TMEclassifier is an R package to perform tumor microenvironment classification based on TME characteristics of gastric cancer.
- 1.TMEclassifier was designed to classify the tumor microenvironment (TME) of gastric cancer and even other cancers.
- 2.This package consists an ensemble classification model integrating 6
machine learning algorithms:
Support Vector Machine (SVM),Random Forest (RF),Neural Networks (NNET),k-Nearest Neighbor (KNN),Decision Tree (DecTree),eXtreme Gradient Boosting (XGBoost). - 3.TMEclassifier identifies three TME-clusters based on expression profiles of 134 TME-related genes and ensemble models.
- 4.In addition, TMEclassifier provides functions for multi-scale
visualization of TMEcluster and some functions depend on
IOBR, which was developed by our team previously. The research about IOBR can be reached by this link.
It is essential that you have R 3.6.3 or above already installed on your computer or server. Before installing TMEclassifier, please install all dependencies by executing the following command in R console:
The dependencies includes caret, e1071, crayon, ggplot2,
scales, tibble, IOBR, ggplot2 and ggpubr.
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager")
depens<-c("crayon", "ggplot2", "scales", "tibble", "caret", "e1071", "randomForest", "xgboost", "ggpp", "kernlab", "ComplexHeatmap", "survminer", "ggpubr")
for(iin1:length(depens)){
depen<-depens[i]
if (!requireNamespace(depen, quietly=TRUE))
BiocManager::install(depen,update=FALSE)
}
#> Registered S3 methods overwritten by 'ggpp':#> method from #> heightDetails.titleGrob ggplot2#> widthDetails.titleGrob ggplot2The package is not yet on CRAN or Bioconductor. You can install it from Github:
if (!requireNamespace("TMEclassifier", quietly=TRUE))
devtools::install_github("LiaoWJLab/TMEclassifier")Library R packages
library(TMEclassifier) data("eset_example1")
res<-tme_classifier(eset= t(eset_example1), method="ensemble", scale=T)
#> Step-1: Expression data preprocessing...#> >>> There are no missing values#> #> Step-2: TME deconvolution...#> >>> This step was skipped, user can set parameter `tme_deconvolution` to TRUE or provide TME data to realize prediction.#> #> Step-3: Predicting TME phenotypes...#> >>>-- Scaling data...#> >>>--- Ensemble Model was used to predict TME phenotypes...#> [12:37:02] WARNING: src/learner.cc:1203: #> If you are loading a serialized model (like pickle in Python, RDS in R) generated by#> older XGBoost, please export the model by calling `Booster.save_model` from that version#> first, then load it back in current version. See:#> #> https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html#> #> for more details about differences between saving model and serializing.#> #> [12:37:02] WARNING: src/learner.cc:888: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.#> [12:37:02] WARNING: src/learner.cc:553: #> If you are loading a serialized model (like pickle in Python, RDS in R) generated by#> older XGBoost, please export the model by calling `Booster.save_model` from that version#> first, then load it back in current version. See:#> #> https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html#> #> for more details about differences between saving model and serializing.#> #> >>>--- DONE!
head(res)
#> ID IE IS IA TMEcluster#> 1 GSM1523727 0.14378383 0.09860092 0.75761525 IA#> 2 GSM1523728 0.00769956 0.10048409 0.89181635 IA#> 3 GSM1523729 0.86870675 0.10316410 0.02812915 IE#> 4 GSM1523744 0.04996065 0.06216853 0.88787082 IA#> 5 GSM1523745 0.05508487 0.81613011 0.12878502 IS#> 6 GSM1523746 0.56659350 0.38249992 0.05090658 IE
table(res$TMEcluster)
#> #> IA IE IS #> 108 93 99Functions applied to visualization were depends on the IOBR R package. Users can install it from Github:
if (!requireNamespace("IOBR", quietly=TRUE))
devtools::install_github("IOBR/IOBR")
library(IOBR)Combining TMEcluster data and phenotype data.
