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pipeline.plot.R
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library(ggplot2)
library(plyr)
library(forcats)
library(dplyr)
library(Rmisc)
library(VennDiagram)
library(stringr)
library(openxlsx)
library(ggfortify)
library(radmixture)
library(yaml)
library(kableExtra)
library(scales) # required for comma
library(dplyr)
library(nanopoRe) # devtools::install_github("sagrudd/nanopoRe")
library(VariantAnnotation)
library(vcfR)
library(CePa)
###################### Sample Information ######################
library(openxlsx)
data_samples_info <- read.xlsx(paste('example','samples.xlsx',sep = '/'))
data_samples_info$Group <- 'HAN'
data_samples_info[data_samples_info$Races=='TIB',]$Group <- 'TIB'
data_samples_info[data_samples_info$Races=='Biaka',]$Group <- 'AFR'
data_samples_info[data_samples_info$Races=='HUM',]$Group <- 'HUM'
data_samples_info$Group <- factor(data_samples_info$Group, levels = c('HUM', 'AFR', 'HAN', 'TIB'))
data_samples_info$Group_Detail <- as.character(data_samples_info$Group)
data_samples_info[data_samples_info$Races=='HAN'&data_samples_info$Location%in%c('Guangxi','Guangdong'),]$Group_Detail <- 'HANS'
data_samples_info[data_samples_info$Races=='HAN'&!data_samples_info$Location%in%c('Guangxi','Guangdong'),]$Group_Detail <- 'HANN'
data_samples_info[data_samples_info$Altitude>=4000&data_samples_info$Races=='TIB',]$Group_Detail <- 'TIBG'
data_samples_info[data_samples_info$Altitude<4000&data_samples_info$Races=='TIB',]$Group_Detail <- 'TIBL'
data_samples_info[data_samples_info$Races=='Biaka',]$Group_Detail <- 'AFR'
data_samples_info[data_samples_info$Races=='HUM',]$Group_Detail <- 'HUM'
data_samples_info$Group_Detail <- factor(data_samples_info$Group_Detail, levels = c('HUM', 'AFR', 'HANN', 'HANS', 'TIBL', 'TIBG'))
data_samples_info$Source <- 'NA'
data_samples_info[str_detect(data_samples_info$SampleID, 'CQ'),]$Source <- 'T15H10'
data_samples_info[str_detect(data_samples_info$SampleID, 'SAM'),]$Source <- 'T38H39'
data_samples_info[str_detect(data_samples_info$SampleID, 'WGC'),]$Source <- 'T48H50'
data_samples_info[str_detect(data_samples_info$SampleID, 'HGDP'),]$Source <- 'HGDP'
data_samples_info[data_samples_info$Races=='HUM',]$Source <- 'HUM'
data_samples_info$Source <- factor(data_samples_info$Source)
data_samples_info[is.na(data_samples_info$Altitude),]$Altitude <- 0
###################### heterozygosity and homozygosity ######################
data_sample_het <- read.table(paste('example', 'merge.genotypes.corrected.delCHR.svtk.gte1ngs.LDpruned.het',sep = '/'), header = TRUE)
data_sample_het$PHt.LDpruned <- (data_sample_het$N_SITES-data_sample_het$O.HOM.)
data_sample_het$PHot.LDpruned <- data_sample_het$O.HOM.
data_sample_het <- data_sample_het[data_sample_het$INDV%in%data_samples_info$SampleID,]
data_samples_info$PHt.LDpruned <- NA
data_samples_info$PHot.LDpruned <- NA
data_samples_info[order(data_samples_info$SampleID),]$PHt.LDpruned <- data_sample_het[order(data_sample_het$INDV),]$PHt.LDpruned
data_samples_info[order(data_samples_info$SampleID),]$PHot.LDpruned <- data_sample_het[order(data_sample_het$INDV),]$PHot.LDpruned
data_sample_het_snp <- read.table(paste('example', 'merge.T48H50andT38H39.reserved.LDpruned.het',sep = '/'), header = TRUE)
data_sample_het_snp <- data_sample_het_snp[data_sample_het_snp$INDV%in%data_samples_info$SampleID,]
data_sample_het_snp$PHt.LDpruned.SNP <- (data_sample_het_snp$N_SITES-data_sample_het_snp$O.HOM.)/data_sample_het_snp$N_SITES
data_samples_info <- merge(data_samples_info,data_sample_het_snp[,c('INDV','PHt.LDpruned.SNP')],by.x='SampleID',by.y='INDV',all.x=TRUE)
###################### sample stats ######################
data_sv_dist <- read.table(paste('example', 'merge.genotypes.corrected.delCHR.svtk.dist', sep = '/'))
colnames(data_sv_dist) <- c('Dist','Chrom','All','Shared','Major','Polymorphic','Singleton')
data_sv_dist <- data_sv_dist[data_sv_dist$All>0, ]
###################### sv details ######################
source('scripts/funcs_load_data.R')
path_TIBvsHAN <- paste('example', 'merge.genotypes.corrected.delCHR.svtk.annotsv.tsv', sep = '/')
data_all <- func.load.svtk.annotsv(path = path_TIBvsHAN, samplelist = data_samples_info)
data_sv_details_all <- data_all$data_sv
data_sample_details <- data_all$data_samples
data_genotypes <- data_all$data_genotypes
rownames(data_genotypes) <- data_sv_details_all$SVID
data_genotypes <- data_genotypes[order(data_sv_details_all$SVID),]
data_sv_details_all <- data_sv_details_all[order(data_sv_details_all$SVID),]
data_sample_details_ONT <- subset(data_sample_details, data_sample_details$id%in%data_samples_info[data_samples_info$Platform%in%c('ONT'),]$SampleID)
###################### sv vcf details ######################
source('funcs_load_data.R')
path_TIBvsHAN_vcf <- paste(path_data, 'merge.genotypes.corrected.delCHR.svtk.vcf.gz', sep = '/')
data_all_vcf <- func.load.svtk.vcf.local(path_TIBvsHAN_vcf, reference_fasta=paste(path_data, '../genome/hg19.fa', sep = '/'))
data_sv_details_raw <- data_all_vcf$data_sv_vcf
data_sv_details_all_karyo <- data_all_vcf$data_sv_karyo
###################### confguration for plots ######################
prefix_filename = 'plots'
config_color_repeat <- c(
'Low_complexity'='light blue',
'Simple_repeat'='blue',
'Satellite'='dark blue',
'SINE'='green',
'LINE'='yellow green',
'LTR'='dark green',
'DNA'='dark red',
'RNA'='purple',
'RC'='yellow',
'None'='gray',
'Other'='darkgray')
config_color_svtype <- c('DEL'='dark red',
'INS'='purple',
'DUP'='blue',
'INV'='dark green',
'TRA'='light blue')
config_color_group_supp <- c('Singleton'='light blue',
'Polymorphic'='blue',
'Major'='purple',
'Shared'='dark red')
config_color_group_pop <- c('All'='black',
'Shared'='gray',
'AFR'='dark red',
'HAN'='dark green',
'TIB'='dark blue',
'HANN'='green',
'HANS'='dark green',
'TIBG'='blue',
'TIBL'='dodger blue')
config_color_group_supp_han <- c('No Support'='gray',
'Singleton'='light blue',
'Polymorphic'='blue',
'Major'='purple',
'Shared'='dark red')
# config_color_group_len <- data.frame(GROUP_LEN=c('0-1k', '1k-20k', 'above 20k'),
# Color=c('light blue', 'purple','dark red'))
config_color_group_len <- c('0-100bp'='dark red',
'100bp-250bp'='purple',
'250bp-500bp'='blue',
'500bp-1kb'='light blue',
'50bp-1kb'='dark red',
'1kb-10kb'='purple',
'10kb-100kb'='blue',
'100kb-1Mb'='dark green',
'above 1Mb'='light blue')
config_color_database <- c('1000Genome'='dark red',
'DDD'='purple',
'DGV'='blue',
'GnomAD'='dark green',
'IMH'='light blue',
'None'='gray')
config_color_gene <- c('COPY_GAIN'='blue',
'DUP_PARTIAL'='purple',
'LOF'='yellow green',
'INV_SPAN'='dark red',
'UTR'='dark green',
'INTRONIC'='gray',
'INTERGENIC'='black')
val_res = 300
data_plot = data_sv_details_all
###################### SV locations (circos) ######################
library(circlize)
karyo_plot <- as.data.frame(data_sv_details_all_karyo)
karyo_plot$SUPP <- as.integer(karyo_plot$SUPP)
karyo_plot_BND <- karyo_plot[karyo_plot$SVTYPE=='TRA',]
karyo_plot_BND$seqnames <- paste('chr',karyo_plot_BND$seqnames, sep = '')
karyo_plot_BND_link <- karyo_plot_BND[,c('CHR2','END','END')]
colnames(karyo_plot_BND_link) <- c('seqnames', 'start', 'end')
karyo_plot_BND_link$seqnames <- paste('chr', as.character(karyo_plot_BND_link$seqnames), sep = "")
karyo_plot_BND_link$start <- as.integer(karyo_plot_BND_link$start)
karyo_plot_BND_link$end <- as.integer(karyo_plot_BND_link$end)
array_seqnames <- paste('chr', karyo_plot$seqnames,sep = '')
pdf(file = paste(prefix_filename,'sv.circos','pdf',sep = '.'), width = 8, height = 8)
circos.initializeWithIdeogram(species = 'hg19')
group_shared <- karyo_plot$SUPP==25
group_major <- karyo_plot$SUPP%in%seq(13,24)
group_polymorphic <- karyo_plot$SUPP%in%seq(2,12)
group_singleton <- karyo_plot$SUPP==1
circos.trackHist(factors=array_seqnames,
track.height = 0.1,
x=karyo_plot$start,col = "#999999", border = '#999999',
bg.border = NA, bin.size = 500000)
circos.trackHist(factors=array_seqnames[group_shared],
track.height = 0.1,
x=karyo_plot[group_shared,]$start,col = "red", border = 'red',
bg.border = NA,bin.size = 500000)
circos.trackHist(factors=array_seqnames[group_major], track.height = 0.1, x=karyo_plot[group_major,]$start,col = "purple", border = "purple",bg.border = NA,bin.size = 500000)
circos.trackHist(factors=array_seqnames[group_polymorphic], track.height = 0.1, x=karyo_plot[group_polymorphic,]$start,col = "blue",border = "blue",bg.border = NA,bin.size = 500000)
circos.trackHist(factors=array_seqnames[group_singleton], track.height = 0.1, x=karyo_plot[group_singleton,]$start,col = "light blue",border = "light blue",bg.border = NA,bin.size = 500000)
group_shared <- karyo_plot_BND$SUPP==25
group_major <- karyo_plot_BND$SUPP%in%seq(13,24)
group_polymorphic <- karyo_plot_BND$SUPP%in%seq(2,12)
group_singleton <- karyo_plot_BND$SUPP==1
dev.off()
###################### samples NGS location ######################
library(maps)
