ABL_sheared_libraries

workflowr

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• 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 is untracked by Git. 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:
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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.

---

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

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This code has some of the analysis done for libraries made using the enzymatic fragrmentation library prep approach (mostly data from lane 11-16)

source("code/merge_samples.R")
source("code/variants_parser.R")
source("code/shortest_codon_finder.R")
source("code/compare_samples.R")
source("code/depth_finder.R")
source("code/resmuts_adder.R")
source("code/res_residues_adder.R")
source("code/plotting/cleanup.R")

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)
  )
)

Function to find the shortest possible ALT codon for a given amino acid substitution

TP3D6=read.csv("data/Consensus_Data/novogene_lane17/sample6/sscs/variant_caller_outputs/variants_unique_ann.csv",header = T,stringsAsFactors = F)
TP3D6=variants_parser(TP3D6,93000)

TP3D2=read.csv("data/Consensus_Data/novogene_lane17/sample7/sscs/variant_caller_outputs/variants_unique_ann.csv",header = T,stringsAsFactors = F)
TP3D2=variants_parser(TP3D2,839)

TP3D2.D6=compare_samples(TP3D2,TP3D6,.055,96)

# TP3D2.D6=TP3D2.D6%>%filter(ct.x>=3)

TP3D2.D6_clinrel=TP3D2.D6%>%filter(n_nuc_min==1)

TP3D2.D6_clinrel=resmuts_adder(TP3D2.D6_clinrel)
TP3D2.D6_clinrel=res_residues_adder(TP3D2.D6_clinrel)

resmuts=TP3D2.D6_clinrel%>%filter(resmuts%in%T)%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))

ic50data_all_sum=read.csv("output/ic50data_all_confidence_intervals_raw_data.csv",row.names = 1)
resmuts_merged=merge(resmuts,ic50data_all_sum,by="species")

ggplot(resmuts_merged,aes(x=netgr_pred_model,y=netgr_obs,label=species))+geom_text()+geom_abline()

ggplot(resmuts_merged,aes(x=netgr_pred_model,y=score,label=species))+geom_text()+geom_abline()

il3D0=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
il3D0=merge_samples(il3D0,"Novogene_lane14/sample11/sscs")
il3D0=merge_samples(il3D0,"Novogene_lane15/sample_3/sscs")
# il3D0=merge_samples(il3D0,"Novogene_lane17/sample1_combined/sscs")

il3D0=variants_parser(il3D0,370)

imatD4=read.csv("data/Consensus_Data/novogene_lane15/sample_7/sscs/variant_caller_outputs/variants_unique_ann.csv",header = T,stringsAsFactors = F)

imatD4=variants_parser(imatD4,10000)

TSIID0.D4=compare_samples(il3D0,imatD4,.055,96)

# TSIID0.D4=TSIID0.D4%>%filter(ct.x>=3)

TSIID0.D4_clinrel=TSIID0.D4%>%filter(n_nuc_min==1)

TSIID0.D4_clinrel=resmuts_adder(TSIID0.D4_clinrel)
TSIID0.D4_clinrel=res_residues_adder(TSIID0.D4_clinrel)

resmuts=TSIID0.D4_clinrel%>%filter(resmuts%in%T)%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))

ic50data_all_sum=read.csv("output/ic50data_all_confidence_intervals_raw_data.csv",row.names = 1)
resmuts_merged=merge(resmuts,ic50data_all_sum,by="species")

ggplot(resmuts_merged,aes(x=netgr_pred_model,y=netgr_obs,label=species))+geom_text()+geom_abline()

ggplot(resmuts_merged%>%filter(!species%in%(c("T315I","F317L"))),aes(x=netgr_pred_model,y=netgr_obs,label=species))+geom_text()+geom_abline()

TSIID0.D4_clinrel_simple=TSIID0.D4_clinrel%>%dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))

