如何用转录组数据定量肿瘤浸润免疫细胞

有哪些方法可以用于评判肿瘤免疫浸润

Tool	Type	Method	Cell types	Code availability	CellMix	References
TIminer	M	PrerankedGSEA	Different gene sets with 31 [10], 28 [11], and 64 cell types [12]	http://icbi.i-med.ac.at/software/timiner/timiner.shtml(Docker image)		[13]
xCell	M	ssGSEA	64 immune and non-immune cell types	http://xcell.ucsf.edu/ (R script, web tool)		[12]
MCP-counter	M	Geometric mean of expression of marker genes	8 immune cells, fibroblasts, and endothelial cells	http://github.com/ebecht/MCPcounter (R script)		[14]
–	P	Linear least squares regression	17 immune cell types		lsfit	[15]
–	P	Constrained least square regression	–		qprog	[16]
DeconRNASeq	P	Constrained least square regression	–	DeconRNASeq package available on Bioconductor (R package)		[17]
PERT	P	Non-negative maximum likelihood		Supplementary material in the original publication (Octave)		[18]
CIBERSORT	P	Nu support vector regression	22 immune cell types	https://cibersort.stanford.edu/ (R script, java executable, web tool)		[19]
TIMER	P	Linear least square regression	6 immune cell types	https://cistrome.shinyapps.io/timer/ (web tool)		[20]
EPIC	P	Constrained least square regression	6 immune cell types, fibroblasts, endothelial cells, and uncharacterized cells	https://gfellerlab.shinyapps.io/EPIC_1-1 (R script, web-interface)		[21]
quanTIseq	P	Constrained least square regression	10 immune cell types, uncharacterized cells	http://icbi.i-med.ac.at/software/quantiseq/doc/index.html(Docker image)		[22]
deconf	C	Non-negative matrix factorization	-	Supplementary material in the original publication (R package)	deconf	[23]
ssKL	C	Non-negative matrix factorization	–		ssKL	[24]
ssFrobenius	C	Non-negative matrix factorization	–		ssFrobenius	[25]
DSA	C	Quadratic programming	–	https://github.com/zhandong/DSA (R package)	dsa	[26]
MMAD	C	Maximum likelihood over the residual sum of squares	–	http://sourceforge.net/projects/mmad/ (Matlab)		[27]

方法很多，这里先选个简单一点MCPcounter，GEO和TCGA数据都可以分析，更重要的是因为包治百病=. =

MCPcounter

加载R包

install.packages(c("devtools","curl")) ##Installs devtools and the MCPcounter dependancy 'curl'
library(devtools)
install_github("ebecht/MCPcounter",ref="master", subdir="Source")

用法

?MCPcounter.estimate
可以看到

Usage
MCPcounter.estimate(expression,featuresType=c("affy133P2_probesets","HUGO_symbols","ENTREZ_ID")[1],
        probesets=read.table(curl("http://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/probesets.txt"),sep="\t",stringsAsFactors=FALSE,colClasses="character"),
        genes=read.table(curl("http://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/genes.txt"),sep="\t",stringsAsFactors=FALSE,header=TRUE,colClasses="character",check.names=FALSE)
)

probesets和genes是作者定义的与浸润细胞相关的基因集，可以看下

probesets

genes

只需要改一下expression和featuresType就可以了

运行

immunescores

input是经过了normalization的microarray数据

结果解释

最后得到每个样本的免疫细胞得分，详读一下原文
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression