# 胃癌TMEscore（源于曾东强博士）

**Repository Path**: focuslyj/TMEscore

## Basic Information

- **Project Name**: 胃癌TMEscore（源于曾东强博士）
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-06
- **Last Updated**: 2023-12-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<!-- README.md is generated from README.Rmd. Please edit that file -->

# TMEscore

### 1.Introduction

TME infiltration patterns were determined and systematically correlated
with TME cell phenotypes, genomic traits, and patient
clinicopathological features to establish the
[TMEscore](https://cancerimmunolres.aacrjournals.org/content/7/5/737):
*Tumor Microenvironment Characterization in Gastric Cancer Identifies
Prognostic and Immunotherapeutically Relevant Gene Signatures*.

![TMEscore logo](./man/TMEscore-honors.png)

TMEscore is an R package to estimate tumor microenvironment score.
Provides functionality to calculate Tumor microenvironment (TME) score
using PCA or z-score.

### 2.Installation

The package is not yet on CRAN. You can install from Github:

``` r
# install.packages("devtools")
if (!requireNamespace("TMEscore", quietly = TRUE))
  devtools::install_github("DongqiangZeng0808/TMEscore")
```

### 3.Usage

Main documentation is on the `tmescore` function in the package:

``` r
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

``` r
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
```

``` r
#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))
```

<img src="man/figuresunnamed-chunk-5-1.png" width="100%" />

### Citation

If you use TMEscore in published research, please cite:

1.  [Tumor microenvironment evaluation promotes precise checkpoint
    immunotherapy of advanced gastric
    cancer](https://jitc.bmj.com/content/9/8/e002467). *Journal for
    ImmunoTherapy of Cancer*, 2021, 9(8), e002467. DOI:
    10.1136/jitc-2021-002467, PMID: 34376552

2.  [Tumor microenvironment characterization in gastric cancer
    identifies prognostic and imunotherapeutically relevant gene
    signatures](https://cancerimmunolres.aacrjournals.org/content/7/5/737).
    *Cancer Immunology Research*, 2019, 7(5), 737-750. DOI:
    10.1158/2326-6066.CIR-18-0436, PMID: 30842092

### Contact

E-mail any questions to <dongqiangzeng0808@gmail.com> or
<interlaken0808@163.com>