TP3D2.D6_clinrel_simple=TP3D2.D6_clinrel%>%dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))

screen_compare=merge(TSIID0.D4_clinrel_simple,
                     TP3D2.D6_clinrel_simple,
                     by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
                     all.x=T,
                     all.y=T)

screen_compare=resmuts_adder(screen_compare)
screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,color=resmuts))+geom_point()+geom_abline()
ggplotly(plotly)

# Side note: check how many of the top mutations are good by cosmic
# Check TP4 vs TP3 agreement. Check 

# Next, I'm going to calculate the weighted mean of the scores across the concomitant vs in series screens
screen_compare_means=screen_compare%>%
  filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)%>%
  rowwise()%>%
  mutate(score_mean=mean(c(score.x,score.y)))

a=screen_compare%>%
  filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)

TSID0.D2 TSID0.D4 TSID0.D6 Comparisons: D0.D2 vs D2 D4 vs D4 D6

Day 0 Day 2 vs Day 0 Day 4

###### TSI Day 0 vs Day 2 #########
delta_t=48 #hours
cells_before=107 #total cells at before time point
cells_after=1515 #total cells at after time point
netgr=.055

# before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane14/sample11/sscs")

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")

# before_timepoint=merge_samples("Novogene_lane15/sample_3/sscs","Novogene_lane13/sample7/sscs")

before_timepoint=variants_parser(before_timepoint,cells_before)
after_timepoint=merge_samples("Novogene_lane13/Sample9/sscs","Novogene_lane13/Sample10/sscs")
after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_4/sscs")

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

before_after=before_after%>%filter(ct.x>=3)
before_after_screen1=before_after


###### TSI Day 0 vs Day 4 #########
delta_t=96 #hours
cells_before=107 #total cells at before time point
cells_after=17239 #total cells at after time point
netgr=.055
# before_timepoint=merge_samples("Novogene_lane15/sample_3/sscs","Novogene_lane13/sample7/sscs")

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")



before_timepoint=variants_parser(before_timepoint,cells_before)

after_timepoint=read.csv("data/Consensus_Data/novogene_lane15/sample_5/sscs/variant_caller_outputs/variants_unique_ann.csv")

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

before_after=before_after%>%filter(ct.x>=3)
before_after_screen2=before_after


before_after_screen1=before_after_screen1%>%
  mutate(ct_screen1=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))


before_after_screen2=before_after_screen2%>%
  mutate(ct_screen2=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))

screen_compare=merge(before_after_screen1,
                     before_after_screen2,
                     by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
                     all.x=T,
                     all.y=T)

screen_compare=resmuts_adder(screen_compare)
screen_compare=res_residues_adder(screen_compare)
screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))


############### Adding Count, Depth, and MAF columns for Ivan 2.20.23###########
# a=before_after_screen1
# b=before_after_screen2
# before_after_screen1=a
# before_after_screen2=b

# before_after_screen1=before_after_screen1%>%
#   mutate(ct_screen1_before=ct.x,
#          depth_screen1_before=depth.x,
#          maf_screen1_before=maf.x,
#          ct_screen1_after=ct.y,
#          depth_screen1_after=depth.y,
#          maf_screen1_after=maf.y)%>%
#   dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))
# 
# before_after_screen2=before_after_screen2%>%
#   mutate(ct_screen2_before=ct.x,
#          depth_screen2_before=depth.x,
#          maf_screen2_before=maf.x,
#          ct_screen2_after=ct.y,
#          depth_screen2_after=depth.y,
#          maf_screen2_after=maf.y)%>%
#   dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))
# 
# screen_compare=merge(before_after_screen1,
#                      before_after_screen2,
#                      by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
#                      all.x=T,
#                      all.y=T,suffixes = c("_screen1","_screen2"))
# 
# screen_compare=resmuts_adder(screen_compare)
# screen_compare=res_residues_adder(screen_compare)
# screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))
# 
# screen_compare_means=screen_compare%>%
#   filter(!score_screen1%in%NA,!score_screen2%in%NA,!score_screen1%in%NaN,!score_screen2%in%NaN)%>%
#   rowwise()%>%
#   mutate(score_mean=mean(c(score_screen1,score_screen2)))

