Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R

Area of Science:

• Computational biology
• Cancer research
• Single-cell genomics

Background:

• The epithelial-to-mesenchymal transition (EMT) is a critical process in cancer metastasis.
• Understanding EMT dynamics is essential for cancer prognosis and therapeutic development.
• Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular transitions like EMT.

Purpose of the Study:

• To present COMET, a novel R package designed for analyzing EMT.
• To enable the inference of EMT trajectories and inter-state transition rates from scRNA-seq data.
• To provide a computational framework for dissecting EMT heterogeneity in cancer.

Main Methods:

• Development of the COMET R package.
• Utilizing scRNA-seq data to infer EMT trajectories.
• Application of continuous-time Markov chain (CTMC) models for trajectory fitting.
• Methods for determining optimal gene sets and estimating transition rates.

Main Results:

• COMET facilitates the identification of EMT-related gene expression patterns.
• The package enables robust estimation of EMT trajectory dynamics.
• Accurate inference of inter-state transition rates within EMT processes is achieved.
• COMET provides a framework for quantifying EMT progression in single cells.

Conclusions:

• COMET offers a valuable tool for researchers studying EMT and cancer metastasis.
• The package enhances the analysis of scRNA-seq data for understanding dynamic cellular processes.
• COMET contributes to advancing cancer research by providing quantitative insights into EMT.