Last updated: 2023-02-13

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Knit directory: duplex_sequencing_screen/

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File Version Author Date Message
Rmd fa7baf1 haiderinam 2022-12-15 Updates to the variant caller
Rmd cec2e31 haiderinam 2022-12-08 Added error rate analyses
html cec2e31 haiderinam 2022-12-08 Added error rate analyses 
source("code/merge_samples.R")

12.8.22

lane14sortedsamples=read.csv("data/Consensus_Data/novogene_lane15/sample_3/ngs/variants_unique_ann.csv")

# lane14sortedsamples=merge_samples("Novogene_lane14/sample10_combined/sscs","Novogene_lane14/sample11/sscs")
lane14sortedsamples=lane14sortedsamples%>%
  rowwise()%>%
  mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
         alt_aa=strsplit(amino_acids,"/")[[1]][2])
lane14sortedsamples=lane14sortedsamples%>%mutate(maf=ct/depth)
lane14sorted_simple=lane14sortedsamples%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
lane14sorted_simple=lane14sorted_simple%>%
  mutate(error_status=case_when(protein_start%in%c(1:241,495:700)~T,
         T~F))
plotly=ggplot(lane14sorted_simple%>%
         filter(protein_start>=99,protein_start<=650,ct>=3)%>%
         mutate(mutant=paste(protein_start,alt_aa)),aes(x=protein_start,y=maf))+
  geom_bar(aes(fill=error_status),stat="sum")+
  scale_fill_manual(values = c("blue","red"))+
  theme_bw()+
  theme(legend.position = "none")
ggplotly(plotly)
# library(ggplot2)

# il3D0.D2$alt_aa=factor(il3D0.D2$alt_aa,levels=c("P","G","Y","W","F","V","L","I","A","T","S","Q","N","M","C","E","D","R","K","H"))

ggplot(lane14sorted_simple%>%filter(nchar(as.character(alt_aa))%in%1,protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=ct))+
  geom_tile()+
  theme(panel.background=element_rect(fill="gray", colour="black"))+
  scale_fill_gradient2(low ="darkblue", high ="red",name="MAF")+
  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")
Warning: Removed 13 rows containing missing values (geom_tile).

a=lane14sorted_simple%>%filter(nchar(as.character(alt_aa))%in%1,protein_start>=242,protein_start<=494,consequence_terms%in%"missense_variant")

Now I’m going to look at how much reduction in error rates we get with duplex sequencing

ngs=read.csv(file = "data/Consensus_Data/Novogene_lane15/sample_3/ngs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
ngs$consensus="NGS"

sscs=read.csv(file = "data/Consensus_Data/Novogene_lane15/sample_3/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sscs$consensus="SSCS"

duplex=read.csv(file = "data/Consensus_Data/Novogene_lane15/sample_3/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
duplex$consensus="Duplex"
ngs=ngs%>%dplyr::select(-c("impact","polyphen_prediction","codons"))
sscs=sscs%>%dplyr::select(-c("ref_codon","alt_codon","frame_pos","ref_aa","alt_aa"))
duplex=duplex%>%dplyr::select(-c("ref_codon","alt_codon","frame_pos","ref_aa","alt_aa"))
sample10_duplex=rbind(ngs,sscs,duplex)

# sample10_duplex=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample10_combined/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)

sample10_duplex=sample10_duplex%>%
  rowwise()%>%
  mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
         alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample10_duplex=sample10_duplex%>%mutate(maf=ct/depth)
sample10_duplex_simple=sample10_duplex%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf,consensus)

sample10_duplex_simple=sample10_duplex_simple%>%
  mutate(error_status=case_when(protein_start%in%c(1:241,495:700)~T,
         T~F))

plotly=ggplot(sample10_duplex_simple%>%
         filter(protein_start>=99,protein_start<=600)%>%
         mutate(mutant=paste(protein_start,alt_aa)),aes(x=protein_start,y=maf,color=error_status))+
  geom_col()+
  scale_color_manual(values = c("red","blue"))+
  theme_bw()+
  theme(legend.position = "none")
ggplotly(plotly)
sample10_duplex_simple$consensus=factor(sample10_duplex_simple$consensus,levels=c("NGS","SSCS","Duplex"))
plotly=ggplot(sample10_duplex_simple%>%
         filter(protein_start>=118,protein_start<=600,!protein_start%in%c(411,493,321,417),ct>=2)%>%
         mutate(mutant=paste(protein_start,alt_aa)),aes(x=protein_start,y=maf,color=error_status))+
  geom_col(aes(position="dodge"))+
  facet_wrap(~consensus,ncol=1)+
  scale_color_manual(values = c("blue","red"))+
  theme_bw()+
  scale_x_continuous(name="Position on ABL")+
  scale_y_continuous(limits=c(0,.01),name="MAF")+
  theme(legend.position = "none")
Warning: Ignoring unknown aesthetics: position
ggplotly(plotly)
Warning: Removed 6 rows containing missing values (position_stack).
Warning: `group_by_()` was deprecated in dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help
ggplot(sample10_duplex_simple%>%
         filter(protein_start>=118,protein_start<=600,!protein_start%in%c(411,493,321,417),ct>=2)%>%
         mutate(mutant=paste(protein_start,alt_aa)),aes(x=protein_start,y=maf,color=error_status))+
  geom_col(aes(position="dodge"))+
  facet_wrap(~consensus,ncol=1)+
  scale_color_manual(values = c("blue","red"))+
  theme_bw()+
  scale_x_continuous(name="Position on ABL")+
  scale_y_continuous(limits=c(0,.01),name="MAF")+
  theme(legend.position = "none")
Warning: Ignoring unknown aesthetics: position
Warning: Removed 6 rows containing missing values (position_stack).
Warning: Removed 164 rows containing missing values (geom_col).

