EMTscore

An integrated R package for comprehensive quantification and analysis of Epithelial-Mesenchymal Transition (EMT) scores from single-cell and bulk omics data.

EMTscore Framework

Overview

Epithelial-Mesenchymal Transition (EMT) is a key cellular plasticity process involved in development, cancer progression, fibrosis, and more. Partial EMT states are increasingly recognized as critical in many biological contexts.

EMTscore provides a unified, flexible toolbox that:

Integrates multiple state-of-the-art EMT scoring methods
Allows users to choose from a curated collection of EMT gene sets
Supports both single-cell and bulk RNA-seq data
Offers advanced visualization and downstream analysis tools

Key Features

Multiple EMT scoring methods nnPCA, AUCell, SCSE, ssGSEA, Seurat AddModuleScore, JASMINE
Extensive curated EMT gene sets Classic EMT signatures, cancer-specific, fibrosis-related, and more
Unique nnPCA-based divergent EMT scoring Captures both classical and alternative EMT programs from a single dataset
Comprehensive downstream analyses Sample-level E/M score visualization, correlation with senescence/stemness scores, screening for correlated gene sets, and cell clustering / E/M state labeling
Publication-quality plots Scatter plots, heatmaps, clustering, Sankey diagrams, correlation plots

Installation

The package is developed against R 4.6 (development) and Bioconductor 3.23 (development).

# Install from Bioconductor
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("EMTscore")

# Or install the development version from GitHub
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("wenmm/EMTscore")

EMTscoreData provides curated EMT gene sets and reference data used by EMTscore:

BiocManager::install("EMTscoreData")

Quick Start

library(EMTscoreData)
library(EMTscore)

eh <- ExperimentHub::ExperimentHub()
A549_EGF   <- eh[["EH10292"]]
A549_TGFB1 <- eh[["EH10293"]]
gmt <- system.file(
    "extdata",
    "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.v2025.1.Hs.gmt",
    package = "EMTscore")

objects <- list(A549_TGFB1 = A549_TGFB1, A549_EGF = A549_EGF)

seurat_objs <- add_EMT_score(objects, gmt_file = gmt,
    emt_name = "EMT_score", method = "nnPCA", nnPCA_dim = 1)
plot_EMT_from_objects(seurat_objs,
    col_name = "Pseudotime", emt_score_col = "EMT_score")

Documentation

Full documentation and a vignette are available in the package:

vignette("EMTscore")

Citation

Wen H, Bleris L, Hong T (2026). EMTscore infers divergent EMT pathways from omics data and enables rapid screening for EMT-associated gene sets. Bioinformatics, 42(5), btag286. https://doi.org/10.1093/bioinformatics/btag286

Feedback & Contributions

Please submit issues or pull requests on GitHub: https://github.com/wenmm/EMTscore We welcome contributions to expand the gene sets, scoring methods, and visualization options.