#if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install("ConsensusClusterPlus")

#if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install("limma")

library(limma)

rt=read.table("合并后的铜死亡基因矩阵.txt",sep="\t",header=T,check.names=F)              #读取输入文件

#一个基因出现多行，取均值
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
data=avereps(data)
data=data[rowMeans(data)>0,]

#删掉正常样品
group=sapply(strsplit(colnames(data),"\\-"),"[",4)
group=sapply(strsplit(group,""),"[",1)
group=gsub("2","1",group)
data=data[,group==0]


#聚类
maxK=9
library(ConsensusClusterPlus)
results = ConsensusClusterPlus(data,
              maxK=maxK,
              reps=100,
              pItem=0.8,
              pFeature=1,
           
              clusterAlg="km",
              distance="euclidean",
              seed=123456,
              plot="png")
icl <- calcICL(results, title = title,plot = "png")
#输出结果
clusterNum=2                  #分几类，根据判断标准判断
cluster=results[[clusterNum]][["consensusClass"]]
write.table(cluster,file="cluster.txt",sep="\t",quote=F,col.names=F)