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Related Experiment Video

Updated: May 5, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

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Published on: April 9, 2019

Modeling single cell trajectory using forward-backward stochastic differential equations.

Kevin Zhang¹, Junhao Zhu¹, Dehan Kong¹

¹Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.

Plos Computational Biology

|April 15, 2024

Summary

This summary is machine-generated.

This study introduces a novel Forward-Backward Stochastic Differential Equation (FBSDE) model to accurately infer complex, nonlinear developmental trajectories from single-cell RNA sequencing (scRNA-seq) data, outperforming existing methods.

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Area of Science:

Computational Biology
Mathematical Modeling
Genomics

Background:

Single-cell sequencing enables mathematical modeling of dynamic developmental processes.
Optimal transport methods are limited in capturing nonlinear single-cell trajectories.
Existing stochastic differential equation (SDE) methods can yield inaccurate inferences due to numerical approximations.

Purpose of the Study:

To develop a more accurate method for inferring nonlinear developmental trajectories from single-cell data.
To address the limitations of current optimal transport and SDE-based approaches.

Main Methods:

Proposed a novel approach combining forward-backward stochastic differential equations (FBSDE).
Integrated forward and backward SDE movements to capture underlying single-cell dynamics.
Employed a refined approximation procedure for enhanced accuracy.

Main Results:

Demonstrated superior performance of the FBSDE model compared to existing methods.
Successfully captured nonlinear developmental trajectories in multiple scRNA-seq datasets.
Highlighted the efficacy of FBSDE in accurately inferring true cellular trajectories.

Conclusions:

The proposed FBSDE approach offers a significant advancement in modeling single-cell developmental dynamics.
FBSDE provides a robust and accurate framework for inferring complex trajectories from scRNA-seq data.
This method enhances our ability to understand dynamic biological processes at the single-cell level.