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new_analysis2.R
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# New Analysis BEGIN ------------------------------------------------------
# 计算1Mbp区间内突变频率与遗传、表观注释值的相关性
# Noncoding 和 Total区间
setwd("~/New_Analysis/")
source("functions.R")
library(data.table)
Sys.setenv(PATH=paste0("/home/zhangjing/bedtools2/bin:", Sys.getenv("PATH")))
# 预处理数据 -------------------------------------------------------------------
options(scipen = 30)
original_mut = "../icgc_data/预处理所有突变.tsv" # 经过预处理过的突变记录文件
if (F) {
mut <- fread(original_mut, data.table = F)
pos_mut <- mut[mut$mut == "single base substitution",]
###以bed格式输出所有单碱基突变
mut_bed <- pos_mut[,3:5]
mut_bed$start <- mut_bed$start - 1L
mut_bed$start = as.integer(mut_bed$start)
mut_bed$end = as.integer(mut_bed$end)
write.table(mut_bed, "single_mut.bed", sep = "\t", row.names = F, quote = F, col.names = F)
rm(mut, pos_mut, mut_bed); gc()
}
# 非编码区 --------------------------------------------------------------------
system("sort -k 1,1 -k 2,2n human.hg19.genome > sorted_human.hg19.genome") #bedtools包中附带文件
system("bedtools complement -i merged_sorted_cds_region.bed -g sorted_human.hg19.genome > region_noncoding.bed")
rm(list = ls()); gc()
source("functions.R")
# 获取不同区间上的突变数目 --------------------------------------------------------
region.coding = "m"
region.noncoding = "region_noncoding.bed"
original_bed = "single_mut.bed"
get_reg_mution(original_bed, region.noncoding, "mut_noncoding.bed")
# 分割基因组区间 -----------------------------------------------------------------
get_region_bed(1000000L)
system("sort -k 1,1 -k 2,2n hg19_mb_1M.bed > sorted_hg19_1M.bed")
# 计算突变数 -------------------------------------------------------------------
get_reg_mution_number("mut_noncoding.bed", "sorted_hg19_1M.bed", "1M_mut_noncoding.tsv")
system("sort -k 1,1 -k 2,2n single_mut.bed > sorted_single_mut.bed")
get_reg_mution_number("sorted_single_mut.bed", "sorted_hg19_1M.bed", "1M_mut_total.tsv")
# 计算突变的各种注释结果 -------------------------------------------------------------
# 这里取自师兄之前的结果
# 下载原数据
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/mutation_counts_mb.tsv",
destfile = "mutation_counts_mb.tsv")
# CpG
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/CpG_percent_Mb.bed",
destfile = "CpG_percent_Mb.bed")
# GC
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/hg19_gcMb.bed",
destfile = "hg19_gcMb.bed")
# Conservation
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/conservation_Mb.bed",
destfile = "conservation_Mb.bed")
# Reptime
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/reptime_Mb.bed",
destfile = "reptime_Mb.bed")
# polII
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/polII_mb.bed",
destfile = "polII_mb.bed")
# mappability
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/mean_mb_mappability.tsv",
destfile = "mean_mb_mappability.tsv")
# rec_rate
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/rec_rate.tsv",
destfile = "rec_rate.tsv")
# tfbs
download.file("https://raw.githubusercontent.com/zhangjing1589/noncoding/master/tfbs_mb.bed",
destfile = "tfbs_mb.bed")
# 收集遗传变异相关性 ---------------------------------------------------------------
rm(list = ls());gc()
#mut_noncoding = "1M_mut_noncoding.tsv"
mut_noncoding = "mutation_counts_mb.tsv" #为保证结果一致，这里使用与师兄一样的文件
mut_total = "1M_mut_total.tsv"
annot_CpG = "CpG_percent_Mb.bed"
annot_GC = "hg19_gcMb.bed"
annot_conservation = "conservation_Mb.bed"
annot_Reptime = "reptime_Mb.bed"
annot_polII = "polII_mb.bed"
annot_mappability = "mean_mb_mappability.tsv"
annot_rec_rate = "rec_rate.tsv"
annot_tfbs = "tfbs_mb.bed"
annot = c(annot_CpG, annot_GC, annot_conservation, annot_Reptime,
annot_polII, annot_mappability, annot_rec_rate, annot_tfbs)
# join by x with V4
# V4, V4, V1, V4, V1, V1, V4, V1
obtain_cor = function(annot_list, join_cols, mut_df) {
require(data.table)
out = data.table()
for (i in seq_along(annot_list)) {
message("Runing annotation #", i, ": ", annot_list[i])
value = fread(annot_list[i])
mut = fread(mut_df)
result = merge(value, mut, by.x = colnames(value)[join_cols[i]], by.y = "V4")
# calculate correlation
if (i == 5) {
result = result[(V3.x != 0) & (V5 !=0)]
tmp = data.table(
coeff = cor(result$V3.x, result$V5),
p_val = cor.test(result$V3.x, result$V5)$p.value
)
} else if (i == 7) {
result = result[(avg != 0) & (V5.y != 0)]
tmp = data.table(
coeff = cor(result$avg, result$V5.y),
p_val = cor.test(result$avg, result$V5.y)$p.value
)
} else {
result = result[(V5.x != 0) & (V5.x != ".") & (V5.y != 0) & (V5.y != ".") ]
result[, V5.x:=as.numeric(V5.x)]
result[, V5.y:=as.numeric(V5.y)]
tmp = data.table(
coeff = cor(result$V5.x, result$V5.y),
p_val = cor.test(result$V5.x, result$V5.y)$p.value
)
}
out = rbind(out, tmp)
}
out
}
cor_genetic = list()
cor_genetic[["noncoding"]] = obtain_cor(annot,
join_cols = c(4, 4, 1, 4, 1, 1, 4, 1),
mut_noncoding)
cor_genetic[["total"]] = obtain_cor(annot,
join_cols = c(4, 4, 1, 4, 1, 1, 4, 1),
mut_total)
cor_genetic[["noncoding"]]
cor_genetic[["total"]]
cor_genetic_df = rbindlist(cor_genetic, idcol = TRUE)
setnames(cor_genetic_df, ".id", "region")
cor_genetic_df[, features:=rep(c("CpG island", "GC content",
"Conservation", "Replication time",
"DNA poly II", "Mappability",
"Recombination rate", "TFBS"), 2)]
cor_genetic_df$features = factor(cor_genetic_df$features,
c("Mappability", "Replication time",
"TFBS", "GC content",
"CpG island", "DNA poly II",
"Conservation",
"Recombination rate"))
library(ggplot2)
library(cowplot)
library(RColorBrewer)
ggplot(cor_genetic_df, aes(x = features, y = coeff, fill=region)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_brewer(palette = "Paired", labels = c("Noncoding", "Total")) +
labs(x = "Genetic features", y = "Correlation coefficient", fill = "Region") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) -> p_genetic
save_plot("Genetic_corrplot_noncoding_vs_total.pdf", plot = p_genetic, base_aspect_ratio = 1.6)
cor_genetic_df2 = cor_genetic_df
setnames(cor_genetic_df2,
colnames(cor_genetic_df2),
c("Genomic region", "Correlation coefficient",
"P value", "Genetic feature"))
write.csv(as.data.frame(cor_genetic_df2), file = "遗传相关性结果-total-wsx.csv", quote = FALSE, row.names = FALSE)
# 表观相关性预处理 ----------------------------------------------------------------
rm(list = ls()); gc()
###按不同组织将肿瘤分类
mut <- fread("~/icgc_data/预处理所有突变.tsv")
donor <- fread("~/icgc_data/donor.tsv")
index <- donor[,1:2]
merge_result <- merge(mut, index, by.x = "donor", by.y = "icgc_donor_id")
dir.create("epi")
#将不同的肿瘤类型输出
#肝癌
liver <- c("LIAD-FR", "LICA-CN", "LICA-FR", "LINC-JP", "LIRI-JP")
liver_cancer <- merge_result[merge_result$project_code %in% liver,]
write.table(liver_cancer, "epi/liver_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(liver_cancer)
# #胃癌
# gastric <- c("STAD-US")
# gastric_cancer <- merge_result[merge_result$project_code %in% gastric,]
# write.table(gastric_cancer, "gastric_cancer_info.bed", sep = "\t", row.names = F, quote = F)
# rm(gastric_cancer)
#肾癌
kidney <- c("RECA-EU")
kidney_cancer <- merge_result[merge_result$project_code %in% kidney,]
write.table(kidney_cancer, "epi/kidney_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(kidney_cancer)
#肺癌
lung <- c("LUSC-CN","LUSC-KR")
lung_cancer <- merge_result[merge_result$project_code %in% lung,]
write.table(lung_cancer, "epi/lung_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(lung_cancer)
#乳腺癌
breast <- c("BRCA-EU","BRCA-FR","BRCA-US")
breast_cancer <- merge_result[merge_result$project_code %in% breast,]
write.table(breast_cancer, "epi/breast_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(breast_cancer)
#胰腺癌
pancreas <- c("PACA-AU","PACA-CA","PAEN-AU","PAEN-IT")
pancreas_cancer <- merge_result[merge_result$project_code %in% pancreas,]
write.table(pancreas_cancer, "epi/pancreas_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(pancreas_cancer)
#卵巢癌
ovary <- c("OV-AU")
ovary_cancer <- merge_result[merge_result$project_code %in% ovary,]
write.table(ovary_cancer, "epi/ovary_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(ovary_cancer)
#黑色素瘤
mela <- c("MELA-AU","SKCA-BR","SKCM-US")
mela_cancer <- merge_result[merge_result$project_code %in% mela,]
write.table(mela_cancer, "epi/mela_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(mela_cancer)
#食道癌
esophagus <- c("ESAD-UK","ESCA-CN")
esophagus_cancer <- merge_result[merge_result$project_code %in% esophagus,]
write.table(esophagus_cancer, "epi/esophagus_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(esophagus_cancer)
#血癌
blood <- c("ALL-US","CLLE-ES","MALY-DE","NKTL-SG")
blood_cancer <- merge_result[merge_result$project_code %in% blood,]
write.table(blood_cancer, "epi/blood_cancer_info.bed", sep = "\t", row.names = F, quote = F)
rm(blood_cancer)
# #结肠癌
# colon <- c("COAD-US","COCA-CN")
# colon_cancer <- merge_result[merge_result$project_code %in% colon,]
# write.table(colon_cancer, "colon_cancer_info.bed", sep = "\t", row.names = F, quote = F)
# rm(colon_cancer)
#
# #胶质瘤
# brain <- c("LGG-US")
# brain_cancer <- merge_result[merge_result$project_code %in% brain,]
# write.table(brain_cancer, "brain_cancer_info.bed", sep = "\t", row.names = F, quote = F)
# rm(brain_cancer)
rm(list = ls()); gc()
#删除chrM染色体
rm_chrM <- function(file)
{
df <- fread(file, data.table = F)
df <- df[df$V1 != "chrM",]
write.table(df, file, sep = "\t", row.names = F, quote = F, col.names = F)
}
##将表观遗传bed文件转化为bigwig文件
bed2bigwig <- function(bedfile, result_file)
{
temp1 <- paste("sorted_", bedfile, sep = "")
cmd1 <- paste("sort -k 1,1 -k 2,2n ",bedfile," > ",temp1, sep = "")
system(cmd1)
rm_chrM(temp1)
cmd2 <- paste("~/bedGraphToBigWig ",temp1,
"~/New_Analysis/sorted_human.hg19.genome",
file.path("~/New_Analysis/bigwig", result_file))
system(cmd2)
file.remove(temp1)
}
# #将DNase文件转换为bed 格式
# toBed <- function(file)
# {
# df <- fread(file, data.table = F)
# df <- df[,c(1:3,5)]
# write.table(df, file, sep = "\t", row.names = F, quote = F, col.names = F)
# }
dir.create("~/New_Analysis/bigwig")
setwd("~/roadmap/lung/bed/")
#肺癌 E096
##将注释文件换成二进制
bed2bigwig("E096-H3K27ac.broadPeak.bed","E096-H3K27ac.broadPeak.bigwig")
bed2bigwig("E096-H3K27me3.broadPeak.bed","E096-H3K27me3.broadPeak.bigwig")
bed2bigwig("E096-H3K36me3.broadPeak.bed","E096-H3K36me3.broadPeak.bigwig")
bed2bigwig("E096-H3K4me1.broadPeak.bed","E096-H3K4me1.broadPeak.bigwig")
bed2bigwig("E096-H3K4me3.broadPeak.bed","E096-H3K4me3.broadPeak.bigwig")
bed2bigwig("E096-H3K9me3.broadPeak.bed","E096-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/liver/bed/")
#肝癌 E066
##将注释文件换成二进制
bed2bigwig("E066-H3K27ac.broadPeak.bed","E066-H3K27ac.broadPeak.bigwig")
bed2bigwig("E066-H3K27me3.broadPeak.bed","E066-H3K27me3.broadPeak.bigwig")
bed2bigwig("E066-H3K36me3.broadPeak.bed","E066-H3K36me3.broadPeak.bigwig")
bed2bigwig("E066-H3K4me1.broadPeak.bed","E066-H3K4me1.broadPeak.bigwig")
bed2bigwig("E066-H3K4me3.broadPeak.bed","E066-H3K4me3.broadPeak.bigwig")
bed2bigwig("E066-H3K9ac.broadPeak.bed","E066-H3K9ac.broadPeak.bigwig")
bed2bigwig("E066-H3K9me3.broadPeak.bed","E066-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/Pancreas/bed/")
#胰腺癌 E098
bed2bigwig("E098-DNase.hotspot.broad.bed","E098-DNase.hotspot.broad.bigwig")
bed2bigwig("E098-H3K27ac.broadPeak.bed","E098-H3K27ac.broadPeak.bigwig")
bed2bigwig("E098-H3K27me3.broadPeak.bed","E098-H3K27me3.broadPeak.bigwig")
bed2bigwig("E098-H3K36me3.broadPeak.bed","E098-H3K36me3.broadPeak.bigwig")
bed2bigwig("E098-H3K4me1.broadPeak.bed","E098-H3K4me1.broadPeak.bigwig")
bed2bigwig("E098-H3K4me3.broadPeak.bed","E098-H3K4me3.broadPeak.bigwig")
bed2bigwig("E098-H3K9me3.broadPeak.bed","E098-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/Kidney/bed/")
#肾癌 E086
bed2bigwig("E086-DNase.hotspot.broad.bed","E086-DNase.hotspot.broad.bigwig")
bed2bigwig("E086-H3K27me3.broadPeak.bed","E086-H3K27me3.broadPeak.bigwig")
bed2bigwig("E086-H3K36me3.broadPeak.bed","E086-H3K36me3.broadPeak.bigwig")
bed2bigwig("E086-H3K4me1.broadPeak.bed","E086-H3K4me1.broadPeak.bigwig")
bed2bigwig("E086-H3K4me3.broadPeak.bed","E086-H3K4me3.broadPeak.bigwig")
bed2bigwig("E086-H3K9ac.broadPeak.bed","E086-H3K9ac.broadPeak.bigwig")
bed2bigwig("E086-H3K9me3.broadPeak.bed","E086-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/Esophagus/bed/")
#食道癌 E079
bed2bigwig("E079-H3K27ac.broadPeak.bed","E079-H3K27ac.broadPeak.bigwig")
bed2bigwig("E079-H3K27me3.broadPeak.bed","E079-H3K27me3.broadPeak.bigwig")
