ABL_cosmic_analysis_fullkinase

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-04-29

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: 52c5709

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 52c5709. 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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    Ignored:    output/ABLEnrichmentScreens/ABL_Region234_Comparisons/before_r4_filtering_3.24.24/.DS_Store
    Ignored:    output/Twinstrand/ABL1AppOutput/.DS_Store
    Ignored:    shinyapp/.DS_Store

Untracked files:
    Untracked:  ImatinibEnrichment_Netgr_Distribution_zoom.pdf
    Untracked:  analysis/ABL_cosmic_analysis_fullkinase.Rmd
    Untracked:  analysis/figure/
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ABLfullkinase_allconditions_growthrates_3.25.24.csv
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ImatinibEnrichment_Netgr_Distribution_zoom.pdf
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ImatinibEnrichment_Plot_netgr_4.28.24.pdf
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ImatinibEnrichment_Plot_netgr_onesitesonly_4.28.24.pdf
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_1200nM_score_medianadjusted.pdf
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/heatmaps_before_F359C/
    Untracked:  output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/

Unstaged changes:
    Modified:   .DS_Store
    Modified:   analysis/4_7_20_update.Rmd
    Modified:   analysis/ABL_cosmic_analysis.Rmd
    Modified:   analysis/region234plottingc0de.Rmd
    Modified:   data/.DS_Store
    Modified:   data/Consensus_Data/.DS_Store
    Modified:   output/.DS_Store
    Modified:   output/ABLEnrichmentScreens/.DS_Store
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    Modified:   output/ABLEnrichmentScreens/ABL_Region1_Lane18_Comparisons/cross-replicate/.DS_Store
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ABLfullkinase_allconditions_growthrates_1.2.23.csv
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_il3_heatmap.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_il3_heatmap_score.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_1200nM.pdf
    Deleted:    output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_1200nM_score.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_300nM.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_300nM_score.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_600nM.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/baf3_imatinib_heatmap_600nM_score.pdf
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/il3_combined_counts_1.2.23.csv
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/imatinib_1200nM_combined_counts_1.2.23.csv
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/imatinib_300nM_combined_counts_1.2.23.csv
    Modified:   output/ABLEnrichmentScreens/ABL_Region234_Comparisons/imatinib_600nM_combined_counts_1.2.23.csv

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.

---

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)
  )
)

ABL_cosmic_analysis had a comparison of sanger data vs pooled screening data FOR REGION 1 ONLY

This chunk of code is pretty similar to ABL_cosmic_analysis, except that the comparisons are done across the full ABL kinase (residues 242-512)

Seeing how well our imatinib resistance mutants work with cosmic (sanger institute) mutants

Unseen mutants: This code also checks whether single nucleotide substitution mutants that are strong hits for imatinib resistance in our screens are not seen in the Sanger database because they are just low probability mutants. Short answer: we don’t see a lot of evidence that resistant mutants are unseen in the sanger database because of their low probability

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

Checking region 2 3 4 plots

#######################Inputting Data#######################
data=read.csv("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/ABLfullkinase_allconditions_growthrates_1.2.23.csv",header = T,stringsAsFactors = F)
data=data%>%rowwise()%>%mutate(netgr_mean=mean(netgr.imat.high.1,netgr.imat.high.1),
                               score_mean=mean(score.imat.high.1,score.imat.high.1))
# class(data)
data=resmuts_adder(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=netgr_obs_mean)
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"))

#######################Waterfall plots#######################

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"))

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

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

#######################Scatter Plots#######################


# Scatterplot with all mutants in the library
ggplot(data%>%filter(!species%in%c("V299L"),protein_start>=242,protein_start<=494)%>%arrange(resmuts),aes(x=netgr.imat.high.1,y=netgr.imat.high.2))+
  geom_point(color="black",shape=21,aes(fill=factor(resmut_cosmic)))+
  # geom_text_repel(aes(label=ifelse(resmuts%in%T,species,"")))+
  geom_label_repel(size=2,aes(label=ifelse(resmuts%in%T,species,"")))+
  geom_abline(linetype="dashed")+
  scale_x_continuous(name="Net Growth Rate Rep A",limits=c(-.025,.06))+
  scale_y_continuous(name="Net Growth Rate Rep B",limits=c(-.025,.06))+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))+
  coord_equal()+
  cleanup+theme(legend.position = "none")

Warning: Removed 30 rows containing missing values (geom_point).

Warning: Removed 30 rows containing missing values (geom_label_repel).