# write.csv(screen_compare_means,"IL3_Enrichment_bgmerged_2.20.23.csv")

##############
plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,color=resmuts))+geom_point()+geom_abline()
ggplotly(plotly)

a=screen_compare%>%filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)
cor(a$score.x,a$score.y)

[1] 0.8203084

screen_compare_means=screen_compare%>%
  filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)%>%
  rowwise()%>%
  mutate(score_mean=mean(c(score.x,score.y)),
         score_weighteed_mean=weighted.mean(c(score.x,score.y),c(ct_screen1,ct_screen2)))

# write.csv(screen_compare_means,"IL3_Enrichment_2.12.23.csv")

ggplot(screen_compare%>%filter(protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score.x))+geom_tile()

screen_compare=screen_compare%>%filter(!score.x%in%c(NA,NaN),!score.y%in%c(NA,NaN))
screen_compare$density=get_density(x = screen_compare$score.x,y=screen_compare$score.y,n=100)
ggplot(screen_compare,aes(x=score.x,y=score.y,color=density))+
  geom_point()+
  scale_color_viridis_c()+
  # geom_jitter(position = position_jitter(width = 0.2, height = 0.2))+
  # scale_x_continuous(name="Score Replicate 1",limits=c(-4,4))+
  # scale_y_continuous(name="Score Replicate 2",limits=c(-4,4))+
  geom_abline(linetype="dashed")+
  theme(legend.position = "none")

# ggsave("il3d0d2.d0d4_score_2.15.23.pdf",width=6,height=6,units="in",useDingbats=F)


a=screen_compare%>%filter(!score.x%in%c(NA,NaN),!score.y%in%c(NA,NaN))
cor(a$score.x,a$score.y)

[1] 0.8203084

# screen_compare$resmuts=factor(screen_compare$resmuts,levels=c("TRUE","FALSE"))
ggplot(screen_compare,aes(x=score.x,y=score.y))+
  geom_point(color="black",shape=21,aes(fill=resmuts))+
  geom_abline()+
  scale_x_continuous(name="Score D0 D2")+
  scale_y_continuous(name="Score D0 D4")+
  labs(fill="Resistant\n Mutant")+
  scale_fill_manual(values=c("gray90","red"))

ggplot(screen_compare,aes(x=score.x,y=score.y))+
  geom_point()+
  geom_abline()+
  scale_x_continuous(name="Score D0 D2")+
  scale_y_continuous(name="Score D0 D4")+
  labs(fill="Resistant\n Mutant")

# ggsave("il3d0d2.d0d4_score_2.15.23.pdf",width=8,height=6,units="in",useDingbats=F)

data=read.csv("IL3_Enrichment_2.12.23.csv",header = T,stringsAsFactors = F)
data=data%>%filter(ct_screen1>=3,ct_screen2>=3)

screen_compare[screen_compare$score.x%in%NA,"score.x"]=-6

#####Graying out unseen residues#####
df_grid  = expand.grid(protein_start = c(242:493),alt_aa = unique(screen_compare$alt_aa))

###Trying to add in the score as WT for residues that are wt
reference_seq=read.table("data/Refs/ABL/abl_cds_translation.txt")
screen_compare=screen_compare%>%
  rowwise()%>%
  # group_by(protein_start)%>%
  mutate(ref_aa=case_when(ref_aa%in%NA~substr(reference_seq,protein_start,protein_start),
                          T~ref_aa))%>%
  mutate(wt=case_when(ref_aa==alt_aa~T,
                      T~F))