library(mapdata)
library(maptools);
china_map=readShapePoly('china-province-border-data/bou2_4p.shp');
china_map@data$cName <- iconv(china_map@data$NAME, from = "GBK")
x <- china_map@data #读取行政信息
xs <- data.frame(x,id=seq(0:924)-1) #含岛屿共925个形状
china_map1 <- fortify(china_map) #转化为数据框
china_map_data <- join(china_map1, xs, type = "full", ) #合并两个数据框
tmp <- summary(factor(data_samples_info[data_samples_info$Platform=='NGS',]$cLocation))
NAME <- names(tmp)
pop <- tmp
pop <- data.frame(NAME, pop)
colnames(pop) <- c('cName', 'pop')
china_map_pop <- join(china_map_data, pop, type = "full")
pdf(file = paste(prefix_filename,'samples.map','pdf',sep = '/'), width = 8, height = 6)
postscript(file = paste(prefix_filename,'samples.map','eps',sep = '.'), width = 8, height = 6)
ggplot(china_map_pop, aes(x = long, y = lat, group = group, fill = pop)) +
geom_polygon() +
geom_path(color = "grey40") +
coord_map() +
xlab('') +
ylab("") +
theme(legend.title=element_blank(),
legend.text = element_text(size = 8,face = "bold"),
axis.text.x = element_blank(),
axis.text.y = element_blank())
dev.off()
###################### samples individual ######################
##need depackage scater
df_sorted <- arrange(data_sample_details_ONT, id, Type)
df_cumsum <- ddply(df_sorted, "id",
transform,
ypos=cumsum(Discovery) - 0.5*Discovery)
pdf(file = paste(prefix_filename,'sv.samples.bar','pdf',sep = '/'), width = 4, height = 3)
ggplot(data=df_cumsum, aes(x=id, y=Discovery, fill = Type)) +
geom_bar(stat="identity") +
theme_minimal()+
theme(legend.title=element_blank(),
legend.direction = 'horizontal',
legend.position=c(0.5,0.98),
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold", angle = 60, vjust = 0.6),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-15,0,0,0))) +
scale_fill_manual(values=c('light blue', 'blue', 'purple','dark red')) +
xlab("") +
ylab("Discovery")
dev.off()
###################### telomere enrichment ######################
pdf(file = paste(prefix_filename,'Svs_dist.gt.update','pdf',sep = '/'), width = 4, height = 3)
ggplot(data = data_sv_dist[data_sv_dist$All!=0,],
aes(x = Dist, y = All)) +
geom_point(size = 0.05, color="blue") +
annotate("rect", fill = "dark gray", alpha = 0.5,
xmin = 0, xmax = 5,
ymin = -Inf, ymax = Inf)+
xlim(0, 150) +
# ylim(0, 250) +
ylab('SVs Per 500kbp') +
xlab('Telomere Distance') +
theme_minimal()+
theme(
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x = element_text(size = 6,face = "bold"))
dev.off()
###################### SV & GROUP_SUPP ######################
data_sv_group <- plyr::count(data_plot,'GROUP_SUPP')
data_sv_group$GROUP_SUPP <- factor(data_sv_group$GROUP_SUPP)
pdf(file = paste(prefix_filename,'sv.group_supp.pie','pdf',sep = '/'), width = 3, height = 3)
ggplot(data_sv_group, aes(x="",y=freq,fill=GROUP_SUPP)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
geom_text(aes(y=freq/4+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_sv_group$freq/sum(data_sv_group$freq)),
size=2,
color='white',
fontface="bold") +
scale_fill_manual(values=config_color_group_supp) +
theme_minimal() +
theme(legend.position = c(0.5,0),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.title=element_blank(),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
###################### SVLEN ######################
pdf(file = paste(prefix_filename,'svlen.freqploy','pdf',sep = '、'), width = 4, height = 3)
ggplot(data=data_plot[data_plot$SVTYPE!='TRA',], aes(x=SVLEN, color = SVTYPE)) +
geom_freqpoly(binwidth=1/5, size=0.8) +
scale_x_continuous(trans = 'log10',
breaks=c(100,1000,10000,100000,1000000,10000000),
labels =c('100bp','1kb','10kb','100kb','1Mb', '10Mb'),
limits = c(50, 10000000)
) +
scale_y_continuous(trans = 'log10') +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position=c(0.9,0.9),
legend.direction = 'vertical',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x = element_text(size = 6,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-10,0,0,0))) +
scale_fill_manual(values=config_color_svtype) +
xlab('') +
ylab("SV Count")
dev.off()
###################### SVLEN & GROUP_SUPP ######################
data_sv_group <- plyr::count(data_plot,c('GROUP_LEN','GROUP_SUPP'))
data_sv_group$label <- 0
for (type_sv in levels(data_sv_group$GROUP_LEN)){
data_sv_group[data_sv_group$GROUP_LEN==type_sv,]$label <- sum(data_sv_group[data_sv_group$GROUP_LEN==type_sv,]$freq)
}
pdf(file = paste(prefix_filename,'svlen.group_supp.bar','pdf',sep = '.'), width = 3, height = 3)