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/ImatinibEnrichment_Plot_netgr_4.28.24.pdf",width=4,height=4,units="in",useDingbats=F)

# Scatterplot with only single nucleotide (i.e. likely) substitutions.
# Notice how there seem to be less "false positives" for resistance in this plot vs the first plot
ggplot(data%>%filter(!species%in%c("V299L"),protein_start>=242,protein_start<=494,n_nuc_min%in%1)%>%arrange(resmuts),aes(x=netgr.imat.high.1,y=netgr.imat.high.2))+
  geom_point(color="black",shape=21,aes(fill=factor(resmut_cosmic)))+
  geom_label_repel(size=2,aes(label=ifelse(resmuts%in%T,species,"")))+
  geom_abline(linetype="dashed")+
  scale_x_continuous(name="Net Growth Rate Rep A",limits=c(-.025,.06))+
  scale_y_continuous(name="Net Growth Rate Rep B",limits=c(-.025,.06))+
  labs(fill="Sanger\n Mutant")+
  scale_fill_manual(values=c("gray90","orange","red"))+
  coord_equal()+
  cleanup+theme(legend.position = "none")

Warning: Removed 6 rows containing missing values (geom_point).

Warning: Removed 6 rows containing missing values (geom_label_repel).

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/ImatinibEnrichment_Plot_netgr_onesitesonly_4.28.24.pdf",width=4,height=4,units="in",useDingbats=F)

# Plotly plot for visualizing which mutants are present
plotly=ggplot(data%>%filter(protein_start>=242,protein_start<=494),aes(x=netgr.imat.high.1,y=netgr.imat.high.2,label=species,color=resmuts))+
  geom_text(size=3)+
  # 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")
ggplotly(plotly)

#######################Boxplots#######################
ggplot(data%>%filter(!species%in%c("V299L"),protein_start>=242,protein_start<=494),aes(x=resmut_cosmic,y=netgr_mean,fill=resmut_cosmic))+
  # geom_violin(color="black")+
  geom_boxplot(color="black")+
  # geom_jitter(color="black", size=.5,width=.1, alpha=0.9)+
  scale_fill_manual(values=c("gray90","orange","red"))+
  scale_y_continuous(name=bquote('Net Growth Rate '(Hours^-1)))+
  scale_x_discrete("Resistance Status",labels=c("Never\n Seen","Rare\n VUDR","Known\n Resistant\n Variant"))+
  cleanup+
  theme(legend.position = "none",axis.title.x = element_blank())

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/ImatinibEnrichment_netgr_BoxPlot_4.28.24.pdf",width=2.5,height=3,units="in",useDingbats=F)


#######################Distribution plots#######################
ggplot(data%>%filter(!species%in%c("V299L"),protein_start>=242,protein_start<=512),aes(x=netgr_mean,fill=resmuts))+
  geom_density(alpha=.7)+
  scale_x_continuous(bquote('Net Growth Rate '(Hours^-1)))+
  scale_y_continuous("Density")+
  scale_fill_manual(values=c("gray","red"))+
  cleanup+
  theme(legend.position = "none")

#######################Heatmaps#######################
heatmap_plotting_function(data%>%mutate(netgr_mean=case_when(netgr_mean>=.055~.055,
                                                                 netgr_mean<=0~0,
                                                    T~netgr_mean)),242,494,fill_variable = "netgr_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,494),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=.03, high ="red",name="Net growth rate")+theme(legend.position = "none")

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

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

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/baf3_imatinib_heatmap_1200nM_score_medianadjusted.pdf",width=10,height = 4,units = "in",useDingbats=F)


# Heatmap with legend for adobe illustrator
heatmap_plotting_function(data%>%mutate(netgr_mean=case_when(netgr_mean>=.055~.055,
                                                                 netgr_mean<=0~0,
                                                    T~netgr_mean)),242,494,fill_variable = "netgr_mean",fill_name = "Net growth rate")+
  scale_x_continuous(name="Residue on the ABL Kinase",limits=c(242,494),expand=c(0,0))+
  # scale_fill_gradient2(low ="darkblue",mid = "white",midpoint=0.04, high ="red",name="Net growth rate")+
  # scale_fill_gradient(low ="lightblue", high ="red",name="Net growth rate")+
  scale_fill_gradient2(low="blue",mid ="#9999FF",midpoint=.03, high ="red",name="Net growth rate")

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

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/baf3_imatinib_heatmap_1200nM_score_medianadjusted_withlegend.pdf",width=12,height = 4,units = "in",useDingbats=F)

Pooled vs spike-in: Comparing the fitness of pooled mutants to mutant standards

library("ggpubr")

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

###Downloading mutation bias data. Source of this data: https://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/tree/master/Figures/Figure3