##########Plotting gray and yellow heatmap
# screen_compare=screen_compare%>%mutate(score.x)

screen_compare$alt_aa=factor(screen_compare$alt_aa,levels=c("P","G","Y","W","F","V","L","I","A","T","S","Q","N","M","C","E","D","R","K","H"))
screen_compare.filtered=screen_compare%>%filter(!protein_start%in%c(290:305))
# a=il3D0.D4.merge.filtered%>%filter(wt%in%T)
ggplot(screen_compare.filtered,aes(x=protein_start,y=alt_aa))+
  geom_tile(data=subset(screen_compare.filtered,!is.na(score.x)),aes(fill=score.x))+
  scale_fill_gradient2(low ="darkblue",midpoint=.03,mid="white", high ="red",name="Score")+
  geom_tile(data=subset(screen_compare.filtered,is.na(score.x)&wt%in%F),aes(color="white"),linetype = "solid",color="white", fill = "gray90", alpha = 0.8)+
  geom_tile(data=subset(screen_compare.filtered,is.na(score.x)&wt%in%T),aes(color="white"),linetype = "solid",color="white", fill = "yellow", alpha = 0.4)+
  theme(panel.background=element_rect(fill="white", colour="black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0))+
  ylab("Mutant Amino Acid")

Warning: Removed 74 rows containing missing values (geom_tile).

# ggsave("BCRABL_IL3_D0D4_2.12.23.pdf",height=6,width=24,units="in",useDingbats=F)


ggplot(screen_compare.filtered,aes(x=score.x,y=score.y))+geom_point()

IL3 Day 0 Day 4 vs Day 2 Day 6

###### TSI Day 0 vs Day 4 #########
delta_t=96 #hours
cells_before=107 #total cells at before time point
cells_after=17239 #total cells at after time point
netgr=.055

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")

before_timepoint=variants_parser(before_timepoint,cells_before)

after_timepoint=read.csv("data/Consensus_Data/novogene_lane15/sample_5/sscs/variant_caller_outputs/variants_unique_ann.csv")



after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

# before_after=before_after%>%filter(ct.x>=3)
before_after_screen1=before_after


###### TSI Day 2 vs Day 6 #########
delta_t=96 #hours
cells_before=1515 #total cells at before time point
cells_after=430713 #total cells at after time point
netgr=.055
before_timepoint=merge_samples("Novogene_lane13/Sample9/sscs","Novogene_lane13/Sample10/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/Sample_4/sscs")

before_timepoint=variants_parser(before_timepoint,cells_before)

after_timepoint=read.csv("data/Consensus_Data/novogene_lane17/sample2/sscs/variant_caller_outputs/variants_unique_ann.csv")

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

# before_after=before_after%>%filter(ct.x>=3)
before_after_screen2=before_after


before_after_screen1=before_after_screen1%>%
  mutate(ct_screen1=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))


before_after_screen2=before_after_screen2%>%
  mutate(ct_screen2=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))

screen_compare=merge(before_after_screen1,
                     before_after_screen2,
                     by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
                     all.x=T,
                     all.y=T)

screen_compare=resmuts_adder(screen_compare)
screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,color=resmuts))+geom_point()+geom_abline()
ggplotly(plotly)

screen_compare_means=screen_compare%>%
  filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)%>%
  rowwise()%>%
  mutate(score_mean=mean(c(score.x,score.y)),
         score_weighteed_mean=weighted.mean(c(score.x,score.y),c(ct_screen1,ct_screen2)))

# write.csv(screen_compare_means,"IL3_Enrichment_2.8.23.csv")

ggplot(screen_compare%>%filter(protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score.x))+geom_tile()

a=screen_compare%>%filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)
cor(a$score.x,a$score.y)

[1] -0.2418853

screen_compare[screen_compare$score.x%in%NA,"score.x"]=-6

#####Graying out unseen residues#####
df_grid  = expand.grid(protein_start = c(242:493),alt_aa = unique(screen_compare$alt_aa))

