1、Count、FPKM、RPKM、TPM之间的转换

TPM相比于FPKM\RPKM，更加适用于不同样本之间的比较

#TPM (Transcripts Per Kilobase Million)  每千个碱基的转录每百万映射读取的Transcripts 
counts2TPM <- function(count=count, efflength=efflen){     
  RPK <- count/(efflength/1000)   #每千碱基reads (reads per kilobase) 长度标准化     
  PMSC_rpk <- sum(RPK)/1e6        #RPK的每百万缩放因子 (“per million” scaling factor ) 深度标准化     
  RPK/PMSC_rpk                       }     

#FPKM/RPKM (Fragments/Reads Per Kilobase Million )  每千个碱基的转录每百万映射读取的Fragments/reads 
#RPKM与FPKM分别针对单端与双端测序而言，计算公式是一样的 
counts2FPKM <- function(count=count, efflength=efflen){      
  PMSC_counts <- sum(count)/1e6   #counts的每百万缩放因子 (“per million” scaling factor) 深度标准化     
  FPM <- count/PMSC_counts        #每百万reads/Fragments (Reads/Fragments Per Million) 长度标准化     
  FPM/(efflength/1000)                                       } 

#FPKM与TPM的转化 
FPKM2TPM <- function(fpkm){     
  fpkm/sum(fpkm)*1e6   } 

tpm <- as.data.frame(apply(counts,2,counts2TPM)) 
colSums(tpm) 

fpkm <- as.data.frame(apply(counts,2,counts2FPKM)) 
colSums(fpkm) 

tpm0 <- as.data.frame(apply(counts,2,FPKM2TPM)) 
colSums(tpm0)

> https://mp.weixin.qq.com/s/IUV9dSbRBK1nvetixKOCRw

2、一致性聚类（consensusclusterplus）
1）原理解读——https://blog.csdn.net/nixiang_888/article/details/122224201?fromshare=blogdetail&sharetype=blogdetail&sharerId=122224201&sharerefer=PC&sharesource=weixin_71400734&sharefrom=from_link