile(imatdata$netgr_obs_mean,c(.5,.75,.90))

        50%         75%         90% 
0.006973064 0.016776129 0.026811147 

# The following line of code tells you what the percentile is for a given net growth rate
ecdf(imatdata$netgr_obs_mean)(.0133)

[1] 0.6785258

# write.csv(imatdata_gnomad,"data/Gnomad_ABL/gnomad_enabled_mutants.csv")


########## Then plot the distribtuion of the gnomad scores and figure out which mutants fall at the fringes of that distribution.##########
ggplot(imatdata%>%filter(protein_start%in%c(242:494)),aes(x=netgr_obs_mean,fill=gnomad_enabled))+geom_density()+facet_wrap(~gnomad_enabled)

a=imatdata%>%filter(gnomad_enabled%in%T)
a=imatdata%>%filter(protein_start%in%c(247,473))
# Once I have all the alt amino acids possible, I will figure out how many of these alt amino acids are at a distance of 1
gnomad_unique_AAs("CGA","CAA")

[1] "E" "H" "K"

# 6.1.24 checking if the gnomad enabled mutants are resistant across multiple imatinib contexts
imatdata_gnomad=imatdata%>%filter(gnomad_enabled%in%T)%>%dplyr::select(ref_aa,protein_start,alt_aa,gnomad_enabled,Allele.Frequency.Gnomad,netgr.imat.low.1,netgr.imat.low.2,netgr.imat.mid.1,netgr.imat.mid.2,netgr.imat.high.1,netgr.imat.high.2)
imatdata_gnomad=imatdata_gnomad%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep=""))

imatdata_gnomad=imatdata_gnomad%>%
  rowwise()%>%
  mutate(netgr_average=mean(c(netgr.imat.low.1,
                              netgr.imat.low.2,
                              netgr.imat.mid.1,
                              netgr.imat.mid.2,
                              netgr.imat.high.1,
                              netgr.imat.high.2)))

# imatdata_gnomad=imatdata_gnomad%>%
#   mutate(rank= rank(-netgr_average))

imatdata_gnomad$rank=rank(-imatdata_gnomad$netgr_average)
# class(imatdata_gnomad$netgr_average)

imatdata_gnomad_melt=melt(imatdata_gnomad,id.vars = c("species","ref_aa","protein_start","alt_aa","gnomad_enabled","Allele.Frequency.Gnomad","rank"),measure.vars =c("netgr.imat.low.1","netgr.imat.low.2","netgr.imat.mid.1","netgr.imat.mid.2","netgr.imat.high.1","netgr.imat.high.2"),
                          variable.name = "dose_condition",value.name = "netgr")



library(forcats)

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

imatdata_gnomad_melt <- imatdata_gnomad_melt %>%
  mutate(species = fct_reorder(species, -rank, .desc = TRUE))

ggplot(imatdata_gnomad_melt,aes(x=species,y=netgr,color=dose_condition))+
  geom_point()

netgr_average=imatdata_gnomad%>%dplyr::select(species,netgr_average,rank)

ggplot(imatdata_gnomad_melt%>%arrange(desc(rank)),aes(x=species,y=netgr,label=species))+
  geom_boxplot()+
  geom_text(data = netgr_average, aes(x = species, y = netgr_average, label = species),
            position = position_nudge(y = 0.02), # Adjust the position if needed
            size = 2, color = "red",
            angle=90)+
  scale_x_discrete("SNP-enabled ABL mutant")+
  scale_y_continuous("Net growth rate (Hrs ^-1)")+
  cleanup+
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/gnomad_enabled_mutants.pdf",width=8,height = 4, units="in",useDingbats=F)

imatdata_gnomad <- imatdata_gnomad %>%
  mutate(species = fct_reorder(species, -rank, .desc = TRUE))
# imatdata_gnomad=imatdata_gnomad%>%arrange(desc(rank))

ggplot(imatdata_gnomad%>%arrange(desc(rank)),aes(x=reorder(species, -netgr_average),y=netgr_average,label=species,size=Allele.Frequency.Gnomad))+
  geom_point(color="black",fill="gray90",shape=21)+
  geom_text(data = netgr_average, aes(x = species, y = netgr_average, label = species),
            position = position_nudge(y = 0.02), # Adjust the position if needed
            size = 2, color = "red",
            angle=90)+
  scale_x_discrete("SNP-enabled ABL mutant")+
  scale_y_continuous("Net growth rate (Hrs ^-1)")+
  cleanup+
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none")

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/gnomad_enabled_mutants_penetrance.pdf",width=8,height = 4, units="in",useDingbats=F)


a=gnomad%>%filter(Position%in%"130872897")

# Looking at Clinvar mutants
library(stringr)
clinvar=read.csv("data/Clinvar_ABL/clinvar_result.csv",header = T)
clinvar=clinvar%>%filter(!Protein.change%in%"")
clinvar=clinvar%>%rowwise()%>%mutate(Protein.change=strsplit(Protein.change,",")[[1]][1],
                                     ref_aa=substr(Protein.change,1,1),
                                     residue=as.numeric(gsub("([0-9]+).*$", "\\1", sub('.', '', Protein.change))),
                                     alt_aa=str_sub(Protein.change,-1,-1))


