lane14_comparisons
Haider Inam
2022-10-24
Last updated: 2022-12-08
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Knit directory: duplex_sequencing_screen/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a2c3ac3 | haiderinam | 2022-12-08 | wflow_publish("analysis/lane14_comparisons.Rmd") |
| html | c522b6d | haiderinam | 2022-12-08 | Build site. |
| Rmd | 7c384cb | haiderinam | 2022-12-08 | wflow_publish("analysis/lane14_comparisons.Rmd") |
| Rmd | 9612cc9 | haiderinam | 2022-11-22 | Added analyses on mutant enrichment scores in the imatinib background |
| Rmd | accc9f5 | haiderinam | 2022-11-16 | Lane 15 additional sequencing |
| Rmd | 48a63e8 | haiderinam | 2022-11-12 | Lane 15 Analyses |
| Rmd | 3a2f887 | haiderinam | 2022-11-04 | Added analyses of IL3 independence |
| Rmd | 6f54acf | haiderinam | 2022-11-04 | Added analyses of SSCS vs DCS error rates |
| html | 0c8d4d2 | haiderinam | 2022-10-29 | Build site. |
| Rmd | 71f4636 | haiderinam | 2022-10-29 | wflow_publish("analysis/lane14_comparisons.Rmd") |
| Rmd | d358fe3 | haiderinam | 2022-10-29 | SSCS Error Analyses |
| html | d358fe3 | haiderinam | 2022-10-29 | SSCS Error Analyses |
| Rmd | 157f17b | haiderinam | 2022-10-24 | Lane 14 Data Added |
10.24.22 Analysis of Novogene Lane 14 Sequencing Data The sequencing here was done on EGFR il3 indep and ABL il3 indep and imat, asc treated libraries
sample14=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample14_combined/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample14=sample14%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample14=sample14%>%mutate(maf=ct/depth)
sample14_simple=sample14%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample15=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample15/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample15=sample15%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample15=sample15%>%mutate(maf=ct/depth)
sample15_simple=sample15%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample16=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample16/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample16=sample16%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample16=sample16%>%mutate(maf=ct/depth)
sample16_simple=sample16%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample17=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample18/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample17=sample17%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample17=sample17%>%mutate(maf=ct/depth)
sample17_simple=sample17%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample18=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample18/duplex/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample18=sample18%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample18=sample18%>%mutate(maf=ct/depth)
sample18_simple=sample18%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_14.16=merge(sample14_simple%>%filter(consequence_terms%in%"missense_variant"),sample16_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_14.16=samples_14.16%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_14.16_simple=samples_14.16%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
samples_14.18=merge(sample14_simple%>%filter(consequence_terms%in%"missense_variant"),sample18_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_14.18=samples_14.18%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_14.18_simple=samples_14.18%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
asc_scores=merge(samples_14.16_simple,samples_14.18_simple,by=c("ref_aa","protein_start","alt_aa","consequence_terms"))
a=asc_scores%>%filter(protein_start%in%c(337,464,465,468))
samples_14.17=merge(sample14_simple%>%filter(consequence_terms%in%"missense_variant"),sample17_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_14.17=samples_14.17%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_14.17_simple=samples_14.17%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
samples_14.15=merge(sample14_simple%>%filter(consequence_terms%in%"missense_variant"),sample15_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_14.15=samples_14.15%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_14.15_simple=samples_14.15%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
imat_scores=merge(samples_14.15_simple,samples_14.17_simple,by=c("ref_aa","protein_start","alt_aa","consequence_terms"))
ggplot(samples_14.16,aes(x=score))+geom_density()
ggplot(samples_14.16,aes(x=score))+geom_histogram(bins=100)