###Trying to add in the score as WT for residues that are wt
reference_seq=read.table("data/Refs/ABL/abl_cds_translation.txt")
screen_compare=screen_compare%>%
  rowwise()%>%
  # group_by(protein_start)%>%
  mutate(ref_aa=case_when(ref_aa%in%NA~substr(reference_seq,protein_start,protein_start),
                          T~ref_aa))%>%
  mutate(wt=case_when(ref_aa==alt_aa~T,
                      T~F))

##########Plotting gray and yellow heatmap
# screen_compare=screen_compare%>%mutate(score.x)

screen_compare$alt_aa=factor(screen_compare$alt_aa,levels=c("P","G","Y","W","F","V","L","I","A","T","S","Q","N","M","C","E","D","R","K","H"))
screen_compare.filtered=screen_compare%>%filter(!protein_start%in%c(290:305))
# a=il3D0.D4.merge.filtered%>%filter(wt%in%T)
ggplot(screen_compare.filtered,aes(x=protein_start,y=alt_aa))+
  geom_tile(data=subset(screen_compare.filtered,!is.na(score.x)),aes(fill=score.x))+
  scale_fill_gradient2(low ="darkblue",midpoint=.03,mid="white", high ="red",name="Score")+
  geom_tile(data=subset(screen_compare.filtered,is.na(score.x)&wt%in%F),aes(color="white"),linetype = "solid",color="white", fill = "gray90", alpha = 0.8)+
  geom_tile(data=subset(screen_compare.filtered,is.na(score.x)&wt%in%T),aes(color="white"),linetype = "solid",color="white", fill = "yellow", alpha = 0.4)+
  theme(panel.background=element_rect(fill="white", colour="black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0))+
  ylab("Mutant Amino Acid")

Warning: Removed 363 rows containing missing values (geom_tile).

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

# ggsave("BCRABL_IL3_D0D4_2.8.23.pdf",height=6,width=24,units="in",useDingbats=F)


ggplot(screen_compare.filtered,aes(x=score.x,y=score.y))+geom_point()

Warning: Removed 1215 rows containing missing values (geom_point).

Imatinib Screens Day 0 Day 2 vs Day 0 Day 4

###### TSII Day 0 vs Day 2 #########

delta_t=48 #hours
cells_before=370 #total cells at before time point
cells_after=1192.25 #total cells at after time point
netgr=.055
# before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")
# source()
before_timepoint=variants_parser(before_timepoint,cells_before)

after_timepoint=merge_samples("Novogene_lane15/sample_6/sscs","Novogene_lane14/sample12/sscs")
# after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_4/sscs")
# after_timepoint=read.csv("data/Consensus_Data/novogene_lane15/sample_7/sscs/variant_caller_outputs/variants_unique_ann.csv")

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

before_after=before_after%>%filter(ct.x>=3)
before_after_screen1=before_after


###### TSII Day 0 vs Day 4 #########
delta_t=96 #hours
cells_before=370 #total cells at before time point
cells_after=10397 #total cells at after time point
netgr=.055
# before_timepoint=merge_samples("Novogene_lane15/sample_3/sscs","Novogene_lane13/sample7/sscs")

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")



before_timepoint=variants_parser(before_timepoint,cells_before)

after_timepoint=read.csv("data/Consensus_Data/novogene_lane15/sample_7/sscs/variant_caller_outputs/variants_unique_ann.csv")
# after_timepoint=read.csv("data/Consensus_Data/novogene_lane17/sample3/sscs/variant_caller_outputs/variants_unique_ann.csv")

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

# before_after=before_after%>%filter(ct.x>=3)
before_after_screen2=before_after



############### Adding Count, Depth, and MAF columns for Ivan 2.20.23###########
# a=before_after_screen1
# b=before_after_screen2
# before_after_screen1=a
# before_after_screen2=b