# clinvar=clinvar%>%filter(residue>=242,residue<=510)
# therefore, out of the 226 clinvar mutants, 63 are present inside the ABL kinase and 163 are outside the kinase
clinvar=clinvar%>%filter(residue>=242,residue<=510)

clinvar=clinvar%>%mutate(Clinical.significance..Last.reviewed.=
                           strsplit(Clinical.significance..Last.reviewed.,"\\(")[[1]][1])
a=clinvar%>%group_by(Clinical.significance..Last.reviewed.)%>%summarise(ct=n())

b=clinvar%>%filter(Clinical.significance..Last.reviewed.%in%"Uncertain significance")
# There are 15 clinvar variants of unknown significance
# write.csv(b,"clinvar_vus_mutants.csv")

Looking at ALL BCRABL mutants detected at pre-treatment according to short et al

shortdata=read.csv("data/Short_et_al_fig1/short_et_al_3.12.23.csv",header = T,stringsAsFactors = F)
shortdata=shortdata%>%filter(!Classification%in%c("Silent","Nonsense"))
shortdata=shortdata%>%mutate(ref_aa=str_sub(Species,1,1),
                             protein_start=str_sub(Species,2,4),
                             alt_aa=str_sub(Species,-1,-1),
                             short_mutant=T)%>%
  dplyr::select(-c("Index","Classification","ref_aa"))


imatdata=read.csv("output/ABLEnrichmentScreens/Imatinib_Enrichment_2.20.23_v2.csv",header = T,stringsAsFactors = F)
imatdata=read.csv("output/ABLEnrichmentScreens/IL3_Enrichment_bgmerged_2.20.23.csv",header = T,stringsAsFactors = F)
# imatdata=imatdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs.x,netgr_obs.y))
imatdata=imatdata%>%rowwise()%>%mutate(netgr_obs_mean=mean(netgr_obs_screen1,netgr_obs_screen2))
imatdata=cosmic_data_adder(imatdata)

imatdata=merge(imatdata,shortdata,by=c("protein_start","alt_aa"),all=T)
imatdata[imatdata$short_mutant%in%NA,"short_mutant"]=F


plotly=ggplot(imatdata%>%filter(protein_start>=242,protein_start<=494,n_nuc_min%in%1),aes(x=reorder(species,-netgr_obs_mean),y=netgr_obs_mean,fill=short_mutant))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name="Net Growth Rate")+scale_x_discrete(name="Mutant")+scale_fill_manual(name="Detection Status",labels=c("Not detected","Detected"),values=c("gray","orange"))+cleanup+theme(legend.position = "none")
ggplotly(plotly)

b=imatdata%>%filter(!netgr_obs_mean%in%NA)
a=imatdata%>%filter(short_mutant%in%T,!netgr_obs_mean%in%NA,cosmic_present%in%F,resmuts%in%F)
a=imatdata%>%filter(short_mutant%in%T,!netgr_obs_mean%in%NA)

a=shortdata%>%filter(protein_start%in%c(242:494))
median(a$netgr_obs_mean)

NULL

ggplot(imatdata%>%filter(!species%in%"T315I",protein_start>=242,protein_start<=494,n_nuc_min%in%1,netgr_obs_mean>=-.02),aes(x=short_mutant,y=netgr_obs_mean,fill=short_mutant))+
  geom_violin(color="black")+
  geom_boxplot(color="black",width=.1)+
  # geom_boxplot(color="black")+
  scale_fill_manual(values=c("gray90","orange"))+theme(legend.position = "none")+scale_y_continuous("Net Growth Rate")+scale_x_discrete("Short et al Ph+ ALL Status",labels=c("Not Detected","Detected"))

# t.test(a$netgr_obs_mean,b$netgr_obs_mean)

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] forcats_0.5.1      reshape2_1.4.4     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] 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         plyr_1.8.6        lifecycle_1.0.0  
[21] munsell_0.5.0     gtable_0.3.0      workflowr_1.6.2   htmlwidgets_1.5.1
[25] codetools_0.2-16  evaluate_0.14     labeling_0.3      knitr_1.28       
[29] fastmap_1.1.0     crosstalk_1.1.0.1 httpuv_1.5.2      fansi_0.4.1      
[33] Rcpp_1.0.4.6      promises_1.1.0    backports_1.1.7   scales_1.1.1     
[37] jsonlite_1.7.2    farver_2.0.3      fs_1.4.1          digest_0.6.25    
[41] stringi_1.7.5     rprojroot_1.3-2   grid_4.0.0        tools_4.0.0      
[45] magrittr_2.0.1    sass_0.4.1        lazyeval_0.2.2    tibble_3.1.2     
[49] crayon_1.4.1      whisker_0.4       tidyr_1.1.3       pkgconfig_2.0.3  
[53] ellipsis_0.3.2    data.table_1.14.8 assertthat_0.2.1  rmarkdown_2.14   
[57] httr_1.4.2        R6_2.4.1          git2r_0.27.1      compiler_4.0.0