Last updated: 2022-11-01
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Knit directory: duplex_sequencing_screen/
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Rmd | 9a33fee | haiderinam | 2022-11-01 | wflow_publish("analysis/DupSeq_QC_Lane11.Rmd") |
Rmd | d358fe3 | haiderinam | 2022-10-29 | SSCS Error Analyses |
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In the following chunks, I’m going to be doing some QC with the duplex sequencing data from novogene lane 11 This sequencing run had both EGFR and ABL data. The libraries are twist libraries.
I’m going to be looking closely at the following:
* Count distributions of EGFR vs ABL * How much coverage do we get with
a single sample of EGFR vs with two samples? Answer: 100k for 1 sample,
200k for two SSCS samples * What is the % on-target? * What is the % N?
* What is the % of split reads? * What is the % mouse?
####Novogene Lane 12####
sample1=read.csv("data/Consensus_Data/Novogene_lane12/sample1/variant_caller_outputs/variants_unique_ann.csv")
sample1$sample="sample1"
sample3=read.csv("data/Consensus_Data/Novogene_lane12/sample3/variant_caller_outputs/variants_unique_ann.csv")
sample3$sample="sample3"
sample5=read.csv("data/Consensus_Data/Novogene_lane12/sample5/variant_caller_outputs/variants_unique_ann.csv")
sample5_all=read.csv("data/Consensus_Data/Novogene_lane12/sample5/variant_caller_outputs/variants_ann.csv")
sample5$sample="sample5"
sample7=read.csv("data/Consensus_Data/Novogene_lane12/sample7/variant_caller_outputs/variants_unique_ann.csv")
sample7$sample="sample7"
sample9=read.csv("data/Consensus_Data/Novogene_lane12/sample9/variant_caller_outputs/variants_unique_ann.csv")
sample9$sample="sample9"
sample3=sample3%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample3=sample3%>%mutate(maf=ct/depth)
sample3_simple=sample3%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample5=sample5%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample5=sample5%>%mutate(maf=ct/depth)
sample5_simple=sample5%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_merged=merge(sample3_simple%>%filter(consequence_terms%in%"missense_variant"),sample5_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_merged=samples_merged%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
# a=samples_merged%>%filter(!ct.x%in%1)
samples_merged=samples_merged%>%mutate(resmuts=case_when(protein_start%in%253&alt_aa%in%"H"~T,
protein_start%in%255&alt_aa%in%"V"~T,
protein_start%in%486&alt_aa%in%"S"~T,
protein_start%in%396&alt_aa%in%"P"~T,
protein_start%in%255&alt_aa%in%"K"~T,
protein_start%in%315&alt_aa%in%"I"~T,
protein_start%in%252&alt_aa%in%"H"~T,
protein_start%in%253&alt_aa%in%"F"~T,
protein_start%in%250&alt_aa%in%"E"~T,
protein_start%in%359&alt_aa%in%"C"~T,
protein_start%in%351&alt_aa%in%"T"~T,
protein_start%in%355&alt_aa%in%"G"~T,
protein_start%in%317&alt_aa%in%"L"~T,
protein_start%in%359&alt_aa%in%"I"~T,
protein_start%in%355&alt_aa%in%"A"~T,
protein_start%in%459&alt_aa%in%"K"~T,
protein_start%in%276&alt_aa%in%"G"~T,
protein_start%in%299&alt_aa%in%"L"~T,
T~F))
ggplot(samples_merged%>%filter(!ct.x%in%1),aes(x=protein_start,y=score,color=resmuts))+geom_point()
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
# ggplot(samples_merged%>%filter(!ct.x%in%1),aes(x=protein_start,y=score2))+geom_point()
# sample3=sample3%>%rowwise()%>%mutate(a=c(protein_start,alt_aa))
#
# a=sample5_all%>%filter(protein_start==255,amino_acids%in%"E/K")
# sort(unique(a$codons))
#
# resmuts=sample5%>%filter(protein_start%in%c())
sample1=sample1%>%mutate(region=case_when(protein_start<250&protein_start>=242~1,