# before_after_screen1=before_after_screen1%>%
#   mutate(ct_screen1_before=ct.x,
#          depth_screen1_before=depth.x,
#          maf_screen1_before=maf.x,
#          ct_screen1_after=ct.y,
#          depth_screen1_after=depth.y,
#          maf_screen1_after=maf.y)%>%
#   dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))
# 
# before_after_screen2=before_after_screen2%>%
#   mutate(ct_screen2_before=ct.x,
#          depth_screen2_before=depth.x,
#          maf_screen2_before=maf.x,
#          ct_screen2_after=ct.y,
#          depth_screen2_after=depth.y,
#          maf_screen2_after=maf.y)%>%
#   dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))
# 
# screen_compare=merge(before_after_screen1,
#                      before_after_screen2,
#                      by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
#                      all.x=T,
#                      all.y=T,suffixes = c("_screen1","_screen2"))
# 
# screen_compare=resmuts_adder(screen_compare)
# screen_compare=res_residues_adder(screen_compare)
# screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))
# 
# screen_compare_means=screen_compare%>%
#   filter(!score_screen1%in%NA,!score_screen2%in%NA,!score_screen1%in%NaN,!score_screen2%in%NaN)%>%
#   rowwise()%>%
#   mutate(score_mean=mean(c(score_screen1,score_screen2)))

# write.csv(screen_compare_means,"Imat_Enrichment_bgmerged_2.22.23.csv")
##############

before_after_screen1=before_after_screen1%>%
  mutate(ct_screen1=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))


before_after_screen2=before_after_screen2%>%
  mutate(ct_screen2=ct.x)%>%
  dplyr::select(-c("ct.x","depth.x","maf.x","totalcells.x","totalmutant.x","ct.y","depth.y","maf.y","totalcells.y","totalmutant.y"))

screen_compare=merge(before_after_screen1,
                     before_after_screen2,
                     by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms","ref_codon","alt_codon","alt_codon_shortest","n_nuc_min"),
                     all.x=T,
                     all.y=T)

screen_compare=resmuts_adder(screen_compare)
screen_compare=screen_compare%>%mutate(species=paste(ref_aa,protein_start,alt_aa,sep = ""))



plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

a=screen_compare%>%filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)
cor(a$score.x,a$score.y)

[1] 0.7306101

screen_compare_means=screen_compare%>%
  filter(!score.x%in%NA,!score.y%in%NA,!score.x%in%NaN,!score.y%in%NaN)%>%
  rowwise()%>%
  mutate(score_mean=mean(c(score.x,score.y)),
         score_weighteed_mean=weighted.mean(c(score.x,score.y),c(ct_screen1,ct_screen2)))

# write.csv(screen_compare_means,"Imatinib_Enrichment_2.20.23_v2.csv")

ggplot(screen_compare%>%filter(protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score.x))+geom_tile()

screen_compare=res_residues_adder(screen_compare)

plotly=ggplot(screen_compare,aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

plotly=ggplot(screen_compare%>%filter(!species%in%"T315I"),aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

ggplot(screen_compare%>%filter(!species%in%"T315I"),aes(x=score.x,y=score.y,label=species,color=resmuts))+geom_text()+geom_abline()+cleanup

Warning: Removed 635 rows containing missing values (geom_text).

plotly=ggplot(screen_compare,aes(x=netgr_obs.x,y=netgr_obs.y,label=species,color=resmuts))+geom_text()+geom_abline()
ggplotly(plotly)

screen_compare=screen_compare%>%filter(!score.x%in%c(NA,NaN),!score.y%in%c(NA,NaN))
screen_compare$density=get_density(x = screen_compare$score.x,y=screen_compare$score.y,n=50)
ggplot(screen_compare,aes(x=netgr_obs.x,y=netgr_obs.y,color=density))+
  geom_point()+
  scale_color_viridis_c()+
  # geom_jitter(position = position_jitter(width = 0.1, height = 0.1))+
  # scale_x_continuous(name="Score Replicate 1",limits=c(-5,5))+
  # scale_y_continuous(name="Score Replicate 2",limits=c(-5,5))+
  geom_abline(linetype="dashed")+
  theme(legend.position = "none")

a=screen_compare%>%filter(!score.x%in%c(NA,NaN),!score.y%in%c(NA,NaN))
cor(a$score.x,a$score.y)

