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