bed2bigwig("E079-H3K36me3.broadPeak.bed","E079-H3K36me3.broadPeak.bigwig")
bed2bigwig("E079-H3K4me1.broadPeak.bed","E079-H3K4me1.broadPeak.bigwig")
bed2bigwig("E079-H3K4me3.broadPeak.bed","E079-H3K4me3.broadPeak.bigwig")
bed2bigwig("E079-H3K9me3.broadPeak.bed","E079-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/blood/bed/")
#血癌 E062
bed2bigwig("E062-H3K27ac.broadPeak.bed","E062-H3K27ac.broadPeak.bigwig")
bed2bigwig("E062-H3K27me3.broadPeak.bed","E062-H3K27me3.broadPeak.bigwig")
bed2bigwig("E062-H3K36me3.broadPeak.bed","E062-H3K36me3.broadPeak.bigwig")
bed2bigwig("E062-H3K4me1.broadPeak.bed","E062-H3K4me1.broadPeak.bigwig")
bed2bigwig("E062-H3K4me3.broadPeak.bed","E062-H3K4me3.broadPeak.bigwig")
bed2bigwig("E062-H3K9ac.broadPeak.bed","E062-H3K9ac.broadPeak.bigwig")
bed2bigwig("E062-H3K9me3.broadPeak.bed","E062-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/breast/bed/")
#乳腺癌 E028
bed2bigwig("E028-DNase.hotspot.broad.bed","E028-DNase.hotspot.broad.bigwig")
bed2bigwig("E028-H3K27me3.broadPeak.bed","E028-H3K27me3.broadPeak.bigwig")
bed2bigwig("E028-H3K36me3.broadPeak.bed","E028-H3K36me3.broadPeak.bigwig")
bed2bigwig("E028-H3K4me1.broadPeak.bed","E028-H3K4me1.broadPeak.bigwig")
bed2bigwig("E028-H3K4me3.broadPeak.bed","E028-H3K4me3.broadPeak.bigwig")
bed2bigwig("E028-H3K9me3.broadPeak.bed","E028-H3K9me3.broadPeak.bigwig")
#黑色素瘤 E059 E061
setwd("~/roadmap/E059_Melanoma/bed/")
bed2bigwig("E059-DNase.hotspot.broad.bed","E059-DNase.hotspot.broad.bigwig")
bed2bigwig("E059-H3K27ac.broadPeak.bed","E059-H3K27ac.broadPeak.bigwig")
bed2bigwig("E059-H3K27me3.broadPeak.bed","E059-H3K27me3.broadPeak.bigwig")
bed2bigwig("E059-H3K36me3.broadPeak.bed","E059-H3K36me3.broadPeak.bigwig")
bed2bigwig("E059-H3K4me1.broadPeak.bed","E059-H3K4me1.broadPeak.bigwig")
bed2bigwig("E059-H3K4me3.broadPeak.bed","E059-H3K4me3.broadPeak.bigwig")
bed2bigwig("E059-H3K9me3.broadPeak.bed","E059-H3K9me3.broadPeak.bigwig")
setwd("~/roadmap/E061_Melanoma/bed/")
bed2bigwig("E061-H3K27ac.broadPeak.bed","E061-H3K27ac.broadPeak.bigwig")
bed2bigwig("E061-H3K27me3.broadPeak.bed","E061-H3K27me3.broadPeak.bigwig")
bed2bigwig("E061-H3K36me3.broadPeak.bed","E061-H3K36me3.broadPeak.bigwig")
bed2bigwig("E061-H3K4me1.broadPeak.bed","E061-H3K4me1.broadPeak.bigwig")
bed2bigwig("E061-H3K4me3.broadPeak.bed","E061-H3K4me3.broadPeak.bigwig")
bed2bigwig("E061-H3K9me3.broadPeak.bed","E061-H3K9me3.broadPeak.bigwig")
#卵巢癌 E097
setwd("~/roadmap/Ovary/bed/")
bed2bigwig("E097-DNase.hotspot.broad.bed","E097-DNase.hotspot.broad.bigwig")
bed2bigwig("E097-H3K27ac.broadPeak.bed","E097-H3K27ac.broadPeak.bigwig")
bed2bigwig("E097-H3K27me3.broadPeak.bed","E097-H3K27me3.broadPeak.bigwig")
bed2bigwig("E097-H3K36me3.broadPeak.bed","E097-H3K36me3.broadPeak.bigwig")
bed2bigwig("E097-H3K4me1.broadPeak.bed","E097-H3K4me1.broadPeak.bigwig")
bed2bigwig("E097-H3K4me3.broadPeak.bed","E097-H3K4me3.broadPeak.bigwig")
bed2bigwig("E097-H3K9me3.broadPeak.bed","E097-H3K9me3.broadPeak.bigwig")
setwd("~/New_Analysis/")
###计算MB范围的突变数
get_reg_mution_number2 = function(orig_file, mutation_file, region_file, result_file){