[1] 0.7306101

# cor(a$netgr_obs.x,a$netgr_obs.y)


plotly=ggplot(screen_compare,aes(x=score.x,y=score.y))+
  geom_point(color="black",shape=21,aes(fill=resmuts))+geom_abline()
ggplotly(plotly)

ggplot(screen_compare,aes(x=score.x,y=score.y))+
  geom_point(color="black",shape=21,aes(fill=resmuts))+
  geom_abline()+
  scale_x_continuous(name="Score D0 D2")+
  scale_y_continuous(name="Score D0 D4")+
  labs(fill="Resistant\n Mutant")+
  scale_fill_manual(values=c("gray90","red"))

# ggsave("imatd0d2.d0d4_score_2.15.23.pdf",width=8,height=6,units="in",useDingbats=F)

IL3 Screens for plotting Day 0 Day 2 vs Day 0 Day 4

###### TSII Day 0 vs Day 2 #########

delta_t=48 #hours
cells_before=107 #total cells at before time point
cells_after=1515 #total cells at after time point
netgr=.055

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane16b/Sample9/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane16b/Sample10/sscs")
before_timepoint=variants_parser(before_timepoint,cells_before)

##########TSI Imatinib D2###########
# after_timepoint=merge_samples("Novogene_lane15/sample_6/sscs","Novogene_lane14/sample12/sscs")
# after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_7/sscs")

##########TSI D2###########
after_timepoint=merge_samples("Novogene_lane13/Sample9/sscs","Novogene_lane13/Sample10/sscs")
# after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_4/sscs")

####################

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

# a=before_after%>%filter(protein_start%in%271)

# a=before_after%>%filter(protein_start%in%271)
ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+geom_tile()

#####Focusing on conserved residues#####
ggplot(before_after%>%filter(protein_start%in%c(271,363,381:383)),aes(x=protein_start,y=alt_aa,fill=netgr_obs))+
  geom_tile()+
  theme(panel.background=element_rect(fill="white", colour="black"))+
  scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Enrichment Score")+
  scale_color_manual(values=c("black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0),breaks = c(248,250,256, 271,275,300,325,350,363,375,381,400,405,425,450,475))+
  ylab("Mutant Amino Acid")+
  theme(panel.grid.major.x = element_blank() ,
        panel.grid.major.y = element_blank() )

# ggsave("BCRABL_iL3Independence_D2_essential.pdf",height=6,width=24,units="in",useDingbats=F)



ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+
  geom_tile()+
  theme(panel.background=element_rect(fill="white", colour="black"))+
  scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Enrichment Score")+
  scale_color_manual(values=c("black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0),breaks = c(248,250,256, 271,275,300,325,350,363,375,381,400,405,425,450,475))+
  ylab("Mutant Amino Acid")+
  theme(panel.grid.major.x = element_blank() ,
        panel.grid.major.y = element_blank() )

Warning: Removed 8 rows containing missing values (geom_tile).

# ggsave("BCRABL_iL3Independence_D2.pdf",height=6,width=24,units="in",useDingbats=F)

IMATINIB Screens for plotting Day 0 Day 2 vs Day 0 Day 4

###### TSII Day 0 vs Day 2 #########
source("code/merge_samples.R")
source("code/depth_finder.R")
delta_t=96 #hours
cells_before=370 #total cells at before time point
cells_after=10397 #total cells at after time point
netgr=.055

before_timepoint=merge_samples("Novogene_lane14/Sample10_combined/sscs","Novogene_lane13/sample7/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane14/sample11/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane15/sample_3/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane16b/Sample9/sscs")
before_timepoint=merge_samples(before_timepoint,"Novogene_lane16b/Sample10/sscs")
before_timepoint=variants_parser(before_timepoint,cells_before)

