TME infiltration patterns were determined and systematically correlated with TME cell phenotypes, genomic traits, and patient clinicopathological features to establish the TMEscore: Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.
TMEscore is an R package to estimate tumor microenvironment score. Provides functionality to calculate Tumor microenvironment (TME) score using PCA or z-score.
The package is not yet on CRAN. You can install from Github:
# install.packages("devtools")
if (!requireNamespace("TMEscore", quietly = TRUE))
devtools::install_github("DongqiangZeng0808/TMEscore")Main documentation is on the tmescore function in the package:
library('TMEscore')
#> 载入需要的程辑包：survival
#> Warning: 程辑包'survival'是用R版本4.2.1 来建造的
#> 载入需要的程辑包：survminer
#> 载入需要的程辑包：ggplot2
#> 载入需要的程辑包：ggpubr
#>
#> 载入程辑包：'survminer'
#> The following object is masked from 'package:survival':
#>
#> myeloma
#> TMEscore v0.1.4 For help: https://github.com/DongqiangZeng0808/TMEscore
#>
#> If you use TMEscore in published research, please cite:
#> --------------------------------
#> Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer.
#> Journal for ImmunoTherapy of Cancer, 2021, 9(8), e002467
#> DOI: 10.1136/jitc-2021-002467
#> PMID: 34376552
#> --------------------------------
#> Tumor microenvironment characterization in gastric cancer identifies prognostic and imunotherapeutically relevant gene signatures.
#> Cancer Immunology Research, 2019, 7(5), 737-750
#> DOI: 10.1158/2326-6066.CIR-18-0436
#> PMID: 30842092
#> --------------------------------
library("ggplot2")
library("patchwork")Example
tmescore<-tmescore(eset = eset_stad, #expression data
pdata = pdata_stad, #phenotype data
method = "PCA", #default
classify = T) #if true, survival data must be provided in pdata
head(tmescore)
#> ID subtype time status TMEscoreA TMEscoreB TMEscore
#> 284 TCGA-RD-A8N2 <NA> 118.00 0 -6.705998 11.66689 -18.37289
#> 95 TCGA-BR-A4IV GS 28.97 1 -6.376907 10.91756 -17.29446
#> 66 TCGA-BR-8371 GS 11.97 1 -6.258413 10.94738 -17.20580
#> 69 TCGA-BR-8380 GS NA 1 -5.213597 11.38528 -16.59887
#> 101 TCGA-BR-A4J9 GS 0.47 0 -5.463828 10.55516 -16.01899
#> 82 TCGA-BR-8592 GS 6.37 1 -5.003108 10.84967 -15.85278
#> TMEscore_binary
#> 284 Low
#> 95 Low
#> 66 Low
#> 69 Low
#> 101 Low
#> 82 Low#remove observation with missing value
tmescore<-tmescore[!is.na(tmescore$subtype),]
p<-ggplot(tmescore,aes(x= subtype,y=TMEscore,fill=subtype))+
geom_boxplot(notch = F,outlier.shape = 1,outlier.size = 0.5)+
scale_fill_manual(values= c('#374E55FF', '#DF8F44FF', '#00A1D5FF', '#B24745FF'))
comparision<-combn(unique(as.character(tmescore$subtype)), 2, simplify=F)
p1<-p+theme_light()+
stat_compare_means(comparisons = comparision,size=2.5)+
stat_compare_means(size=2.5)
# survival analysis
colnames(tmescore)[which(colnames(tmescore)=="TMEscore_binary")]<-"score"
fit<- survfit(Surv(time, status) ~ score, data = tmescore)
p2<-ggsurvplot(fit,
conf.int = FALSE,
palette = c('#374E55FF', '#DF8F44FF'),
risk.table = TRUE,
pval = TRUE,
risk.table.col = "strata")
p2<-list(p2)
p2 <- arrange_ggsurvplots(p2, print = FALSE, ncol = 1, nrow = 1)
# print plots
(p1|p2)+plot_layout(ncol = 2, widths = c(1,2))
If you use TMEscore in published research, please cite:
-
Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. Journal for ImmunoTherapy of Cancer, 2021, 9(8), e002467. DOI: 10.1136/jitc-2021-002467, PMID: 34376552
-
Tumor microenvironment characterization in gastric cancer identifies prognostic and imunotherapeutically relevant gene signatures. Cancer Immunology Research, 2019, 7(5), 737-750. DOI: 10.1158/2326-6066.CIR-18-0436, PMID: 30842092
E-mail any questions to dongqiangzeng0808@gmail.com or interlaken0808@163.com