require(data.table)
options(scipen = 30)
mut <- fread(orig_file, data.table = F)
pos_mut <- mut[mut$mut == "single base substitution",]
mut_bed <- pos_mut[,3:5]
mut_bed$start <- mut_bed$start - 1L
write.table(mut_bed, mutation_file, sep = "\t", row.names = F, quote = F, col.names = F)
rm(mut, mut_bed); gc()
# 获取各个要计算的基因组区间
#region.noncoding = "region_noncoding.bed"
region.total = "region_total.bed"
#mutation_file.noncoding = file.path("~/New_Analysis/epi/mutation/", paste0("mut_noncoding_", basename(mutation_file)))
mutation_file.total = file.path("~/New_Analysis/epi/mutation/", paste0("mut_total_", basename(mutation_file)))
#get_reg_mution(mutation_file, region.noncoding, mutation_file.noncoding)
get_reg_mution(mutation_file, region.total, mutation_file.total)
#result_file.noncoding = file.path("~/New_Analysis/epi/mutation/", paste0("noncoding_", basename(result_file)))
result_file.total = file.path("~/New_Analysis/epi/mutation/", paste0("total_", basename(result_file)))
# 获取不同区间的1M区间突变
#get_reg_mution_number(mutation_file.noncoding, region_file, result_file.noncoding)
get_reg_mution_number(mutation_file.total, region_file, result_file.total)
}
hg19 = fread("sorted_human.hg19.genome")
hg19$V3 = 0L
hg19 = hg19[, c(1, 3, 2)]
write.table(hg19, file = "region_total.bed", quote = FALSE, sep = "\t", col.names = FALSE, row.names = FALSE)
# Noncoding之前算过了，这里注释掉
# 批量计算不同癌症类型和区间的突变数
for (f in dir("epi", pattern = "_info.bed", full.names = T)){
mut_file = file.path("epi/mutation", sub("_info", "", basename(f)))
reg_file = "sorted_hg19_1M.bed"
result_file = paste0("1M_", sub("_info", "", basename(f)))
get_reg_mution_number2(f, mut_file, reg_file, result_file)
}
###计算每个MB区间的表观遗传值
dir.create("bigwig/output")
for (f in list.files("bigwig", pattern = "^E", full.names = TRUE, include.dirs = FALSE)) {
out = file.path("bigwig/output/", sub("([^\\.]*)\\..*", "\\1", basename(f)))
cmd <- paste("~/bigWigAverageOverBed", f, "sorted_hg19_1M.bed", out, sep = " ")
system(cmd)
}
rm(list = ls()); gc()
#求两列数据的平均值
cal_mean <- function(file1, file2, result_file)
{
file1_1 <- fread(file1, data.table = F)
file2_2 <- fread(file2, data.table = F)
file.copy(from = file1, to = "mela")
file.copy(from = file2, to = "mela")
file.remove(file1)
file.remove(file2)
merge_result <- merge(file1_1, file2_2, by.x = "V1", by.y = "V1")
mt <- merge_result[,c(5,10)]
mt <- as.matrix(mt)
mean_value <- apply(mt, 1, mean)
merge_result$mean <- mean_value
df <- merge_result[,c(1:4,12,6)]
write.table(df, result_file, sep = "\t", row.names = F, col.names = F, quote = F)
}
setwd("~/New_Analysis/bigwig/output/")
dir.create("mela") # back up files not used
cal_mean("E059-H3K36me3","E061-H3K36me3","E059_E061-H3K36me3")
cal_mean("E059-H3K9me3","E061-H3K9me3","E059_E061-H3K9me3")
cal_mean("E059-H3K27ac","E061-H3K27ac","E059_E061-H3K27ac")
cal_mean("E059-H3K4me1","E061-H3K4me1","E059_E061-H3K4me1")
cal_mean("E059-H3K27me3","E061-H3K27me3","E059_E061-H3K27me3")
cal_mean("E059-H3K4me3","E061-H3K4me3","E059_E061-H3K4me3")