protein_start<258&protein_start>=250~2,
protein_start<266&protein_start>=258~3,
protein_start<274&protein_start>=266~4,
protein_start<282&protein_start>=274~5,
protein_start<290&protein_start>=282~6,
protein_start<298&protein_start>=290~7,
protein_start<306&protein_start>=298~8,
protein_start<314&protein_start>=306~9,
protein_start<322&protein_start>=314~10,
protein_start<330&protein_start>=322~11,
protein_start<338&protein_start>=330~12,
protein_start<346&protein_start>=338~13,
protein_start<354&protein_start>=346~14,
protein_start<362&protein_start>=354~15,
protein_start<370&protein_start>=362~16,
protein_start<378&protein_start>=370~17,
protein_start<386&protein_start>=378~18,
protein_start<394&protein_start>=386~19,
protein_start<402&protein_start>=394~20,
protein_start<410&protein_start>=402~21,
protein_start<418&protein_start>=410~22,
protein_start<426&protein_start>=418~23,
protein_start<434&protein_start>=426~24,
protein_start<442&protein_start>=434~25,
protein_start<450&protein_start>=442~26,
protein_start<458&protein_start>=450~27,
protein_start<466&protein_start>=458~28,
protein_start<474&protein_start>=466~29,
protein_start<482&protein_start>=474~30,
protein_start<490&protein_start>=482~31,
protein_start<498&protein_start>=490~32,
T~0))
library(RColorBrewer)
getPalette = colorRampPalette(brewer.pal(33, "Set2"))
Warning in brewer.pal(33, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
sample1=sample1%>%rowwise()%>%mutate(ID=paste(protein_start,amino_acids,sep=""))
plotly=ggplot(sample1%>%filter(consequence_terms%in%"missense_variant",protein_start>=242,protein_start<=494),aes(x=protein_start,y=ct))+geom_col(color="black",aes(fill=factor(region)))+cleanup+scale_fill_manual(values=getPalette(33))
ggplotly(plotly)
calls_sum=sample1%>%filter(consequence_terms%in%"missense_variant")%>%group_by(protein_start,region)%>%summarize(unique_mutants=n(),count=sum(ct))
`summarise()` has grouped output by 'protein_start'. You can override using the `.groups` argument.
# calls_sum=calls_sum
ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=unique_mutants))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
plotly=ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col(color="black",aes(fill=factor(region)))+cleanup+scale_fill_manual(values=getPalette(33))
ggplotly(plotly)
###The following plot is proof that I pooled in our iL3 independent regions very well.
calls_sum_byregion=calls_sum%>%group_by(region)%>%summarize(unique_regions=n(),counts=sum(count))
getPalette = colorRampPalette(brewer.pal(33, "Set2"))
Warning in brewer.pal(33, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
plotly=ggplot(calls_sum_byregion%>%filter(!region%in%0),aes(x=region,y=counts))+geom_col(color="black",aes(fill=factor(region)))+cleanup+scale_fill_manual(values=getPalette(33))
ggplotly(plotly)
highscore=samples_merged%>%filter(!ct.x%in%1,protein_start>=242,protein_start<=494)
highscore=highscore%>%mutate(subs_name=paste(ref_aa,protein_start,alt_aa,sep = ""))
plotly=ggplot(highscore,aes(x=reorder(subs_name,-score),y=score,fill=resmuts))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+coord_flip()
ggplotly(plotly)
a=highscore%>%filter(protein_start%in%"255")
####Novogene Lane 11####
Sample 6
# getwd()
# sample5_all=read.csv("data/Consensus_Data/Novogene_lane11/sample5/variants_ann_sample5.csv")
sample5_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample5/variant_caller_outputs/variants_unique_ann_sample5.csv")