# ggsave("errorrates.pdf",width=8,height=8,units="in",useDingbats=F)

Analyzing the TP53 data for errors (aka unintended codons)

tp53_data=read.csv("data/TP53data/41588_2018_204_MOESM5_ESM.csv",header = T,stringsAsFactors = F)
# Some summary from Data:
unintended_snvs=tp53_data%>%filter(Nucleotide_Changes_from_WT%in%1,Variant_group%in%"Unintended")
intended_snvs=tp53_data%>%filter(Nucleotide_Changes_from_WT%in%1,Variant_group%in%"IntendedVtCodon")
mean(unintended_snvs$TP53_MITE_Entry_Library)
[1] 27.77768
median(unintended_snvs$TP53_MITE_Entry_Library)
[1] 24
mean(intended_snvs$TP53_MITE_Entry_Library)
[1] 52.38124
median(intended_snvs$TP53_MITE_Entry_Library)
[1] 44
#   Mean and median of MITE Entry library SNV:

#     Unintended: 27/24
#     Intended: 52/44

mean(unintended_snvs$A549_p53WT_Nutlin.3_Experiment_1)
[1] 685.6124
median(unintended_snvs$A549_p53WT_Nutlin.3_Experiment_1)
[1] 356.5
mean(intended_snvs$A549_p53WT_Nutlin.3_Experiment_1)
[1] 1217.127
median(intended_snvs$A549_p53WT_Nutlin.3_Experiment_1)
[1] 695
#   Mean and median of MITE Nutlin Treated library SNV:
#     Unintended: 685/356
#     Intended: 1217/695




#########Untreated A549s experiment 1#########
ggplot(tp53_data%>%
                filter(Nucleotide_Changes_from_WT%in%1,!Variant_group%in%"IntendedWtCodon",TP53_MITE_Expression_Library<=200000),aes(x=TP53_MITE_Expression_Library,fill=Variant_group))+
  geom_density(alpha=.7)+
  scale_x_continuous(trans="log10",name="Number of Times a Variant was Seen")+
  scale_y_continuous(name="Density")+
  scale_fill_manual(values=c("blue","red"))+
  cleanup+
  theme(legend.position = "none")

# ggsave("tp53_untreated.pdf",width=4,height=3,units="in",useDingbats=F)

#########Nutlin-3 Treated A549s experiment 1#########
ggplot(tp53_data%>%
                filter(Nucleotide_Changes_from_WT%in%1,!Variant_group%in%"IntendedWtCodon",A549_p53WT_Nutlin.3_Experiment_1<=20000),aes(x=A549_p53WT_Nutlin.3_Experiment_1,fill=Variant_group))+
  geom_density(alpha=.7)+
  scale_x_continuous(trans="log10",name="Number of Times a Variant was Seen")+
  scale_y_continuous(name="Density")+
  scale_fill_manual(values=c("blue","red"))+
  cleanup+
  theme(legend.position = "none")

# ggsave("tp53_treated.pdf",width=4,height=3,units="in",useDingbats=F)

sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

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

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.31         bslib_0.3.1       purrr_0.3.4      
 [5] colorspace_1.4-1  vctrs_0.3.8       generics_0.0.2    htmltools_0.5.2  
 [9] viridisLite_0.3.0 yaml_2.2.1        utf8_1.1.4        rlang_0.4.11     
[13] jquerylib_0.1.4   later_1.0.0       pillar_1.6.1      glue_1.4.1       
[17] withr_2.4.2       DBI_1.1.0         lifecycle_1.0.0   munsell_0.5.0    
[21] gtable_0.3.0      workflowr_1.6.2   htmlwidgets_1.5.1 codetools_0.2-16 
[25] evaluate_0.14     labeling_0.3      knitr_1.28        fastmap_1.1.0    
[29] crosstalk_1.1.0.1 httpuv_1.5.2      fansi_0.4.1       Rcpp_1.0.4.6     
[33] promises_1.1.0    backports_1.1.7   scales_1.1.1      jsonlite_1.7.2   
[37] farver_2.0.3      fs_1.4.1          digest_0.6.25     stringi_1.7.5    
[41] rprojroot_1.3-2   grid_4.0.0        tools_4.0.0       magrittr_2.0.1   
[45] sass_0.4.1        lazyeval_0.2.2    tibble_3.1.2      crayon_1.4.1     
[49] whisker_0.4       tidyr_1.1.3       pkgconfig_2.0.3   ellipsis_0.3.2   
[53] data.table_1.12.8 assertthat_0.2.1  rmarkdown_2.14    httr_1.4.2       
[57] R6_2.4.1          git2r_0.27.1      compiler_4.0.0