ggplot(data_sv_group, aes(x = GROUP_LEN, y = freq, fill = GROUP_SUPP)) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-10,0,0,0))) +
scale_fill_manual(values=config_color_group_supp) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=1)
dev.off()
###################### SVTYPE & GROUP_SUPP ######################
data_sv_group <- plyr::count(data_plot,c('SVTYPE','GROUP_SUPP'))
data_sv_group$SVTYPE <- factor(data_sv_group$SVTYPE)
data_sv_group$label <- 0
for (type_sv in levels(data_sv_group$SVTYPE)){
data_sv_group[data_sv_group$SVTYPE==type_sv,]$label <- sum(data_sv_group[data_sv_group$SVTYPE==type_sv,]$freq)
}
data_sv_group$SVTYPE <- factor(data_sv_group$SVTYPE, levels = c( 'DEL', 'INS', 'DUP', 'INV', 'TRA'))
pdf(file = paste(prefix_filename,'svtype.group_supp.bar','pdf',sep = '.'), width = 4, height = 3)
ggplot(data_sv_group, aes(x = SVTYPE, y = freq, fill = GROUP_SUPP)) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-15,0,0,0))) +
scale_fill_manual(values=config_color_group_supp) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=2)
dev.off()
###################### Brepoints CI ###################################
data_tmp <- data_plot
data_tmp$GROUP_CI <- NA
data_tmp[data_tmp$MAXCI_POS<=1000&data_tmp$MAXCI_END<=1000,]$GROUP_CI <- '500bp-1kb'
data_tmp[data_tmp$MAXCI_POS<=500&data_tmp$MAXCI_END<=500,]$GROUP_CI <- '250bp-500bp'
data_tmp[data_tmp$MAXCI_POS<=250&data_tmp$MAXCI_END<=250,]$GROUP_CI <- '100bp-250bp'
data_tmp[data_tmp$MAXCI_POS<=100&data_tmp$MAXCI_END<=100,]$GROUP_CI <- '0-100bp'
data_tmp$GROUP_CI <- factor(data_tmp$GROUP_CI, levels = c('0-100bp', '100bp-250bp', '250bp-500bp', '500bp-1kb'))
df.new<-ddply(data_tmp,.(GROUP_SUPP),plyr::summarise,
prop=prop.table(table(GROUP_CI)),
SUPP=names(table(GROUP_CI)))
df.new$SUPP <- factor(df.new$SUPP, levels = c('0-100bp', '100bp-250bp', '250bp-500bp', '500bp-1kb'))
pdf(file = paste(prefix_filename,'breakpoints.ci.group_supp.prop','pdf',sep = '.'), width = 4, height =3)
ggplot(df.new, aes(SUPP, prop, fill=GROUP_SUPP)) +
geom_bar(stat="identity",position = 'dodge') +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position=c(0.5,0.9),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x = element_text(size = 6,face = "bold")
) +
scale_fill_manual(values=config_color_group_supp) +
xlab("Max Interval of Breakpoints") +
ylab("Prop")
dev.off()
###################### Brepoints & Repeat ###################################
data_tmp <- data_plot[,c('SVID','SVTYPE','GROUP_LEN','SVLEN','Repeats_type_right', 'Repeats_type_left', 'GCcontent_left','GCcontent_right', 'GROUP_SUPP')]
data_tmp <- rbind(data_tmp, data_tmp)
data_tmp$RepClass <- factor(c(as.character(data_plot$Repeats_type_left),as.character(data_plot$Repeats_type_right)), levels = c('Low_complexity', 'Simple_repeat', 'Satellite','SINE','LINE','LTR','DNA','RNA','RC','Other','None'))
data_tmp$GCcontent <- c(data_plot$GCcontent_left,data_plot$GCcontent_right)
data_tmp <- data_tmp[data_tmp$RepClass!='Other',]
data_tmp$RepClass <- factor(as.character(data_tmp$RepClass),levels = c('Low_complexity', 'Simple_repeat', 'Satellite','SINE','LINE','LTR','DNA','RNA','RC','None'))
data_sv_group <- plyr::count(data_tmp,c('RepClass','GROUP_SUPP'))
data_sv_group$label <- 0
for (type_sv in levels(data_sv_group$RepClass)){
data_sv_group[data_sv_group$RepClass==type_sv,]$label <- sum(data_sv_group[data_sv_group$RepClass==type_sv,]$freq)
}
pdf(file = paste(prefix_filename,'breakpoints.repeat.group_supp.bar','pdf',sep = '.'), width = 8, height = 6)
ggplot(data_sv_group, aes(x = RepClass, y = freq, fill = GROUP_SUPP)) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 10,face = "bold"),
axis.text.y = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-10,0,0,0))) +
scale_fill_manual(values=config_color_group_supp) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=2.5)
dev.off()
###################### Brepoints & Repeat & formation ###################################
data_sv_repeat <- subset(data_tmp, SVTYPE%in%c('DEL','INS')&GROUP_LEN%in%c('50bp-1kb','1kb-10kb'))
data_sv_repeat <- subset(data_tmp, SVTYPE%in%c('DEL','INS')&SVLEN<=1000000&GROUP_SUPP%in%c('Major','Shared'))
data_sv_repeat_ins <- subset(data_sv_repeat, SVTYPE=='INS')
data_sv_repeat_del <- subset(data_sv_repeat, SVTYPE=='DEL')
data_repeat_set <- data_plot[,c('SVTYPE','GROUP_LEN','SVLEN','Repeats_type_right', 'Repeats_type_left', 'GCcontent_left','GCcontent_right', 'GROUP_SUPP', 'RepClass')]