# compicmut=read.csv("data/Combined_data_frame_IC_Mutprob_abundance.csv",header=T,stringsAsFactors=F)

#############################300 nM Imatinib#############################
standards=read.csv("output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
standards=standards%>%filter(conc%in%0.3)
standards=merge(data,standards,by.x="species",by.y="mutant")

ggplot(standards,aes(x=.055-drug_effect,y=netgr.imat.low.2,label=species))+
  geom_text(size=2)+
  # scale_x_continuous("Predicted Net Growth Rate (Hrs-1) \nClonal IC50",limits=c(-0.01,.06))+
  # scale_y_continuous("Observed Net Growth Rate (Hrs-1) \nDMS Screen",limits=c(-0.01,.06))+
  scale_x_continuous(element_blank(),limits=c(-0.01,.065))+
  scale_y_continuous(element_blank(),limits=c(-0.01,.065))+
  stat_cor(method="pearson")+
  geom_abline(linetype="dashed")+
  cleanup

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/mutantstandards_dms_vs_ic50s_300nM.pdf",width=3,height = 3,units="in",useDingbats=F)

 #############################600 nM Imatinib#############################
standards=read.csv("output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
standards=standards%>%filter(conc%in%0.6)
standards=merge(data,standards,by.x="species",by.y="mutant")

ggplot(standards,aes(x=.055-drug_effect,y=netgr.imat.mid.1,label=species))+
  geom_text(size=2)+
  # scale_x_continuous("Predicted Net Growth Rate (Hrs-1) \nClonal IC50",limits=c(-0.01,.06))+
  # scale_y_continuous("Observed Net Growth Rate (Hrs-1) \nDMS Screen",limits=c(-0.01,.06))+
  scale_x_continuous(element_blank(),limits=c(-0.01,.065))+
  scale_y_continuous(element_blank(),limits=c(-0.01,.065))+
  stat_cor(method="pearson")+
  geom_abline(linetype="dashed")+
  cleanup

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/mutantstandards_dms_vs_ic50s_600nM.pdf",width=3,height = 3,units="in",useDingbats=F)

#############################1200 nM Imatinib#############################
standards=read.csv("output/ic50data_all_conc.csv",header = T,stringsAsFactors = F)
standards=standards%>%filter(conc%in%0.6)
standards=merge(data,standards,by.x="species",by.y="mutant")

ggplot(standards,aes(x=.055-drug_effect,y=netgr.imat.high.1,label=species))+
  # geom_text(size=2)+
  # geom_point()+
  geom_text(size=2)+
  # scale_x_continuous("Predicted Net Growth Rate (Hrs-1) \nClonal IC50",limits=c(-0.01,.06))+
  # scale_y_continuous("Observed Net Growth Rate (Hrs-1) \nDMS Screen",limits=c(-0.01,.06))+
  scale_x_continuous(element_blank(),limits=c(-0.01,.065))+
  scale_y_continuous(element_blank(),limits=c(-0.01,.065))+
  stat_cor(method="pearson")+
  geom_abline(linetype="dashed")+
  cleanup

# ggsave("output/ABLEnrichmentScreens/ABL_Region234_Comparisons/sm_imatinib_plots/mutantstandards_dms_vs_ic50s_1200nM.pdf",width=3,height = 3,units="in",useDingbats=F)

twinstrand_maf_merge=read.csv("output/twinstrand_maf_merge.csv",header = T,stringsAsFactors = F)
twinstrand_maf_merge=twinstrand_maf_merge%>%
  mutate(Spike_in_freq=as.numeric(Spike_in_freq))%>%
  mutate(Spike_in_freq=case_when(experiment=="Enu_4"~2000,
                                 experiment==experiment~Spike_in_freq))%>%
  mutate(actualDepth=Depth*3)%>% #To account for 2 mouse 1 human reads
  mutate(expectedAltDepth=case_when(time_point=="D0"&Spike_in_freq==1000~Depth/1000,
                                    time_point=="D0"&Spike_in_freq==5000~Depth/5000,
                                    time_point=="D0"&Spike_in_freq==2000~Depth/2000,
                                    time_point==time_point~NaN))