# sample5_all=read.csv("data/Consensus_Data/Novogene_lane11/sample5/variants_ann_sample5.csv")
# a=sample5_all%>%filter(protein_start%in%903,ref%in%"T",alt%in%"C")
# sample6_all=read.csv("data/Consensus_Data/Novogene_lane11/sample6/variants_ann_sample5&6.csv")
sample6_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample6/variant_caller_outputs/variants_unique_ann_sample6.csv")
sample5_simple=sample5_sum%>%dplyr::select(alt_start_pos,protein_start,ref,alt,consequence_terms,amino_acids,ct,depth)
sample5_simple$sample="sample5"
sample6_simple=sample6_sum%>%dplyr::select(alt_start_pos,protein_start,ref,alt,consequence_terms,amino_acids,ct,depth)
sample6_simple$sample="sample6"
sample56=rbind(sample5_simple,sample6_simple)
# a=sample56%>%filter(protein_start>=715,protein_start<=900,consequence_terms%in%"missense_variant")
plotly=ggplot(sample56%>%filter(protein_start>=715,protein_start<=900,consequence_terms%in%"missense_variant"),aes(x=protein_start,y=depth,fill=sample))+geom_col(position=position_dodge())+facet_wrap(~sample)+cleanup
ggplotly(plotly)
Warning: `group_by_()` was deprecated in dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help
sample56_merged=merge(sample5_simple,sample6_simple,by=c("alt_start_pos","protein_start","ref","alt","consequence_terms","amino_acids"),all.x = T)
# a=sample56_merged%>%filter(depth.x>=1000,protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant")
sample56_merged[sample56_merged$ct.y%in%NA,10]=1000
sample56_merged[sample56_merged$depth.y%in%NA,11]=1000
# sample56_merged=sample56_merged%>%
# rowwise()%>%
# mutate(ct.y=case_when(ct.y%in%NA~0,
# T~ct.y))
ggplot(sample56_merged%>%filter(depth.x>=1000,protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant"),aes(x=ct.x/depth.x,y=ct.y/depth.y))+
geom_point(color="black",shape=21,aes(fill=log10(ct.x)))+
scale_x_continuous(trans="log10")+
scale_y_continuous(trans="log10")+
cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
plotly=ggplot(sample5_sum%>%filter(protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant",ct<=50,type%in%"mnv"),aes(x=ct))+
geom_histogram(aes(y=..density..),color=1,fill="white",bins=50)+
geom_density(lwd = 1, colour = 4,fill = 4, alpha = 0.25)
ggplotly(plotly)
plotly=ggplot(sample6_sum%>%filter(protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant",ct<=50,type%in%"mnv"),aes(x=ct))+
geom_histogram(aes(y=..density..),color=1,fill="white",bins=50)+
geom_density(lwd = 1, colour = 4,fill = 4, alpha = 0.25)
# scale_x_continuous(trans="log10")
ggplotly(plotly)
# a=sample5_sum%>%filter(protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant",ct==1)
# nrow(a%>%filter(type%in%"mnv"))
# b=sample6_sum%>%filter(protein_start>=715,protein_start<=870,consequence_terms%in%"missense_variant",ct==1)
# nrow(b%>%filter(type%in%"mnv"))
#For sample 5, out of the mutants that are only seen once, 34% are mnvs (a lot of SNP errors). So a lot of the mutants that we are seeing at a coverage of 1 are false. This means that for sample 5, the distribution of mutants is centered >1
#For sample 6, out of the mutants that are only seen once, 62% are mnvs (low SNP errors)
calls_sum=sample5_sum%>%filter(consequence_terms%in%"missense_variant")%>%group_by(protein_start)%>%summarize(unique_mutants=n(),count=sum(ct))
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870),aes(x=protein_start,y=unique_mutants))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870),aes(x=protein_start,y=count))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
calls_sum=sample6_sum%>%filter(consequence_terms%in%"missense_variant")%>%group_by(protein_start)%>%summarize(unique_mutants=n(),count=sum(ct))