ggplot(imat_scores,aes(x=score.x,y=score.y))+geom_point()+geom_abline()
ggplot(imat_scores%>%mutate(name=paste(ref_aa,protein_start,alt_aa)),aes(x=score.x,y=score.y,label=name))+geom_text()
# ggplotly(plotly)
ggplot(imat_scores,aes(x=score.x,y=score.y))+geom_bin2d(bins=100)+scale_fill_continuous(type="viridis")+theme_bw()
ggplot(imat_scores,aes(x=score.x,y=score.y))+stat_density_2d(aes(fill=..level..),geom = "polygon", colour="white")+scale_fill_continuous(type="viridis")+theme_bw()+xlab("Enrichment Score Imat Treatment Replicate 1")+ylab("Enrichment Score Imat Treatment Replicate 2")
plotly=ggplot(imat_scores%>%filter(!alt_aa%in%c("PA","NY","LL"),protein_start>=242,protein_start<=492),aes(x=protein_start,y=alt_aa,fill=score.x))+geom_tile()+theme(panel.background=element_rect(fill="white", colour="white"))+scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Score")
ggplotly(plotly)Warning in matrix(g$fill_plotlyDomain, nrow = length(y), ncol = length(x), :
data length [1805] is not a sub-multiple or multiple of the number of rows [20]Warning in matrix(g$hovertext, nrow = length(y), ncol = length(x), byrow =
TRUE): data length [1805] is not a sub-multiple or multiple of the number of
rows [20]# cor(imat_scores$score.x,imat_scores$score.y,method="pearson")
# cor(asc_scores$score.x,asc_scores$score.y,method="pearson")
ggplot(asc_scores,aes(x=score.x,y=score.y))+geom_point()+geom_abline()
ggplot(asc_scores%>%mutate(name=paste(ref_aa,protein_start,alt_aa)),aes(x=score.x,y=score.y,label=name))+geom_text()
# ggplotly(plotly)
ggplot(asc_scores,aes(x=score.x,y=score.y))+geom_bin2d(bins=100)+scale_fill_continuous(type="viridis")+theme_bw()
ggplot(asc_scores,aes(x=score.x,y=score.y))+stat_density_2d(aes(fill=..level..),geom = "polygon", colour="white")+scale_fill_continuous(type="viridis")+theme_bw()+xlab("Enrichment Score Asciminib Treatment Replicate 1")+ylab("Enrichment Score Asciminib Treatment Replicate 2")
plotly=ggplot(asc_scores%>%filter(!alt_aa%in%c("PA","NY","LL"),protein_start>=242,protein_start<=492),aes(x=protein_start,y=alt_aa,fill=score.x))+geom_tile()+theme(panel.background=element_rect(fill="white", colour="white"))+scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Score")
ggplotly(plotly)Warning in matrix(g$fill_plotlyDomain, nrow = length(y), ncol = length(x), :
data length [1786] is not a sub-multiple or multiple of the number of rows [20]Warning in matrix(g$hovertext, nrow = length(y), ncol = length(x), byrow =
TRUE): data length [1786] is not a sub-multiple or multiple of the number of
rows [20]a=asc_scores
# a=samples_14.17%>%filter(ct.x>1)sample14=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample14_combined/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample14=sample14%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample14=sample14%>%mutate(maf=ct/depth)
sample14_simple=sample14%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample1517=merge_samples("Novogene_lane14/sample15/sscs","Novogene_lane14/sample17/sscs")
sample1517=sample1517%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample1517=sample1517%>%mutate(maf=ct/depth)
sample1517_simple=sample1517%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_14.1517=merge(sample14_simple%>%filter(consequence_terms%in%"missense_variant"),sample1517_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"),all.x = T)
samples_14.1517=samples_14.1517%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_14.1517_simple=samples_14.1517%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
samples_14.1517=samples_14.1517%>%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))
samples_14.1517=samples_14.1517%>%mutate(resresids=case_when(protein_start%in%253~T,
protein_start%in%255~T,
protein_start%in%486~T,
protein_start%in%396~T,
protein_start%in%255~T,
protein_start%in%315~T,
protein_start%in%252~T,
protein_start%in%253~T,
protein_start%in%250~T,
protein_start%in%359~T,
protein_start%in%351~T,
protein_start%in%355~T,
protein_start%in%317~T,
protein_start%in%359~T,
protein_start%in%355~T,
protein_start%in%459~T,
protein_start%in%276~T,
protein_start%in%299~T,
T~F))
highscore=samples_14.1517%>%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))+scale_y_continuous(name="Enrithcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))
ggplotly(plotly)Warning: Removed 319 rows containing missing values (position_stack).ggplot(highscore%>%filter(resmuts%in%"TRUE"),aes(x=reorder(subs_name,-score),y=score))+geom_col()+theme_bw()Warning: Removed 2 rows containing missing values (position_stack).