data_repeat_set <- data_repeat_set[data_repeat_set$SVLEN<=1000000,]
data_repeat_set_del <- data_repeat_set[data_repeat_set$SVTYPE=='DEL',]
data_repeat_set_ins <- data_repeat_set[data_repeat_set$SVTYPE=='INS',]
num_sv <- nrow(data_repeat_set_del)
condition_vntr <- data_repeat_set_del$RepClass%in%c('Low_complexity','Simple_repeat','Satellite')
condition_nahr <- (!condition_vntr)&data_repeat_set_del$Repeats_type_right==data_repeat_set_del$Repeats_type_left&data_repeat_set_del$Repeats_type_left!='None'
condition_te <- !condition_vntr & ! condition_nahr &
(data_repeat_set_del$Repeats_type_right%in%
c('SINE','LINE','LTR','DNA')|
data_repeat_set_del$Repeats_type_left%in%
c('SINE','LINE','LTR','DNA'))
ids_pie = c('SV','DEL', 'INS')
labels_pie = c('SV formation','DEL', 'INS')
parents_pie = c('','SV','SV')
values_pie = c(38216,nrow(data_repeat_set_del), nrow(data_repeat_set_ins))
ids_pie <- c(ids_pie, 'VNTR-DEL')
labels_pie <- c(labels_pie, 'VNTR')
parents_pie <- c(parents_pie, 'DEL')
values_pie <- c(values_pie, sum(condition_vntr))
for (label_condition in c('Low_complexity','Simple_repeat','Satellite')) {
ids_pie <- c(ids_pie, paste(label_condition, 'VNTR-DEL', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'VNTR-DEL')
values_pie <- c(values_pie, sum(data_repeat_set_del[condition_vntr,]$RepClass==label_condition) )
}
ids_pie <- c(ids_pie, 'NAHR')
labels_pie <- c(labels_pie, 'NAHR')
parents_pie <- c(parents_pie, 'DEL')
values_pie <- c(values_pie, sum(condition_nahr))
for (label_condition in c('SINE','LINE','LTR', 'Other')) {
ids_pie <- c(ids_pie, paste(label_condition, 'NAHR', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'NAHR')
if (label_condition == 'Other'){
values_pie <- c(values_pie, sum(!data_repeat_set_del[condition_nahr,]$Repeats_type_left%in%c('SINE','LINE','LTR')) )
} else {
values_pie <- c(values_pie, sum(data_repeat_set_del[condition_nahr,]$Repeats_type_left==label_condition) )
}
}
ids_pie <- c(ids_pie, 'TE-DEL')
labels_pie <- c(labels_pie, 'TE')
parents_pie <- c(parents_pie, 'DEL')
values_pie <- c(values_pie, sum(condition_te))
for (label_condition in c('SINE','LINE', 'Other')) {
ids_pie <- c(ids_pie, paste(label_condition, 'TE-DEL', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'TE-DEL')
if (label_condition == 'Other'){
values_pie <- c(values_pie,
sum(condition_te)-
sum((data_repeat_set_del[condition_te,]$Repeats_type_left=='SINE'|
data_repeat_set_del[condition_te,]$Repeats_type_right=='SINE') &
data_repeat_set_del[condition_te,]$Repeats_type_left!='LINE' &
data_repeat_set_del[condition_te,]$Repeats_type_right!='LINE')-
sum((data_repeat_set_del[condition_te,]$Repeats_type_left=='LINE'|
data_repeat_set_del[condition_te,]$Repeats_type_right=='LINE') &
data_repeat_set_del[condition_te,]$Repeats_type_left!='SINE' &
data_repeat_set_del[condition_te,]$Repeats_type_right!='SINE'))
} else {
values_pie <- c(values_pie,
sum((data_repeat_set_del[condition_te,]$Repeats_type_left==label_condition|
data_repeat_set_del[condition_te,]$Repeats_type_right==label_condition) &
!data_repeat_set_del[condition_te,]$Repeats_type_left%in%setdiff(c('SINE','LINE'), label_condition) &
!data_repeat_set_del[condition_te,]$Repeats_type_right%in%setdiff(c('SINE','LINE'), label_condition)))
}
}
num_sv <- nrow(data_repeat_set_ins)
condition_vntr <- data_repeat_set_ins$RepClass%in%c('Low_complexity','Simple_repeat','Satellite')
condition_te <- !condition_vntr & (data_repeat_set_ins$Repeats_type_right%in%c('SINE','LINE','LTR','DNA')|data_repeat_set_ins$Repeats_type_left%in%c('SINE','LINE','LTR','DNA'))
ids_pie <- c(ids_pie, 'VNTR-INS')
labels_pie <- c(labels_pie, 'VNTR')
parents_pie <- c(parents_pie, 'INS')
values_pie <- c(values_pie, sum(condition_vntr))
for (label_condition in c('Low_complexity','Simple_repeat','Satellite')) {
ids_pie <- c(ids_pie, paste(label_condition, 'VNTR-INS', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'VNTR-INS')
values_pie <- c(values_pie, sum(data_repeat_set_ins[condition_vntr,]$RepClass==label_condition) )
}
ids_pie <- c(ids_pie, 'TE-INS')
labels_pie <- c(labels_pie, 'TE')
parents_pie <- c(parents_pie, 'INS')
values_pie <- c(values_pie, sum(condition_te))
for (label_condition in c('SINE','LINE', 'Other')) {
ids_pie <- c(ids_pie, paste(label_condition, 'TE-INS', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'TE-INS')
if (label_condition == 'Other'){
values_pie <- c(values_pie,
sum(condition_te)-
sum((data_repeat_set_ins[condition_te,]$Repeats_type_left=='SINE'|