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870),aes(x=protein_start,y=unique_mutants))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870),aes(x=protein_start,y=count))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870,!protein_start%in%861),aes(x=protein_start,y=count))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
ggplot(calls_sum%>%filter(protein_start>=715,protein_start<=870,!protein_start%in%c(861,791)),aes(x=protein_start,y=count))+geom_col()+cleanup
Version | Author | Date |
---|---|---|
097ad61 | haiderinam | 2022-09-29 |
# a=sample5_sum%>%filter(consequence_terms%in%"missense_variant",protein_start>=715,protein_start<=870)
# sum(a$ct)
Sample 6
calls_missense=calls_missense%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(calls_missense,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=(ct/depth)))+
scale_x_continuous(name="Position along EGFR Kinase",expand=c(0,0),limits=c(717,870))+
scale_y_discrete(name="Amino Acid Substitution",expand=c(0,0))+
scale_fill_continuous(name="Allele \nFrequency",trans="log10")+
cleanup+
theme(legend.position = "none")
# a=calls_missense%>%filter(protein_start>=715,protein_start<=880)
# ggsave("egfr_heatmap_sample5.pdf",width=6,height=4,units = "in",useDingbats=F)
ggplot(calls_missense_sum,aes(x=protein_start,y=count))+geom_col()+scale_x_continuous(limits=c(710,875))
SAMPLE 2
library("plotly")
sample2_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample2/archive/variants_unique_ann_sample2.csv",header = T,stringsAsFactors = F)[-1]
sample2_sum$consensus="sscs"
sample2_sum=sample2_sum%>%
# filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample2_all=read.csv("data/Consensus_Data/Novogene_lane11/sample2/archive/variants_ann_sample2.csv",header = T,stringsAsFactors = F)[-1]
sample2_all$consensus="sscs"
dcs_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample2/archive/duplex_sum_ann.csv",header = T,stringsAsFactors = F)[-1]
dcs_sum$consensus="dcs"
sample2_all=sample2_all%>%
# filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
# a=sample2_all%>%filter(protein_start%in%493,amino_acids%in%"F")
sample2_sum
dcs_sum=dcs_sum%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(sample2_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
scale_x_continuous(expand=c(0,0),limits=c(242,333))+
scale_y_discrete(expand=c(0,0))
all_sum=rbind(sample2_sum,dcs_sum)
plotly=ggplot(all_sum,aes(x=ct,fill=consensus,alpha=0.5))+
geom_density(position=position_dodge(),bins=100)+
scale_x_continuous(limits=c(0,100))
# scale_x_continuous(trans="log10")
ggplotly(plotly)
plotly=ggplot(all_sum,aes(x=ct,fill=consensus,alpha=0.5))+
geom_density(position=position_dodge(),bins=100)+
# scale_x_continuous(limits=c(0,100))+
scale_x_continuous(trans="log10")
ggplotly(plotly)
ggplot(all_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
facet_grid(~consensus)+
scale_x_continuous(expand=c(0,0),limits=c(241,333))+
scale_y_discrete(expand=c(0,0))+
scale_fill_gradient(na.value = "black",low="red",high="blue",name="Count")
# a=sscs_sum%>%filter(protein_start>=242,protein_start<=289)
# a=a%>%group_by(protein_start)%>%summarize(count=n())
calls_sum=sample2_sum%>%filter(consequence_terms%in%"missense_variant")%>%group_by(protein_start)%>%summarize(unique_mutants=n(),count=sum(ct))
ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col()+cleanup
ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=unique_mutants))+geom_col()+cleanup
# a=sample2_sum%>%filter(consequence_terms%in%"missense_variant",protein_start>=242,protein_start<=500)
# sum(a$ct)
Lane 11 SAMPLE 10 and 11
###########Sample 10############
sscs_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample10/sscs_sum_ann.csv",header = T,stringsAsFactors = F)[-1]
