特异性分析
#设定工作路径
setwd("D:/DMP_mk_")
#加载需要的R包
#rm(list=ls())
library(reshape2)
library(dplyr)
#加载需要的数据
rm(list = ls())
#单个甲基化位点
meth <- read.table("meth_1.txt")#改动的地方
exprset <- read.table("exprset.txt")
#启动子区域平均甲基化
meth_prom <- read.table("meth_prom_250updown.txt")#改动的地方
meth_spm <- read.table("meth_1.txt.spm") #改动的地方
#表达矩阵
exprset_spm <- read.table("exprset.txt.spm")
meth_prom_spm <- read.table("meth_prom_250updown.txt.spm")#改动的地方
#甲基化位点注释信息
load(file = "anno_1.Rdata")#改动的地方
#生成已经添加行名和列名的长数据
meth_prom_L <- cbind(rep(rownames(meth_prom),ncol(meth_prom)),melt(meth_prom))
colnames(meth_prom_L) <- c("gene","tissue","rate")
meth_prom_spm_L <- cbind(rep(rownames(meth_prom),ncol(meth_prom_spm)),melt(meth_prom_spm))
colnames(meth_prom_spm_L) <- c("gene","tissue","spm")
exprset_L <- cbind(rep(rownames(exprset),ncol(exprset)),melt(exprset))
colnames(exprset_L) <- c("gene","tissue","value")
exprset_spm_L <- cbind(rep(rownames(exprset),ncol(exprset_spm)),melt(exprset_spm))
colnames(exprset_spm_L) <- c("gene","tissue","spm")
meth_L <- melt(meth)
meth_spm_L <- melt(meth_spm)
#启动子甲基化与基因表达的关系
vs <- read.csv(file="vs.csv")
table(meth_prom_L[,1] %in% vs[,1])
gene_symbol <- vs[match(meth_prom_L[,1],vs[,1]),2]
tmp <- transform(meth_prom_L,gene = gene_symbol)
merge_1 <- merge(exprset_L,tmp,by = c("gene","tissue"))
length(merge_1)
png(file="correlation.png", bg="transparent")
plot(merge_1$rate,merge_1$value)
dev.off()
cor.test(merge_1$value, merge_1$rate,method = c("pearson"))
#组织特异性甲基化区域与组织特异性表达的关系
meth_prom_plus <- merge(meth_prom_L,meth_prom_spm_L,by = c("gene","tissue"))
exprset_plus <- merge(exprset_L,exprset_spm_L,by = c("gene","tissue"))
methpromspec <- filter(meth_prom_plus,spm>0.6)
exprsetspec <- filter(exprset_plus,spm>0.9)
vs <- read.csv(file="vs.csv")
table(methpromspec[,1] %in% vs[,1])
gene_symbol_1 <- vs[match(methpromspec[,1],vs[,1]),2]
tmp_1 <- transform(methpromspec,gene = gene_symbol_1)
merge_2 <- merge(tmp_1,exprsetspec,by = c("gene","tissue"))
nrow(methpromspec)
nrow(exprsetspec)
nrow(merge_2)
#组织特异性甲基化位点与组织特异性表达的关系
meth_spm_L <- filter(meth_spm_L,variable != "DPM")
meth_plus <- cbind(meth_L,meth_spm_L$value)
colnames(meth_plus) <- c("tissue","rate","spm")
meth_plus <- cbind(anno_1,meth_plus)#改动的地方
methspec <- filter(meth_plus,spm>0.9)
merge_3 <- merge(methspec,exprsetspec,by.x = "gene_symbol",by.y = "gene")
nrow(methspec)
nrow(merge_3)
得到1495个组织特异性的启动子甲基化区域。（由12118个启动子甲基化区域得到）
不重复的基因只有1392个，有一些基因在不只一个组织中，也有的基因在组织中重复出现。可能是多个ucsc name对应到了一个gene symbol上。
12059个组织特异性基因。（由36598个基因得到）
overlap数目为24个。（0.01605351）
32312个组织特异性的甲基化位点。（由715765个位点得到）
overlap数目为5455个。（ 0.1688227）
1.相关性分析结果
> cor.test(merge_1$value, merge_1$rate,method = c("pearson"))
Pearson's product-moment correlation
data: merge_1$value and merge_1$rate
t = -5.1149, df = 143011, p-value = 3.142e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.018705797 -0.008342175
sample estimates:
cor
-0.01352435
2.epigenome association analysis
load(file = "united_data_1.Rdata")#改动的地方
coordinate <- data.frame(meth_1$chr,meth_1$start,meth_1$strand)#改动的地方
coordinate <- coordinate[rep(1:nrow(meth_1),times=ncol(meth)),]
meth_plus_ <- cbind(meth_plus,coordinate)