data_repeat_set_ins[condition_te,]$Repeats_type_right=='SINE') &
data_repeat_set_ins[condition_te,]$Repeats_type_left!='LINE' &
data_repeat_set_ins[condition_te,]$Repeats_type_right!='LINE')-
sum((data_repeat_set_ins[condition_te,]$Repeats_type_left=='LINE'|
data_repeat_set_ins[condition_te,]$Repeats_type_right=='LINE') &
data_repeat_set_ins[condition_te,]$Repeats_type_left!='SINE' &
data_repeat_set_ins[condition_te,]$Repeats_type_right!='SINE'))
} else {
values_pie <- c(values_pie,
sum((data_repeat_set_ins[condition_te,]$Repeats_type_left==label_condition|
data_repeat_set_ins[condition_te,]$Repeats_type_right==label_condition) &
!data_repeat_set_ins[condition_te,]$Repeats_type_left%in%setdiff(c('SINE','LINE'), label_condition) &
!data_repeat_set_ins[condition_te,]$Repeats_type_right%in%setdiff(c('SINE','LINE'), label_condition)))
}
}
if (!require("processx")) install.packages("processx")
fig <- plot_ly(
ids=ids_pie,
labels=labels_pie,
parents=parents_pie,
values=values_pie,
type='sunburst',
branchvalues = 'total'
) %>% add_surface()
orca(fig, "../data/result_genotypted/plots/surface-plot.svg")
# png(filename = paste(prefix_filename,'formation.pie.del.png',sep = '.'), width = 1200, height = 1000, res = val_res)
pdf(file = paste(prefix_filename,'formation.pie.del','pdf',sep = '.'), width = 4, height = 3)
ggplot(data_rep_tmp, aes(x="",y=freq,fill=mech)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
geom_text(aes(y=freq/4+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_rep_tmp$freq/sum(data_rep_tmp$freq)),
size=2,
color='white',
fontface="bold") +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
num_sv <- nrow(data_repeat_set_ins)
condition_vntr <- data_repeat_set_ins$RepClass%in%c('Low_complexity','Simple_repeat','Satellite')
# condition_nahr <- !condition_vntr&data_repeat_set_ins$Repeats_type_right==data_repeat_set_ins$Repeats_type_left
condition_te <- !condition_vntr & (data_repeat_set_ins$Repeats_type_right%in%c('SINE','LINE','LTR','DNA')|data_repeat_set_ins$Repeats_type_left%in%c('SINE','LINE','LTR','DNA'))
data_rep_tmp <- data.frame(mech=c('VNTR','TE','Others'),
freq=c(
sum(condition_vntr),
# sum(condition_nahr),
sum(condition_te),
num_sv - sum(condition_vntr) - sum(condition_te)))
# png(filename = paste(prefix_filename,'formation.pie.ins.png',sep = '.'), width = 1200, height = 1000, res = val_res)
pdf(file = paste(prefix_filename,'formation.pie.ins','pdf',sep = '.'), width = 4, height = 3)
ggplot(data_rep_tmp, aes(x="",y=freq,fill=mech)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
geom_text(aes(y=freq/4+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_rep_tmp$freq/sum(data_rep_tmp$freq)),
size=2,
color='white',
fontface="bold") +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
###################### Brepoints & Repeat & SVLEN ###################################
pdf(file = paste(prefix_filename,'breakpoints.repeat.len.lte1m','pdf',sep = '.'),
width = 3, height = 2.5)
ggplot(data=data_sv_repeat, aes(x=abs(SVLEN), fill = RepClass)) +
geom_histogram() +
scale_x_continuous(trans = 'log10',breaks = c(100,300,2000,6000,20000),
labels = c('100bp','300bp','2kb','6kb','20kb'),limits = c(50,20000)) +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position=c(0.8,0.65),
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold", angle = 45),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x = element_text(size = 6,face = "bold")) +
scale_fill_manual(values=config_color_repeat) +
xlab('') +
ylab("SV Count")
dev.off()
###################### Brepoints & GCcontent & Repeat###################################
pdf(file = paste(prefix_filename,'breakpoints.gc.repeat','pdf',sep = '.'), width = 8, height = 6)
ggplot(data=data_sv_repeat, aes(x=GCcontent, fill = RepClass)) +
geom_histogram() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position='bottom',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 10,face = "bold", angle = 45),
axis.text.y = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.title.x = element_text(size = 10,face = "bold")) +
scale_fill_manual(values=config_color_repeat) +
xlab('GC Content') +
ylab("SV Count")
dev.off()
###################### Gene Annotation####################################################
c('COPY_GAIN','DUP_PARTIAL','LOF','INV_SPAN','UTR','INTRONIC','INTERGENIC')
data_sv_group <- plyr::count(data_plot[data_plot$SVLEN<1000000,],'GROUP_SV')
data_sv_group$GROUP_SV <- factor(data_sv_group$GROUP_SV ,levels = c('DUP_PARTIAL','LOF','INV_SPAN','UTR','INTRONIC','INTERGENIC'))
