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Related Experiment Video

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Learning collective multicellular dynamics with an interacting mean field neural SDE model.

Qi Jiang1,2, Longquan Li1,3, Lei Zhang4

  • 1State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

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We developed scIMF, a deep learning model for analyzing temporal single-cell RNA sequencing data. It effectively models cell-cell interactions to reconstruct complex multicellular dynamics and uncover non-reciprocal cellular communication patterns.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Temporal single-cell RNA sequencing (scRNA-seq) enables studying dynamic biological processes.
  • Integrating cell-cell interactions (CCIs) into computational models of cellular dynamics is challenging due to data complexity.

Purpose of the Study:

  • To present scIMF, a novel deep-generative model for learning collective multicellular dynamics from scRNA-seq data.
  • To address the challenge of integrating complex CCIs into models of cellular systems.

Main Methods:

  • scIMF utilizes a deep-generative Interacting Mean Field model based on the McKean-Vlasov stochastic differential equation framework.
  • Incorporates a cell-wise attention mechanism to capture nonlocal and asymmetric CCIs in high-dimensional gene expression data.

Main Results:

  • scIMF accurately reconstructs gene expression at unobserved time points and infers cellular velocities across diverse scRNA-seq datasets.
  • The model outperforms existing state-of-the-art methods in temporal data analysis.
  • scIMF reveals biologically interpretable, non-reciprocal cell interaction patterns.

Conclusions:

  • scIMF provides a robust framework for modeling collective multicellular dynamics and complex intercellular relationships.
  • The model offers new insights into non-equilibrium biological systems by uncovering non-reciprocal cell communication.