Modeling Cell Developmental Trajectory using Multinomial Unbalanced Optimal Transport

TL;DR

This paper introduces a novel discrete unbalanced optimal transport method to model and analyze cell type differentiation trajectories over time using single-cell RNA sequencing data.

Contribution

It presents a new approach for modeling cell type transitions that detects biological changes and infers transition probabilities, validated on mouse embryonic fibroblast data.

Findings

Accurately identified major developmental changes in cell types.

Inferred transition probabilities align with biological ground truth.

Validated method effectively captures cell differentiation trajectories.

Abstract

Single-cell trajectory analysis aims to reconstruct the biological developmental processes of cells as they evolve over time, leveraging temporal correlations in gene expression. During cellular development, gene expression patterns typically change and vary across different cell types. A significant challenge in this analysis is that RNA sequencing destroys the cell, making it impossible to track gene expression across multiple stages for the same cell. Recent advances have introduced the use of optimal transport tools to model the trajectory of individual cells. In this paper, our focus shifts to a question of greater practical importance: we examine the differentiation of cell types over time. Specifically, we propose a novel method based on discrete unbalanced optimal transport to model the developmental trajectory of cell types. Our method detects biological changes in cell types and infers their transitions to different states by analyzing the transport matrix. We validated our method using single-cell RNA sequencing data from mouse embryonic fibroblasts. The results accurately identified major developmental changes in cell types, which were corroborated by experimental evidence. Furthermore, the inferred transition probabilities between cell types are highly congruent to biological ground truth.