# mapping of cancer and code
# lung E096
# liver E066
# ovary E097
# pancreas E098
# kidney E086
# esophagus E079
# blood E062
# breast E028
# melanoma E059 E061
mut_files = c(list.files("~/New_Analysis/epi/mutation", pattern = "noncoding_1M", full.names = TRUE),
list.files("~/New_Analysis/epi/mutation", pattern = "total_1M", full.names = TRUE))
annot_files = list.files("~/New_Analysis/bigwig/output/", pattern = "^E", full.names = TRUE)
mappings = c("blood", "breast", "esophagus", "kidney", "liver", "lung", "ovary", "pancreas", "mela")
names(mappings) = c("E062", "E028", "E079", "E086", "E066", "E096", "E097", "E098", "E059")
obtain_cor2 = function(mappings, mut_files, annot_files, types = c("coding", "noncoding", "promoter", "nonpromoter")) {
require(data.table)
options(scipen = 30)
out = data.table()
for (i in seq_along(mappings)) {
message("Processing tumor type - ", mappings[i], " - ", names(mappings)[i])
message(">> Searching mutation files...")
type_mut = grep(mappings[i], mut_files, value = TRUE)
message(">> OK")
message(">> Searching annotation files...")
type_anno = grep(names(mappings)[i], annot_files, value = TRUE)
message(">> OK")
for (type in types) {
message(">> Extracting region - ", type)
region_mut = grep(paste0("/",type), type_mut, value = TRUE)
message(">> OK")
message(">> Calculating correaltion...")
mut = fread(region_mut)
for(f in type_anno) {
anno_type = unlist(strsplit(basename(f), split = "-"))[2]
anno = fread(f)
result = merge(mut, anno, by.x = "V4", by.y = "V1")
# calculate correlation
result = result[(V5.x != 0) & (V5.x != ".") & (V5.y != 0) & (V5.y != ".") ]
result[, V5.x:=as.numeric(V5.x)]
result[, V5.y:=as.numeric(V5.y)]
tmp = data.table(
tumor_type = mappings[i],
region = type,
anno_type = anno_type,
coeff = cor(result$V5.x, result$V5.y),
p_val = cor.test(result$V5.x, result$V5.y)$p.value)
out = rbind(out, tmp)
}
message(">> OK")
}
}
message("Done")
out
}
annot_cor = obtain_cor2(mappings, mut_files, annot_files, c("noncoding", "total"))
setwd("~/New_Analysis/")
# save
annot_cor2 = data.table::copy(annot_cor)
setnames(annot_cor2,
colnames(annot_cor2),
c("Tumor type" ,"Genomic region",
"Epigenetic type",
"Correlation coefficient",
"P value"))
write.csv(as.data.frame(annot_cor2), file = "表观相关性结果-total-wsx.csv", quote = FALSE, row.names = FALSE)
# plot
plot_df = list()
plot_df[["noncoding"]] = annot_cor["noncoding", on = "region"][, c("region", "p_val"):=NULL]
plot_df[["total"]] = annot_cor["total", on = "region"][, c("region", "p_val"):=NULL]
plot_heatmap = function(df){
df_wide = tidyr::spread(data = df, tumor_type, coeff) %>%
tibble::column_to_rownames("anno_type")
rownames(df_wide)[rownames(df_wide) == "DNase"] = "DNase.hotspot"
colnames(df_wide)[colnames(df_wide) == "mela"] = "melanoma"
# row_order = c("H3K4me3", "DNase.hotspot", "H3K9ac",
# "H3K4me1", "H3K27ac", "H3K36me3",
# "H3K27me3", "H3K9me3")
# col_order = c("esophagus", "lung", "kidney",
# "blood", "breast", "liver",
# "melanoma", "ovary", "pancreas")
pheatmap::pheatmap(df_wide, cluster_rows = F, cluster_cols = F)
}
plot_heatmap(plot_df[["noncoding"]])
plot_heatmap(plot_df[["total"]])
# output pdf set width 6, height 5