pdf(file = paste(prefix_filename,'gene.pie','pdf',sep = '.'), width = 2.5, height = 2.5)
ggplot(data_sv_group, aes(x="",y=freq,fill=GROUP_SV)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
scale_fill_manual(values=config_color_gene) +
geom_text(aes(y=freq/6+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_sv_group$freq/sum(data_sv_group$freq)),
size=2,
color='white',
fontface="bold") +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position=c(0.6,0.025),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
data_sv_group <- data_sv_group[!data_sv_group$GROUP_SV%in%c('INTRONIC','INTERGENIC'),]
pdf(file = paste(prefix_filename,'gene.cds.pie','pdf',sep = '.'), width = 4, height = 3)
ggplot(data_sv_group, aes(x="",y=freq,fill=GROUP_SV)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
scale_fill_manual(values=config_color_gene) +
geom_text(aes(y=freq/4+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_sv_group$freq/sum(data_sv_group$freq)),
size=2,
color='white',
fontface="bold") +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
data_sv_group <- plyr::count(data_plot[data_plot$SVLEN<1000000,],c('GROUP_SV','GROUP_SUPP'))
data_sv_group$label <- 0
data_sv_group$GROUP_SV <- factor(data_sv_group$GROUP_SV)
for (type_sv in levels(data_sv_group$GROUP_SV)){
data_sv_group[data_sv_group$GROUP_SV==type_sv,]$label <- sum(data_sv_group[data_sv_group$GROUP_SV==type_sv,]$freq)
}
pdf(file = paste(prefix_filename,'gene.group_supp.bar','pdf',sep = '.'), width = 8, height = 6)
ggplot(data_sv_group, aes(x = GROUP_SV, y = freq, fill = GROUP_SUPP)) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 10,face = "bold"),
axis.text.y = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-10,0,0,0))) +
scale_fill_manual(values=config_color_group_supp) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=2.5)
dev.off()
###################### Database ######################
data_sv_group <- plyr::count(data_plot,'Database')
pdf(file = paste(prefix_filename,'database.pie','pdf',sep = '.'), width = 3, height = 3)
ggplot(data_sv_group, aes(x="",y=freq,fill=Database)) +
geom_bar(width=1,stat="identity") + coord_polar("y",start=0) +
geom_text(aes(y=freq/6+c(0,cumsum(freq)[-length(freq)])),
label=percent(data_sv_group$freq/sum(data_sv_group$freq)),
size=2,
color='white',
fontface="bold") +
scale_fill_manual(values=config_color_database) +
theme_minimal() +
theme(legend.title=element_blank(),
legend.position=c(0.5,0),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
dev.off()
###################### Database & GROUP_SUPP ######################
data_sv_anno_group <- plyr::count(data_plot, c('GROUP_SUPP', 'Database'))
data_sv_anno_group$label <- 0
for (type_sv in levels(data_sv_anno_group$GROUP_SUPP)){
data_sv_anno_group[data_sv_anno_group$GROUP_SUPP==type_sv,]$label <- sum(data_sv_anno_group[data_sv_anno_group$GROUP_SUPP==type_sv,]$freq)
}
pdf(file = paste(prefix_filename,'database.group_supp','pdf',sep = '.'), width = 4, height = 3)
ggplot(data_sv_anno_group, aes(x = GROUP_SUPP, y = freq, fill = forcats::fct_rev(Database))) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.3, 'cm'),
legend.text = element_text(size = 6,face = "bold"),
axis.text.x = element_text(size = 6,face = "bold"),
axis.text.y = element_text(size = 6,face = "bold"),
axis.title.y = element_text(size = 6,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-20,0,0,0))) +
scale_fill_manual(values=config_color_database) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=2, fontface="bold")
dev.off()
###################### Database & SVTYPE ######################
data_sv_anno_group <- plyr::count(data_plot , c('SVTYPE', 'Database'))
data_sv_anno_group$SVTYPE <- factor(data_sv_anno_group$SVTYPE)
data_sv_anno_group$label <- 0
for (type_sv in levels(data_sv_anno_group$SVTYPE)){
data_sv_anno_group[data_sv_anno_group$SVTYPE==type_sv,]$label <- sum(data_sv_anno_group[data_sv_anno_group$SVTYPE==type_sv,]$freq)
}
pdf(file = paste(prefix_filename,'database.svtype','pdf',sep = '.'), width = 8, height = 6)
ggplot(data_sv_anno_group, aes(x = SVTYPE, y = freq, fill = forcats::fct_rev(Database))) +
geom_bar(position = "fill",stat = "identity") +
scale_y_continuous(labels = scales::percent_format(), expand = expand_scale(mult = .1)) +
coord_flip() +
theme_minimal()+
theme(legend.title=element_blank(),
legend.position="bottom",
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 10,face = "bold"),