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
ic50data_all_sum=read.csv("output/ic50data_all_confidence_intervals_raw_data.csv",row.names = 1)
resmuts_merged=merge(highscore%>%filter(resmuts%in%"TRUE")%>%dplyr::select(subs_name,score),
ic50data_all_sum%>%dplyr::select(species,netgr_pred_model,netgr_pred_mean),by.x="subs_name",by.y="species")
ggplot(resmuts_merged,aes(x=netgr_pred_mean,y=score,label=subs_name))+geom_text()+theme_bw()+xlab("IC50 Predicted Growth Rates")+ylab("Enrichment Score observed w 5k mutant library \n at 60k sscs at 2 days post 300nM Imatinib")+ggtitle("Imatinib Treatment on iL3 independent 384W library")Warning: Removed 2 rows containing missing values (geom_text).
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
# ggplot(a,aes(x=netgr_pred_mean,y=netgr_pred_model,label=subs_name))+geom_text()
# aa=a%>%filter(!subs_name%in%"D276G")
# # cor(aa$score,aa$netgr_pred_mean)
# a=sample1517%>%filter(protein_start%in%"396")
# b=sample14%>%filter(protein_start%in%"396")
# ggplot(a%>%filter(!subs_name%in%"D276G"),aes(x=netgr_pred_mean,y=score,label=subs_name))+geom_point()+theme_bw()+xlab("IC50 Predicted Growth Rates")+ylab("Enrichment Score observed w 5k mutant library \n at 60k sscs")
# write.csv(highscore,"BCRABL_EnrichmentScores_iL3Independent_Library.csv")
ggplot(resmuts_merged,aes(x=netgr_pred_mean,y=score,label=subs_name))+geom_text()+theme_bw()+xlab("IC50 Predicted Growth Rates")+ylab("Observed Enrichment Score")Warning: Removed 2 rows containing missing values (geom_text).
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
# ggsave("BCRABL_EnrichmentCorrelation.pdf",width=4,height=4,units="in",useDingbats=F)
a=resmuts_merged%>%filter(!score%in%NA)
cor(a$netgr_pred_mean,a$score)[1] 0.6156103a=sample1517%>%filter(protein_start%in%255)Summing counts for the two samples for Asciminib and the two samples for imatinib to see if we can improve number of mutants seen in the dataset
How uneven is the ABL library?
sample14=sample14%>%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))
calls_sum=sample14%>%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.ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col()+cleanup
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 colorsplotly=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)sample11=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample11/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample11=sample11%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample11=sample11%>%mutate(maf=ct/depth)
sample11=sample11%>%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))
calls_sum=sample11%>%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.ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col()+cleanup
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
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 colorsplotly=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)ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500),aes(x=protein_start,y=count))+geom_col()+cleanup
plotly=ggplot(calls_sum%>%filter(protein_start>=242,protein_start<=500,!protein_start%in%c(411,417,455)),aes(x=protein_start,y=count))+geom_col(color="black",aes(fill=factor(region)))+cleanup+scale_fill_manual(values=getPalette(33))
ggplotly(plotly)TSII Sample 11 and 12
sample11=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample11/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample11=sample11%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample11=sample11%>%mutate(maf=ct/depth)
sample11_simple=sample11%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample12=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample12/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample12=sample12%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample12=sample12%>%mutate(maf=ct/depth)