##########TSI Imatinib D2###########
after_timepoint=merge_samples("Novogene_lane15/sample_6/sscs","Novogene_lane14/sample12/sscs")
after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_7/sscs")

##########TSI D2###########
# after_timepoint=merge_samples("Novogene_lane13/Sample9/sscs","Novogene_lane13/Sample10/sscs")
# after_timepoint=merge_samples(after_timepoint,"Novogene_lane15/Sample_4/sscs")

####################

after_timepoint=variants_parser(after_timepoint,cells_after)

before_after=compare_samples(before_timepoint,after_timepoint,netgr,delta_t)

# a=before_after%>%filter(protein_start%in%271)
# a=before_after%>%filter(protein_start%in%426)

# a=before_after%>%filter(resmuts%in%T)
before_after=resmuts_adder(before_after)
before_after=res_residues_adder(before_after)

ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+geom_tile()

#####Focusing on RESISTANT residues#####
ggplot(before_after%>%filter(resmuts%in%T,!protein_start%in%"315"),aes(x=protein_start,y=alt_aa,fill=score))+
  geom_tile()+
  theme(panel.background=element_rect(fill="white", colour="black"))+
  scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Enrichment Score")+
  scale_color_manual(values=c("black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0),breaks=c(250,253,255,276,299,315,317,351,355,359,396,459,486))+
  ylab("Mutant Amino Acid")+
  theme(panel.grid.major.x = element_blank() ,
        panel.grid.minor.x = element_blank() ,
        panel.grid.major.y = element_blank() ,
        panel.grid.minor.y = element_blank() )

# ggsave("BCRABL_imatinib_D2_resistant.pdf",height=6,width=24,units="in",useDingbats=F)



ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+
  geom_tile()+
  theme(panel.background=element_rect(fill="white", colour="black"))+
  scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Enrichment Score")+
  scale_color_manual(values=c("black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0),breaks = c(248,250,256, 271,275,300,325,350,363,375,381,400,405,425,450,475))+
  ylab("Mutant Amino Acid")+
  theme(panel.grid.major.x = element_blank() ,
        panel.grid.minor.x = element_blank() ,
        panel.grid.major.y = element_blank() ,
        panel.grid.minor.y = element_blank() )

Warning: Removed 8 rows containing missing values (geom_tile).

# ggsave("BCRABL_imat_D2.pdf",height=6,width=24,units="in",useDingbats=F)



ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=netgr_obs))+
  geom_tile()+
  theme(panel.background=element_rect(fill="white", colour="black"))+
  scale_fill_gradient2(low ="blue",midpoint=0.035,mid="white", high ="red",name="Net Growth Rate")+
  scale_color_manual(values=c("black"))+scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,493),expand=c(0,0),breaks = c(248,250,256, 271,275,300,325,350,363,375,381,400,405,425,450,475))+
  ylab("Mutant Amino Acid")+
  theme(panel.grid.major.x = element_blank() ,
        panel.grid.minor.x = element_blank() ,
        panel.grid.major.y = element_blank() ,
        panel.grid.minor.y = element_blank())+
  theme_dark()

Warning: Removed 8 rows containing missing values (geom_tile).

a=before_after%>%filter(protein_start>=240,protein_start<=494,!ct.x%in%.5,!ct.y%in%.5)
median(a$netgr_obs)

[1] 0.02824059

ggplot(before_after%>%filter(protein_start>=240,protein_start<=494),aes(x=netgr_obs))+geom_histogram()

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

Warning: Removed 27 rows containing non-finite values (stat_bin).

# ggsave("BCRABL_imat_D2_netgr.pdf",height=6,width=24,units="in",useDingbats=F)

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      tidyr_1.1.3      
[49] pkgconfig_2.0.3   ellipsis_0.3.2    data.table_1.12.8 assertthat_0.2.1 
[53] rmarkdown_2.14    httr_1.4.2        R6_2.4.1          git2r_0.27.1     
[57] compiler_4.0.0