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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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A Physics-informed Neural Sde Network For Learning Cellular Dynamics From Time-series Scrna-seq Data.

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A Physics-informed Neural Sde Network For Learning Cellular Dynamics From Time-series Scrna-seq Data.

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A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

Qi Jiang^1,2, Lin Wan^1,2

¹Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Bioinformatics (Oxford, England)

|September 4, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

We introduce PI-SDE, a physics-informed framework for reconstructing cellular potential energy landscapes from single-cell RNA sequencing data. This method enhances prediction accuracy and biological interpretability of cell differentiation dynamics.

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Area of Science:

Computational Biology
Systems Biology
Single-cell Genomics

Background:

Reconstructing cellular potential energy landscapes (Waddington landscapes) from time-series single-cell RNA sequencing (scRNA-seq) data is crucial for understanding cell dynamics.
Current data-driven methods often lack physical principles, limiting prediction accuracy and interpretability.

Purpose of the Study:

To develop a physics-informed framework, PI-SDE, for accurate and interpretable reconstruction of cellular dynamics.
To integrate physical principles, specifically the Hamilton-Jacobi equation, into neural stochastic differential equations for learning from scRNA-seq data.

Main Methods:

Proposed PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework.
Combined Hamilton-Jacobi (HJ) equation with neural SDE, enforcing the HJ equation to reconstruct the cellular potential energy function based on potential energy theory.

Integrated the principle of least action into the learning process.

Main Results:

PI-SDE demonstrated improved prediction accuracy for gene expression at held-out time points on real scRNA-seq datasets.
The reconstructed potential energy landscape provided biologically interpretable insights into cell differentiation.
The framework showed enhanced model performance, robustness, and stability.

Conclusions:

Incorporating the HJ regularization term is vital for accurate dynamic inference and prediction in scRNA-seq data analysis.
PI-SDE offers a robust and interpretable approach to modeling cell differentiation dynamics.
The PI-SDE software is publicly available for broader research application.