生信技巧GSEA/GSVA/GO/KEGG

如何用转录组数据定量肿瘤浸润免疫细胞

2019-08-29  本文已影响0人  王子狐

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)
)

probesetsgenes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expressionfeaturesType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

上一篇下一篇

猜你喜欢

热点阅读