a=twinstrand_maf_merge%>%filter(time_point=="D0",experiment%in%c("M3","M6")&tki_resistant_mutation=="True"|experiment%in%"Enu_4",!mutant=="NA",!mutant=="D276G",!mutant=="V280syn")%>%
  mutate(expectedAltDepth=case_when(experiment=="Enu_4"&mutant=="F311L"~expectedAltDepth,
                                   experiment=="Enu_4"&mutant=="T315I"~expectedAltDepth*55,
                                   experiment=="Enu_4"&mutant=="F317L"~expectedAltDepth*6,
                                   experiment=="Enu_4"&mutant=="E355G"~expectedAltDepth*3,
                                   experiment=="Enu_4"&mutant=="F359V"~expectedAltDepth*13,
                                   experiment=="Enu_4"&mutant=="F359C"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="H396P"~expectedAltDepth*17,
                                   experiment=="Enu_4"&mutant=="A397P"~expectedAltDepth*12,
                                   experiment=="Enu_4"&mutant=="Y253H"~expectedAltDepth*63,
                                   experiment=="Enu_4"&mutant=="Q252H"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="G250E"~expectedAltDepth*11,
                                   experiment=="Enu_4"&mutant=="L248V"~expectedAltDepth*6,
                                   experiment=="Enu_4"&mutant=="H214R"~expectedAltDepth*4,
                                   experiment=="Enu_4"&mutant=="K285N"~expectedAltDepth*5,
                                   experiment=="Enu_4"&mutant=="L324R"~expectedAltDepth*7,
                                   mutant==mutant~expectedAltDepth))

twinstrand_simple_melt_merge=read.csv("output/twinstrand_simple_melt_merge.csv",header = T,stringsAsFactors = F)
a=twinstrand_simple_melt_merge%>%
  filter(!experiment%in%c("Enu_4","Enu_3"),duration%in%"d3d6",conc=="0.8")%>%
  mutate(netgr_obs=case_when(experiment=="M5"~netgr_obs+.015,
                                   experiment%in%c("M6","M3","M5","M4","M7")~netgr_obs))

a=a%>%filter(experiment%in%c("M3","M5"),!mutant%in%"D276G")
spikeins_cast=dcast(a,formula = mutant~experiment,value.var = "netgr_obs")


standards=merge(data,spikeins_cast,by.x="species",by.y="mutant")

ggplot(standards,aes(x=M5,y=netgr.imat.mid.1,label=species))+geom_text()

ggplot(standards,aes(x=M3,y=netgr.IL3.1,label=species))+geom_text()

# As you can see, there isn't a strong correlation between the mutant standards measured in DMS DS and in the 20-mutant spike-in screen. Part of the reason is probably the absense of high sensitivity mutants (such as V299L) from the spike-in data

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] ggpubr_0.4.0       MASS_7.3-55        viridisLite_0.3.0  reshape2_1.4.4    
 [5] ggrepel_0.8.2      RColorBrewer_1.1-2 doParallel_1.0.15  iterators_1.0.12  
 [9] foreach_1.5.0      tictoc_1.0         plotly_4.9.2.1     ggplot2_3.3.3     
[13] dplyr_1.0.6        stringr_1.4.0     

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.1        tidyr_1.1.3       jsonlite_1.7.2   
 [5] carData_3.0-3     bslib_0.3.1       assertthat_0.2.1  cellranger_1.1.0 
 [9] yaml_2.2.1        pillar_1.6.1      backports_1.1.7   glue_1.4.1       
[13] digest_0.6.25     promises_1.1.0    ggsignif_0.6.0    colorspace_1.4-1 
[17] htmltools_0.5.2   httpuv_1.5.2      plyr_1.8.6        pkgconfig_2.0.3  
[21] broom_0.7.6       haven_2.4.1       purrr_0.3.4       scales_1.1.1     
[25] openxlsx_4.1.5    later_1.0.0       rio_0.5.16        git2r_0.27.1     
[29] tibble_3.1.2      generics_0.0.2    farver_2.0.3      car_3.0-7        
[33] ellipsis_0.3.2    withr_2.4.2       lazyeval_0.2.2    readxl_1.3.1     
[37] magrittr_2.0.1    crayon_1.4.1      evaluate_0.14     fs_1.4.1         
[41] fansi_0.4.1       rstatix_0.6.0     forcats_0.5.1     foreign_0.8-78   
[45] tools_4.0.0       data.table_1.14.8 hms_1.1.0         lifecycle_1.0.0  
[49] munsell_0.5.0     zip_2.0.4         compiler_4.0.0    jquerylib_0.1.4  
[53] rlang_0.4.11      grid_4.0.0        htmlwidgets_1.5.1 crosstalk_1.1.0.1
[57] labeling_0.3      rmarkdown_2.14    gtable_0.3.0      codetools_0.2-16 
[61] abind_1.4-5       DBI_1.1.0         curl_4.3          R6_2.4.1         
[65] knitr_1.28        fastmap_1.1.0     utf8_1.1.4        workflowr_1.6.2  
[69] rprojroot_1.3-2   stringi_1.7.5     Rcpp_1.0.4.6      vctrs_0.3.8      
[73] tidyselect_1.1.0  xfun_0.31