sscs_sum=sscs_sum%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(sscs_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
scale_x_continuous(expand=c(0,0),limits=c(242,330))+
scale_y_discrete(expand=c(0,0))
###########Sample 8############
sscs_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample8/sscs_sum_ann.csv",header = T,stringsAsFactors = F)[-1]
sscs_sum=sscs_sum%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(sscs_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
scale_x_continuous(expand=c(0,0),limits=c(242,330))+
scale_y_discrete(expand=c(0,0))
Lane 11 SAMPLE 7
###########Sample 10############
sscs_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample7/archive/sscs_sum_ann.csv",header = T,stringsAsFactors = F)[-1]
sscs_sum=sscs_sum%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(sscs_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
# scale_x_continuous(expand=c(0,0),limits=c(242,330))+
scale_x_continuous(expand=c(0,0))+
scale_y_discrete(expand=c(0,0))
###########Sample 8############
sscs_sum=read.csv("data/Consensus_Data/Novogene_lane11/sample8/sscs_sum_ann.csv",header = T,stringsAsFactors = F)[-1]
sscs_sum=sscs_sum%>%
filter(protein_start==protein_end,consequence_terms%in%"missense_variant")%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
ggplot(sscs_sum,aes(x=protein_start,y=alt_aa))+
geom_tile(color="black",aes(fill=ct))+
scale_x_continuous(expand=c(0,0),limits=c(242,330))+
scale_y_discrete(expand=c(0,0))
Plotting the correlation of allele frequencies of stuff that I see in sample 2 vs in sample 7
samplex=read.csv("data/Consensus_Data/Novogene_lane14/sample15/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
samplex=samplex%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
samplex=samplex%>%mutate(maf=ct/depth)
samplex_simple=samplex%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sampley=read.csv("data/Consensus_Data/Novogene_lane14/sample17/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sampley=sampley%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sampley=sampley%>%mutate(maf=ct/depth)
sampley_simple=sampley%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_xy=merge(samplex_simple%>%filter(consequence_terms%in%"missense_variant"),sampley_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"),all = T)
plotly=ggplot(samples_xy%>%filter(protein_start>=242,protein_start<=492),aes(x=maf.x,y=maf.y))+
geom_point(color="black",shape=21,aes(fill=ct.x))+
scale_x_continuous(trans="log")+
scale_y_continuous(trans="log")+
geom_abline()+
theme_bw()
ggplotly(plotly)
Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: number
of items to replace is not a multiple of replacement length
# cor(samples_xy$maf.x,samples_xy$maf.y)
source("code/merge_samples.R")
lane12sortedsamples=merge_samples("Novogene_lane11/Sample1","Novogene_lane11/Sample2")
lane12sortedsamples=merge_samples(lane12sortedsamples,"Novogene_lane11/Sample3")
lane12sortedsamples=merge_samples(lane12sortedsamples,"Novogene_lane11/Sample4")
lane12sortedsamples=lane12sortedsamples%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
lane12sortedsamples=lane12sortedsamples%>%mutate(maf=ct/depth)
# samplex_simple=samplex%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
# sampley=sampley%>%
# rowwise()%>%
# mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
# alt_aa=strsplit(amino_acids,"/")[[1]][2])
# sampley=sampley%>%mutate(maf=ct/depth)
# sampley_simple=sampley%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
ggplot(lane12sortedsamples%>%
filter(nchar(alt_aa)%in%1,