sample12_simple=sample12%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_11.12=merge(sample11_simple%>%filter(consequence_terms%in%"missense_variant"),sample12_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_11.12=samples_11.12%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_11.12_simple=samples_11.12%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
ggplot(samples_11.12,aes(x=score))+geom_density()
ggplot(samples_14.16,aes(x=score))+geom_histogram(bins=100)
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
samples_11.12=samples_11.12%>%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))
samples_11.12=samples_11.12%>%mutate(resresids=case_when(protein_start%in%253~T,
protein_start%in%255~T,
protein_start%in%486~T,
protein_start%in%396~T,
protein_start%in%255~T,
protein_start%in%315~T,
protein_start%in%252~T,
protein_start%in%253~T,
protein_start%in%250~T,
protein_start%in%359~T,
protein_start%in%351~T,
protein_start%in%355~T,
protein_start%in%317~T,
protein_start%in%359~T,
protein_start%in%355~T,
protein_start%in%459~T,
protein_start%in%276~T,
protein_start%in%299~T,
T~F))
highscore=samples_11.12%>%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))+scale_y_continuous(name="Enrithcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))
ggplotly(plotly)plotly=ggplot(highscore%>%filter(ct.y>=2),aes(x=reorder(subs_name,-score),y=score,fill=resresids))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name="Enrithcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))
ggplotly(plotly)ggplot(samples_11.12%>%filter(!alt_aa%in%c("LL","PA"),protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+geom_tile()+theme(panel.background=element_rect(fill="white", colour="white"))+scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Score")
# a=highscore%>%filter(protein_start%in%"255")TSII Sample 10 and 12
source("code/merge_samples.R")
sample10=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample10_combined/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample10=sample10%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample10=sample10%>%mutate(maf=ct/depth)
sample10_simple=sample10%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
sample12=read.csv(file = "data/Consensus_Data/Novogene_lane14/sample12/sscs/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
sample12=sample12%>%
rowwise()%>%
mutate(ref_aa=strsplit(amino_acids,"/")[[1]][1],
alt_aa=strsplit(amino_acids,"/")[[1]][2])
sample12=sample12%>%mutate(maf=ct/depth)
sample12_simple=sample12%>%dplyr::select(alt_start_pos,protein_start,ref,alt,ref_aa,alt_aa,consequence_terms,ct,depth,maf)
samples_10.12=merge(sample10_simple%>%filter(consequence_terms%in%"missense_variant"),sample12_simple%>%filter(consequence_terms%in%"missense_variant"),by=c("ref_aa","protein_start","alt_aa","ref","alt","alt_start_pos","consequence_terms"))
samples_10.12=samples_10.12%>%mutate(score=log2(maf.y/maf.x),score2=log2(ct.y/ct.x))
samples_10.12_simple=samples_10.12%>%dplyr::select(ref_aa,protein_start,alt_aa,consequence_terms,ct.x,score)
ggplot(samples_10.12,aes(x=score))+geom_density()
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
# ggplot(samples_14.16,aes(x=score))+geom_histogram(bins=100)
samples_10.12=samples_10.12%>%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))
samples_10.12=samples_10.12%>%mutate(resresids=case_when(protein_start%in%253~T,
protein_start%in%255~T,
protein_start%in%486~T,
protein_start%in%396~T,
protein_start%in%255~T,
protein_start%in%315~T,
protein_start%in%252~T,
protein_start%in%253~T,
protein_start%in%250~T,
protein_start%in%359~T,
protein_start%in%351~T,
protein_start%in%355~T,
protein_start%in%317~T,
protein_start%in%359~T,
protein_start%in%355~T,
protein_start%in%459~T,
protein_start%in%276~T,
protein_start%in%299~T,
T~F))
highscore=samples_10.12%>%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))+scale_y_continuous(name="Enrithcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))