axis.text.y = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.title.x.bottom = element_text(margin = margin(-20,0,0,0))) +
scale_fill_manual(values=config_color_database) +
xlab("") +
ylab("") +
geom_text(aes(label = label, y= ..prop..), stat= "count", hjust = -0.1, size=2, fontface="bold")
dev.off()
###################### Database & AF & GROUP_SUPP ######################
pdf(file = paste(prefix_filename,'database.af','pdf',sep = '.'), width = 8, height = 12)
ggplot(data=data_plot, aes(x=AF_Database, fill = GROUP_SUPP)) +
geom_histogram() +
theme_minimal()+
# scale_y_continuous(trans = 'log', breaks = c(1000,10000),labels = c('1000','10000')) +
theme(legend.title=element_blank(),
legend.position='bottom',
legend.spacing.x = unit(0.1, 'cm'),
legend.key.size=unit(0.5, 'cm'),
legend.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 10,face = "bold", angle = 45),
axis.text.y = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.title.x = element_text(size = 10,face = "bold")) +
scale_fill_manual(values=config_color_group_supp) +
xlab('AF') +
ylab("Discovery")
dev.off()
###################### Genotyping ######################
data_tmp <- data_plot
data_tmp$GROUP_SUPP_NGS <- as.character(data_tmp$GROUP_SUPP_NGS)
data_tmp <- data_tmp[!is.na(data_tmp$MR_NGS),]
# data_tmp <- data_tmp[!is.na(data_tmp$MR_NGS)&data_tmp$MR_NGS<0.05,]
data_tmp[is.na(data_tmp$GROUP_SUPP_NGS),]$GROUP_SUPP_NGS <- 'No Support'
data_tmp$GROUP_SUPP_NGS <- factor(data_tmp$GROUP_SUPP_NGS, levels = c('No Support','Singleton', 'Polymorphic', 'Major', 'Shared'))
data_tmp_1 <- plyr::count(data_tmp[data_tmp$MR_NGS<0.05,],'GROUP_SUPP_NGS')
data_tmp_2 <- plyr::count(data_tmp[data_tmp$MR_NGS<0.05&data_tmp$GROUP_SUPP_NGS=='No Support',],
'GROUP_SUPP')
data_tmp_3 <- plyr::count(data_tmp[data_tmp$MR_NGS<0.05&data_tmp$GROUP_SUPP_NGS!='No Support',],
'GROUP_SUPP')
ids_pie = c('SV', 'MR_0', 'MR_1', 'No_Support', 'Support')
labels_pie = c('SV genotyping', 'MR < 0.05', 'MR >= 0.05', 'genotyped AF = 0', 'genotyped AF > 0')
parents_pie = c('', 'SV', 'SV', 'MR_0', 'MR_0')
values_pie = c(nrow(data_tmp),
sum(data_tmp$MR_NGS<0.05),
sum(data_tmp$MR_NGS>=0.05),
data_tmp_1[data_tmp_1$GROUP_SUPP_NGS=='No Support',]$freq,
sum(data_tmp_1$freq)-data_tmp_1[data_tmp_1$GROUP_SUPP_NGS=='No Support',]$freq)
for (label_condition in data_tmp_2$GROUP_SUPP) {
ids_pie <- c(ids_pie, paste(label_condition, 'No_Support', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'No_Support')
values_pie <- c(values_pie, data_tmp_2[data_tmp_2$GROUP_SUPP==label_condition,]$freq)
}
for (label_condition in data_tmp_3$GROUP_SUPP) {
ids_pie <- c(ids_pie, paste(label_condition, 'Support', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, 'Support')
values_pie <- c(values_pie, data_tmp_3[data_tmp_3$GROUP_SUPP==label_condition,]$freq)
}
data_sv_group <- subset(data_tmp, GROUP_SUPP_NGS=='No Support' & MR_NGS <= 0.05)
data_sv_group$GROUP_REP <- 'None'
data_sv_group[data_sv_group$RepClass!='None',]$GROUP_REP <- 'Repclass'
data_sv_group[data_sv_group$RepClass=='None'&data_sv_group$SD!='',]$GROUP_REP <- 'SD'
data_sv_group[data_sv_group$GROUP_REP=='None'&
(data_sv_group$Repeats_type_left!='None'|data_sv_group$Repeats_type_right!='None'),]$GROUP_REP <- 'flanked'
data_tmp_4 <- plyr::count(data_sv_group,c('GROUP_SUPP','GROUP_REP'))
for (label_group in data_tmp_2$GROUP_SUPP){
for (label_condition in unique(data_tmp_4$GROUP_REP)) {
ids_pie <- c(ids_pie, paste(label_condition, label_group, 'No_Support', sep = '_'))
labels_pie <- c(labels_pie, label_condition)
parents_pie <- c(parents_pie, paste(label_group,'No_Support', sep = '_'))
values_pie <- c(values_pie, data_tmp_4[data_tmp_4$GROUP_SUPP==label_group&
data_tmp_4$GROUP_REP==label_condition,]$freq)
}
}
if (!require("processx")) install.packages("processx")
fig <- plot_ly(
ids=ids_pie,
labels=labels_pie,
parents=parents_pie,
values=values_pie,
type='sunburst',
branchvalues = 'total'
)
orca(fig, "../data/result_genotypted/plots/genotyping.pie.svg")
###################### Genotyping & HardyWeinberg ######################
library(HardyWeinberg)
plot.HWE <- function(dat,pop=NULL,title=NULL,full.legend=F,lab.cex=1){
require(HardyWeinberg,quietly=T)
#Gather HW p-values & colors
HWE.mat <- dat
HW.p <- HWChisqStats(X=HWE.mat,x.linked=F,pvalues=T)
HW.cols <- rep("#4DAC26",times=length(HW.p))
HW.cols[which(HW.p<0.05)] <- "#81F850"
HW.cols[which(HW.p<0.05/length(HW.p))] <- "#AC26A1"
#Generate HW plot frame