lane14_comparisons

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Last updated: 2022-10-29

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Knit directory: duplex_sequencing_screen/

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Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/Consensus_Data/.Rhistory
    Ignored:    data/Consensus_Data/Novogene_lane13/sample7/variant_caller_outputs/
    Ignored:    data/Consensus_Data/Novogene_lane13/sample8/variant_caller_outputs/
    Ignored:    data/Consensus_Data/Novogene_lane14/sample10_combined/variant_caller_outputs/duplex/
    Ignored:    data/Consensus_Data/Novogene_lane14/sample13/
    Ignored:    data/Consensus_Data/Novogene_lane14/sample14b/
    Ignored:    data/Consensus_Data/Novogene_lane14/sample1_combined/
    Ignored:    data/Consensus_Data/Novogene_lane14/sample8/variant_caller_outputs/
    Ignored:    data/Consensus_Data/Novogene_lane2/abl_ref/kraken2-master/newdir/
    Ignored:    data/Consensus_Data/Novogene_lane4/.ipynb_checkpoints/
    Ignored:    data/Consensus_Data/Novogene_lane4/GalaxyData/initialanalysis/
    Ignored:    data/Consensus_Data/Novogene_lane4/output/
    Ignored:    data/Consensus_Data/Novogene_lane6/
    Ignored:    data/Consensus_Data/Novogene_lane7/
    Ignored:    data/Consensus_Data/Ranomics_Pooled/RP4/
    Ignored:    data/Consensus_Data/Ranomics_Pooled/RP5/

Untracked files:
    Untracked:  data/Consensus_Data/Novogene_lane14/sample10_combined/duplex_sorted_filtered.tsv
    Untracked:  data/Consensus_Data/Novogene_lane14/sample10_combined/variant_caller_outputs/.DS_Store

Unstaged changes:
    Modified:   .DS_Store
    Modified:   data/.DS_Store
    Modified:   data/Consensus_Data/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane11/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane12/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane12/sample1/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane12/sample3/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/sample14_combined/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/sample15/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/sample16/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/sample17/.DS_Store
    Modified:   data/Consensus_Data/Novogene_lane14/sample18/.DS_Store
    Modified:   data/Refs/.DS_Store
    Modified:   data/Refs/EGFR/.DS_Store

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/lane14_comparisons.Rmd) and HTML (docs/lane14_comparisons.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
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/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/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/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/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/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"))

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()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

ggplot(samples_14.16,aes(x=score))+geom_histogram(bins=100)

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

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

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

ggplot(imat_scores%>%mutate(name=paste(ref_aa,protein_start,alt_aa)),aes(x=score.x,y=score.y,label=name))+geom_text()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

# ggplotly(plotly)
ggplot(imat_scores,aes(x=score.x,y=score.y))+geom_bin2d(bins=100)+scale_fill_continuous(type="viridis")+theme_bw()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

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")

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

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 [4653] 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 [4653] is not a sub-multiple or multiple of the number of
rows [20]

# cor(imat_scores$score.x,imat_scores$score.y,method="pearson")




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

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

ggplot(asc_scores%>%mutate(name=paste(ref_aa,protein_start,alt_aa)),aes(x=score.x,y=score.y,label=name))+geom_text()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

# ggplotly(plotly)
ggplot(asc_scores,aes(x=score.x,y=score.y))+geom_bin2d(bins=100)+scale_fill_continuous(type="viridis")+theme_bw()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

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")

Past versions of unnamed-chunk-1-11.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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 [4642] 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 [4642] is not a sub-multiple or multiple of the number of
rows [20]

# a=samples_14.17%>%filter(ct.x>1)

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

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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%>%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/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

Past versions of unnamed-chunk-3-3.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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%>%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

Past versions of unnamed-chunk-3-5.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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/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/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()

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Version	Author	Date
d358fe3	haiderinam	2022-10-29

ggplot(samples_14.16,aes(x=score))+geom_histogram(bins=100)

Past versions of unnamed-chunk-4-2.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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")

Past versions of unnamed-chunk-4-5.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

# 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/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/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()

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

# 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")

Past versions of unnamed-chunk-5-4.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

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")

Past versions of unnamed-chunk-5-5.png

Version	Author	Date
d358fe3	haiderinam	2022-10-29

# 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)

lane14sortedsamples=merge_samples("Novogene_lane14/sample10_combined","Novogene_lane14/sample11")
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<=600,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)

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] 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