ggplotly(plotly)plotly=ggplot(highscore%>%filter(ct.y>=2),aes(x=reorder(subs_name,-score),y=score,fill=resresids))+geom_col()+theme(axis.text.x=element_text(angle=90, hjust=1))+scale_y_continuous(name="Enrithcment Score")+scale_x_discrete(name="Mutant")+guides(fill = guide_legend(title = "Clinically\n Observed \n Resmut"))
ggplotly(plotly)ggplot(samples_10.12%>%filter(!alt_aa%in%c("LL","PA"),protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+geom_tile()+theme(panel.background=element_rect(fill="white", colour="white"))+scale_fill_gradient2(low ="blue",midpoint=0,mid="white", high ="red",name="Score")
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
ggplot(samples_10.12%>%filter(!alt_aa%in%c("LL","PA"),protein_start>=242,protein_start<=494),aes(x=protein_start,y=alt_aa,fill=score))+geom_tile()+theme(panel.background=element_rect(fill="white", colour="white"))+scale_fill_gradient2(low ="blue",midpoint=3,mid="white", high ="red",name="Score")
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
ggplot(highscore%>%filter(resmuts%in%"TRUE"),aes(x=reorder(subs_name,-score),y=score))+geom_col()+theme_bw()
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
ic50data_all_sum=read.csv("output/ic50data_all_confidence_intervals_raw_data.csv",row.names = 1)
a=merge(highscore%>%filter(resmuts%in%"TRUE")%>%dplyr::select(subs_name,score),
ic50data_all_sum%>%dplyr::select(species,netgr_pred_model,netgr_pred_mean),by.x="subs_name",by.y="species")
ggplot(a,aes(x=netgr_pred_mean,y=score,label=subs_name))+geom_text()+theme_bw()+xlab("IC50 Predicted Growth Rates")+ylab("Enrichment Score observed w 5k mutant library \n at 30k sscs at 2 days post 300nM Imatinib")+ggtitle("Concurrent iL3 withdrawal + Imat Treatment")
| Version | Author | Date |
|---|---|---|
| c522b6d | haiderinam | 2022-12-08 |
# a=highscore%>%filter(protein_start%in%"255")
# sum(highscore$ct.y)
sample10_simple=sample10_simple%>%
mutate(error_status=case_when(protein_start%in%c(1:241,495:700)~T,
T~F))
plotly=ggplot(sample10_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)plotly=ggplot(sample10_simple%>%
filter(protein_start>=99,protein_start<=600,ct>=3)%>%
mutate(mutant=paste(protein_start,alt_aa)),aes(x=protein_start,y=maf,fill=error_status))+
geom_col()+
# geom_col(position = "dodge")+
scale_fill_manual(values = c("blue","red"))+
theme_bw()+
theme(legend.position = "none")
# scale_y_continuous(trans="log10")
ggplotly(plotly)plotly=ggplot(sample10_simple%>%
filter(protein_start>=99,protein_start<=600,ct>=1)%>%
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)#Things to do with the error rate plot:
#1. Look at sscs vs ngs vs dcs
#2. Combine two samples and see how that improves error rates
# setwd("../")
# samplex=read.csv("data/Consensus_Data/Novogene_lane14/sample10_combined/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
# sampley=read.csv("data/Consensus_Data/Novogene_lane14/sample11/variant_caller_outputs/variants_unique_ann.csv",header=T,stringsAsFactors = F)
# samples_xy=merge(samplex,sampley,by=c("type",
# "alt_start_pos",
# "alt_end_pos",
# "ref",
# "alt",
# "protein_start",
# "protein_end",
# "amino_acids",
# "codons",
# "impact",
# "polyphen_prediction",
# "consequence_terms"),all = T)
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] isoband_0.2.1 jquerylib_0.1.4 later_1.0.0 pillar_1.6.1
[17] glue_1.4.1 withr_2.4.2 DBI_1.1.0 lifecycle_1.0.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2 htmlwidgets_1.5.1
[25] codetools_0.2-16 evaluate_0.14 labeling_0.3 knitr_1.28
[29] fastmap_1.1.0 crosstalk_1.1.0.1 httpuv_1.5.2 fansi_0.4.1
[33] Rcpp_1.0.4.6 promises_1.1.0 backports_1.1.7 scales_1.1.1
[37] jsonlite_1.7.2 farver_2.0.3 fs_1.4.1 digest_0.6.25
[41] stringi_1.7.5 rprojroot_1.3-2 grid_4.0.0 tools_4.0.0
[45] magrittr_2.0.1 sass_0.4.1 lazyeval_0.2.2 tibble_3.1.2
[49] crayon_1.4.1 whisker_0.4 tidyr_1.1.3 pkgconfig_2.0.3
[53] MASS_7.3-55 ellipsis_0.3.2 data.table_1.12.8 assertthat_0.2.1
[57] rmarkdown_2.14 httr_1.4.2 R6_2.4.1 git2r_0.27.1
[61] compiler_4.0.0