分析测序数据时,常常需要将counts数据转换为TPM格式,而这个转变过程就需要涉及每个基因的长度,幸好有专业人士已经帮我们处理好这个东东,我们可以一键进行操作。
首先来认识下这个牛气冲天的R包IOBR(Immuno-Oncology Biological Research):
IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.
![](https://img.haomeiwen.com/i17982813/97829a011c745e57.png)
提供8种计算免疫细胞浸润的方法,收录255种构建的signature,这么强大的功能咱们以后慢慢学,这次先学习下
count2tpm
功能。
#devtools::install_github("IOBR/IOBR",ref="master")
rm(list = ls())
library(IOBR)
library(UCSCXenaTools)
help("count2tpm")
eset_prad<-XenaGenerate(subset = XenaCohorts =="GDC TCGA Prostate Cancer (PRAD)") %>%
XenaFilter(filterDatasets = "TCGA-PRAD.htseq_counts.tsv") %>%
XenaQuery() %>%
XenaDownload() %>%
XenaPrepare()
eset_prad$Ensembl_ID <- substring(eset_prad$Ensembl_ID,1,15)
eset_prad <- column_to_rownames(eset_prad,var = "Ensembl_ID")
eset_prad<-(2^eset_prad)+1
eset_prad <- count2tpm(countMat = eset_prad,idType = "Ensembl",source = "default")
![](https://img.haomeiwen.com/i17982813/3f92b1cd1b4be339.png)
参考链接:
IOBR: 一步完成RNAseq: counts到TPM的转化
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