#筛选出特异性的甲基化位点
methspec <- filter(meth_plus_,spm>0.8)
#得到染色体位置对应的探针，做表观关联分析
colnames(methspec)[11] <- "chr"
colnames(methspec)[12] <- "pos"
colnames(methspec)[13] <- "strand"
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
data(Locations)#含有染色体位置和探针信息
Locations <- data.frame(transform(Locations,"probe"=rownames(Locations)))
specific_probe <- data.frame(merge(Locations,methspec,
by=c("chr","pos","strand")))
for (i in 1:13)
{
assign(paste0(unique(specific_probe$tissue)[i]),
unique(specific_probe[specific_probe$tissue==
unique(specific_probe$tissue)[i],4]))
filename=paste0(unique(specific_probe$tissue)[i],".txt")
write.table(get(paste0(unique(specific_probe$tissue)[i])),
file = filename,quote = F,row.names = F,col.names = F)
}
#验证是否只在一个组织中特异
library(tidyfst)
methspec %>% count_dt(chr,pos,strand) -> count_methspec
使用工具：https://bigd.big.ac.cn/ewas/toolkit
3.对组织特异性的位点进行描述性统计
高甲基化or低甲基化；
> dim(methspec)
[1] 32312 10
> sum(methspec$rate<20)
[1] 26438
> sum(methspec$rate>70)
[1] 162
在基因的哪个区域；
sum(methspec$prom==1)
[1] 14252
> sum(methspec$exon==1)
[1] 11419
> sum(methspec$intron==1)
[1] 14457
> sum(methspec$CpGi==1 | methspec$shores==1)
[1] 30196
> sum(methspec$CpGi==1)
[1] 28110
> sum(methspec$shores==1)
[1] 2086
在哪些组织；
4.组织特异性基因go和KEGG富集分析
setwd("D:/genes")
#加载包
library(clusterProfiler)
library(topGO)
library(DO.db)
library(pathview)
library(DOSE)
library(org.Hs.eg.db)
library(Rgraphviz)
i=5
#得到gene_symbol
unique(exprsetspec$tissue)[i]
assign(paste0("x",i),unique(exprsetspec[exprsetspec$tissue==
unique(exprsetspec$tissue)[i],1]))
#id转换
eg <- bitr( get(paste0("x",i)), fromType="SYMBOL", toType=c("ENTREZID"),
OrgDb="org.Hs.eg.db")
head(eg)
assign(paste0("genelist",i),eg[,2])
#GO富集分析
go <- enrichGO( get(paste0("genelist",i)), OrgDb = org.Hs.eg.db, ont='ALL',
pAdjustMethod = 'BH',
pvalueCutoff = 0.05, qvalueCutoff = 0.2,keyType = 'ENTREZID')
#查看主要富集到哪条通路
dim(go)
dim(go[go$ONTOLOGY=='BP',])
dim(go[go$ONTOLOGY=='CC',])
dim(go[go$ONTOLOGY=='MF',])
#简单的可视化
filename=paste0("GObarplot_",unique(exprsetspec$tissue)[i],".png")
png(file=filename, bg="transparent",height = 480,width = 700)
barplot(go,showCategory=20,drop=T)
dev.off()
filename=paste0("GOdotplot_",unique(exprsetspec$tissue)[i],".png")
png(file=filename, bg="transparent",height = 480,width = 700)
dotplot(go,showCategory=20,orderBy = "x")
dev.off()
#KEGG富集分析
if(F)
{
kegg <- enrichKEGG(
get(paste0("genelist",i)),
organism = "hsa",
keyType = "kegg",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
minGSSize = 10,
maxGSSize = 500,
qvalueCutoff = 0.2,
use_internal_data = FALSE
)
head(kegg)
dim(kegg)
filename=paste0("kegg_",unique(exprsetspec$tissue)[i],".png")
png(file=filename, bg="transparent")
dotplot(kegg, showCategory=30,orderBy = "x")
dev.off()
}
#疾病通路富集分析
DO <- enrichDO(
get(paste0("genelist",i)),
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
minGSSize = 10,
maxGSSize = 500,
qvalueCutoff = 0.2,
readable = FALSE
)
dim(DO)
filename=paste0("DO_",unique(exprsetspec$tissue)[i],".png")
png(file= filename, bg="transparent",height = 480,width = 480,)
dotplot(DO, showCategory=20,orderBy = "x")
dev.off()
i=i+1
不知道为什么循环运行之后图片就都显示不出来，没有找到具体的原因，所以只好手动。