library(ggplot2)
library(tidyverse)
library(ggupset)
vcf_to_vcf <- '/imppc/labs/lplab/share/marc/repos/vcf2maf/vcf2vcf.pl'
GenomeAnalysisTK <- '/imppc/labs/lplab/share/bin/GenomeAnalysisTK-3.8-1-0-gf15c1c3ef/GenomeAnalysisTK.jar'
# correct vcf format
pass_vcfs <- list.files(out_path,
pattern = "_PASS.vcf$",
full.names = T,
recursive = T)
for (pass_vcf in pass_vcfs){
postCalling(pass_vcf = pass_vcf,
gatk4 = gatk4,
ref = ref,
bcftools = bcftools)
}
rh_vcfs <- list.files(out_path,
pattern = "_rhe.vcf$",
full.names = T,
recursive = T)
################
combined_vcf <- file.path(out_path, paste0(sample_name, "_combined.vcf"))
vcfs_list <- list.files(out_path,
pattern = "_rhe.vcf$",
full.names = T,
recursive = T)
system(paste('java -jar', GenomeAnalysisTK,
'-T CombineVariants',
'-R', ref,
'--variant', vcfs_list,
'-o', combined_vcf,
'-genotypeMergeOptions UNIQUIFY'))
df_vcf <- data.frame()
for(vcf in vcfs_list){
df_tmp <- read.table(vcf)
tmp_vect <- unlist(strsplit(gsub('_rhe.vcf', '', basename(vcf)), "_"))
df_tmp$sample <- tmp_vect[1]
df_tmp$soft <- tmp_vect[2]
df_tmp$type <- tmp_vect[3]
df_vcf <- rbind(df_vcf, df_tmp)
}
df_vcf$pos <- paste0(df_vcf$V1, "-", df_vcf$V2)
dplyr::summarise()
df_vcf_sub <- df_vcf[c('pos', 'soft')]
df_callers <- aggregate(df_vcf_sub$soft, list(df_vcf_sub$pos), paste, collapse="-")
df_callers$counts <- lapply(df_callers$x, function(x) unlist(strsplit(x, "-")))
library(ggplot2)
library(tidyverse)
library(ggupset)
tidy_callers <- as_tibble(df_callers)
tidy_callers$counts[8]
tidy_callers %>%
ggplot(aes(x=counts)) +
geom_bar() +
scale_x_upset(n_intersections = 20)
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