data("pdata_example")
input<-inner_join(res, pdata_example, by="ID")
input[1:5, 1:8]
#> ID IE IS IA TMEcluster ProjectID Technology#> 1 GSM1523727 0.14378383 0.09860092 0.75761525 IA GSE62254 Affymetrix #> 2 GSM1523728 0.00769956 0.10048409 0.89181635 IA GSE62254 Affymetrix #> 3 GSM1523729 0.86870675 0.10316410 0.02812915 IE GSE62254 Affymetrix #> 4 GSM1523744 0.04996065 0.06216853 0.88787082 IA GSE62254 Affymetrix #> 5 GSM1523745 0.05508487 0.81613011 0.12878502 IS GSE62254 Affymetrix #> platform#> 1 HG-U133_Plus_2#> 2 HG-U133_Plus_2#> 3 HG-U133_Plus_2#> 4 HG-U133_Plus_2#> 5 HG-U133_Plus_2Box plot
cols<- c('#fc0d3a','#ffbe0b','#2692a4')
p1<-sig_box(data=input, signature="TMEscore", variable="TMEcluster", cols=cols, hjust=0.5)
#> # A tibble: 3 × 8#> .y. group1 group2 p p.adj p.format p.signif method #> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> #> 1 signature IA IE 3.66e-24 1.10e-23 < 2e-16 **** Wilcoxon#> 2 signature IA IS 1.18e-17 2.40e-17 < 2e-16 **** Wilcoxon#> 3 signature IE IS 9.44e- 6 9.40e- 6 9.4e-06 **** Wilcoxon#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_boxplot()`).#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_signif()`).#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_compare_means()`).p2<-sig_box(data=input, signature="T.cells.CD8", variable="TMEcluster", cols=cols, hjust=0.5)
#> # A tibble: 3 × 8#> .y. group1 group2 p p.adj p.format p.signif method #> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> #> 1 signature IA IE 1.06e- 9 2.10e- 9 1.1e-09 **** Wilcoxon#> 2 signature IA IS 7.54e-18 2.3 e-17 < 2e-16 **** Wilcoxon#> 3 signature IE IS 1.09e- 3 1.1 e- 3 0.0011 ** WilcoxonKM-plot
library(survminer)
p3<-surv_cluster(input_pdata=input,
target_group="TMEcluster",
time="OS_time",
status="OS_status",
project="ACRG",
cols= c('#fc0d3a','#ffbe0b','#2692a4'),
save_path= paste0("./man/figures"))
#> IA IE IS #> 108 93 99#> Warning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 2 rows.#> ℹ Please consider using `annotate()` or provide this layer with data containing#> a single row.#> All aesthetics have length 1, but the data has 2 rows.#> ℹ Please consider using `annotate()` or provide this layer with data containing#> a single row.#> All aesthetics have length 1, but the data has 2 rows.#> ℹ Please consider using `annotate()` or provide this layer with data containing#> a single row.#> All aesthetics have length 1, but the data has 2 rows.#> ℹ Please consider using `annotate()` or provide this layer with data containing#> a single row.Distribution of TMEcluster and molecular subtypes
# install.package("remotes") #In case you have not installed it.if (!requireNamespace("ggpie", quietly=TRUE)) remotes::install_github("showteeth/ggpie")
# if (!requireNamespace("ggtreeExtra", quietly = TRUE)) # BiocManager::install("ggtreeExtra")
library(ggpie)
p4<-ggdonut(data=input, group_key="TMEcluster", count_type="full",
label_info="ratio", label_type="circle", label_split=NULL,
label_size=5, label_pos="in", donut.label.size=4)+
scale_fill_manual(values=cols)
p5<-ggdonut(data=input, group_key="Subtype", count_type="full",
label_info="ratio", label_type="circle", label_split=NULL,
label_size=5, label_pos="in", donut.label.size=4)+
scale_fill_manual(values= palettes(palette="nrc", show_col=FALSE, show_message=FALSE))
#########################################p6<-ggnestedpie(data=input, group_key= c("TMEcluster", "Subtype"),
count_type="full",
inner_label_info="ratio",
inner_label_split=NULL,
# inner_label_threshold = 5, inner_label_size=4, outer_label_type="circle",
outer_label_size=5,
outer_label_pos="in", outer_label_info="count")+
scale_fill_manual(values=cols)Combination of plots
if (!requireNamespace("patchwork", quietly=TRUE)) install.packages("patchwork")
library(patchwork)
p<-(p1|p2|p3)/(p4|p5|p6)
p+ plot_annotation(tag_levels='A')
#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_boxplot()`).#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_signif()`).#> Warning: Removed 1 row containing non-finite outside the scale range#> (`stat_compare_means()`).
1.Zeng D, Yu Y, Qiu W, Mao Q, …, Liao W; Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer. Advanced Science, (2025) e2417593. PMID: 40433880
2.Zeng D, Fang Y, …, Liao W; Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0. Cell Reports Methods, 2024, 4(12):100910. DOI: 10.1016/j.crmeth.2024.100910, PMID: 39626665
Please report bugs to the Github issues page
E-mail any questions to interlaken@smu.edu.cn