4.将差异甲基化位点拿来做批量生存分析
rm(list = ls())
library(data.table)
library(stringr)
library(survival)
library(survminer)
load('./Rdata/step2_filtered_pd_myNorm.Rdata')
load('./Rdata/step3.df_DMP.Rdata')
cg <- rownames(df_DMP[df_DMP$change != 'NOT',])
myNorm_tumor <- myNorm[cg,]
logrank test批量生存分析
这里和TCGA系列里的内容是一样的,详见:两种方法批量做生存分析
suv_dat <- data.table::fread('./raw_data/TCGA-HNSC.survival.tsv.gz')
suv_dat$sample = str_sub(suv_dat$sample,1,15)
suv_dat <- suv_dat[suv_dat$sample %in% colnames(myNorm_tumor),]
suv_dat <- suv_dat[str_sub(suv_dat$sample,14,15)=='01',]
suv_dat = merge(pd,suv_dat,by.x = 'sampleID',by.y = 'sample')
myNorm_tumor <- myNorm_tumor[,suv_dat$sample]
identical(colnames(myNorm_tumor),suv_dat$sample)
#> [1] TRUE
library(survival)
logrankP <- apply(myNorm_tumor, 1, function(x){
#x <- myNorm_tumor[1,]
suv_dat$group <- ifelse(x>mean(x),'High','Low')
res <- coxph(Surv(OS.time, OS)~group, data=suv_dat)
beta <- coef(res)
se <- sqrt(diag(vcov(res)))
p.val <- 1 - pchisq((beta/se)^2, 1)
})
table(logrankP<0.05) #110个CpG位点
#>
#> FALSE TRUE
#> 1676 110
得到110个对生存影响显著的差异甲基化位点,取前20个画图。
surv_gene <- names(sort(logrankP))[1:20]
choose_matrix <- myNorm[surv_gene,]
annotation_col <- data.frame(Sample=pd$group_list)
rownames(annotation_col) <- colnames(choose_matrix)
ann_colors = list(Sample = c(Normal='#4DAF4A', Tumor='#E41A1C'))
library(pheatmap)
pheatmap(choose_matrix,show_colnames = T,
annotation_col = annotation_col,
border_color=NA,
color = colorRampPalette(colors = c('white','navy'))(50),
annotation_colors = ann_colors)
image.png也可以画画生存分析的图
gs=head(surv_gene,4)
exprSet = myNorm_tumor
meta = suv_dat
splots <- lapply(gs, function(g){
meta$gene=ifelse(exprSet[g,]>median(exprSet[g,]),'high','low')
sfit1=survfit(Surv(OS.time, OS)~gene, data=meta)
ggsurvplot(sfit1,pval =TRUE, data = meta)
})
arrange_ggsurvplots(splots, print = TRUE,
ncol = 2, nrow = 2)
5.富集分析
利用ChAMP包对过滤后的数据做了差异甲基化位点分析。如果是肿瘤数据的话,可以加一步生存分析。
rm(list = ls())
load(file = 'Rdata/step3.df_DMP.Rdata')
library(ggplot2)
library(stringr)
library(clusterProfiler)
library(org.Hs.eg.db)
ID转换
length(unique(df_DMP$gene))
#> [1] 16338
s2e <- bitr(unique(df_DMP$gene), fromType = 'SYMBOL',
toType = c( 'ENTREZID'),
OrgDb = org.Hs.eg.db)
df_DMP=merge(df_DMP,s2e,by.y='SYMBOL',by.x='gene')
table(!duplicated(df_DMP$ENTREZID))
#>
#> FALSE TRUE
#> 83778 14188
gene_up= unique(df_DMP[df_DMP$change == 'UP','ENTREZID'] )
gene_down=unique(df_DMP[df_DMP$change == 'DOWN','ENTREZID'] )
gene_diff=c(gene_up,gene_down)
gene_all=unique(df_DMP$ENTREZID)
富集分析及其可视化
之前完整的富集分析会把上下调基因分开、合并都做。GO富集分析会将MF、CC、BP三个部分分开做。代码太多了,在这简化一下。
kkgo_file = './Rdata/kkgo_file.Rdata'
if(!file.exists(kkgo_file)){
kk <- enrichKEGG(gene = gene_diff,
universe = gene_all,
organism = 'hsa',
pvalueCutoff = 0.05)
go <- enrichGO(gene_diff, OrgDb = 'org.Hs.eg.db', ont='all')
save(kk,go,file = kkgo_file)
}
load(kkgo_file)
dotplot(kk)
image.pngbarplot(go, split='ONTOLOGY',font.size =10)+
facet_grid(ONTOLOGY~., scale='free') +
scale_x_discrete(labels=function(x) str_wrap(x, width=45))