consequence_terms%in%"missense_variant",
protein_start>=242,
protein_start<=282),
aes(x=protein_start,y=alt_aa,fill=maf))+
geom_tile()+
theme_bw()
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
ggplot(lane12sortedsamples)
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
#To do: error plot where red lines outside mutagenized rgn
#plot how many variants do you see with 1 sample vs 2 vs 3 etc
# a=lane12sortedsamples%>%
# filter(nchar(alt_aa)%in%1,
# consequence_terms%in%"missense_variant",
# protein_start>=242,
# protein_start<300)
qc_data=read.csv("data/QC_Data_Cloning/TwistQC-ABL252residues-Q-146958.csv",header = T,stringsAsFactors = F)
qc_data=qc_data%>%filter(!AA.Position%in%NA)
ggplot(qc_data,aes(x=AA.Position,y=variant_aa,fill=variant_maf))+
geom_tile()+
theme_bw()
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
qc_data_formerge=qc_data%>%filter(AA.Position<60)
lane12sorted_formerge=lane12sortedsamples%>%filter(nchar(alt_aa)%in%1,
consequence_terms%in%"missense_variant",
protein_start>=242,
protein_start<302)%>%
dplyr::select(wt_aa=ref_aa,
variant_aa=alt_aa,
protein_start,
variant_maf=maf,
ct)%>%
mutate(AA.Position=protein_start-241)
qc_data_merged=merge(qc_data,lane12sorted_formerge,by=c("AA.Position","wt_aa","variant_aa"),all.x = T)
qc_data_merged$detected="Yes"
qc_data_merged[qc_data_merged$variant_maf.y%in%NA,"detected"]="No"
qc_data_merged[qc_data_merged$variant_maf.y%in%NA,"variant_maf.y"]=1e-5
# With this plot, we can see that most of the variants are within 4 fold of each other in both the twist library and with our librayr
qc_data_merged[qc_data_merged$ct%in%NA,"ct"]=0
qc_data_merged$counts="0"
qc_data_merged[qc_data_merged$ct%in%1,"counts"]="1"
qc_data_merged[qc_data_merged$ct%in%2,"counts"]="2"
qc_data_merged[qc_data_merged$ct%in%3,"counts"]="3"
qc_data_merged[qc_data_merged$ct>=4,"counts"]=">3"
plotly=ggplot(qc_data_merged,aes(x=variant_maf.x,y=variant_maf.y,color=ct))+
geom_point()+
scale_x_continuous(trans="log2")+
scale_y_continuous(trans="log2")+
theme_bw()
ggplotly(plotly)
Warning: Transformation introduced infinite values in continuous x-axis
plotly=ggplot(qc_data_merged,aes(x=variant_maf.x,y=variant_maf.y))+
geom_point(color="black",shape=21,aes(fill=counts))+
scale_x_continuous(trans="log2")+
scale_y_continuous(trans="log2")+
theme_bw()
ggplotly(plotly)
Warning: Transformation introduced infinite values in continuous x-axis
a=qc_data_merged%>%filter(AA.Position<=49,detected%in%"No")
#With the following plot, we can see that of the variants that we don't see in our library, they're not the low AF samples.
ggplot(qc_data_merged%>%filter(AA.Position<=49),aes(x=detected,y=variant_maf.x,fill=detected))+
geom_violin()+
scale_y_continuous(trans="log10")+
theme_bw()+
geom_jitter(shape=16, position=position_jitter(0.2))
Version | Author | Date |
---|---|---|
d358fe3 | haiderinam | 2022-10-29 |
# ggplot(qc_data_merged%>%filter(variant_maf.x>0),aes(x=variant_maf.x,y=variant_maf.y))+
# stat_density_2d(aes(fill=..level..),geom = "polygon", colour="white")+
# scale_fill_continuous(type="viridis")+
# theme_bw()+
# scale_x_continuous(trans="log")+
# scale_y_continuous(trans="log")
# a=qc_data_merged%>%filter(ct>=10)
cor(qc_data_merged$variant_maf.x,qc_data_merged$variant_maf.y)
[1] 0.122601
# cor(a$variant_maf.x,a$variant_maf.y)
#Numbers: Between AAs 242 and 291, twist made 909 QC-confirmed mutants, we detected 839, or 92% of those. #Numbers: Between AAs 242 and 291, twist made 999 mutants overall (including QC-failed mutants), we detected 839, or 82% of those.
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