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Synaptic Microcircuit Modeling with 3D Cocultures of Astrocytes and Neurons from Human Pluripotent Stem Cells

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scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Wenying Li¹, Haiyun Wang¹, Jianping Zhao¹

¹School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China.

Briefings in Bioinformatics

|September 14, 2024

Summary

This summary is machine-generated.

scHyper infers cell-cell communications using a novel hypergraph model. This method provides a global network view, improving the analysis of complex intercellular communication networks from single-cell RNA sequencing data.

Keywords:

deep learning global network hypergraph neural networks intercellular communications single-cell RNA-seq

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Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Cell-cell communication is fundamental for multicellular life and tissue organization.
Existing methods for inferring cell-cell communication from single-cell RNA sequencing (scRNA-seq) data often lack a comprehensive global network perspective.
Multilayered and complex intercellular interactions require advanced analytical approaches.

Purpose of the Study:

To develop a novel computational method, scHyper, for inferring intercellular communications with a global network view.
To represent and analyze complex cell-cell communication pathways using a heterogeneous hypergraph structure.
To identify the impact of ligand-receptor interactions and cell-specific expression on communication scores.

Main Methods:

scHyper utilizes a heterogeneous hypergraph to model tripartite relationships between ligand expression, receptor expression, and ligand-receptor pairs.
Employs hypergraph representation learning to quantify communication probabilities based on node attributes and hyperedge formation.
Incorporates a rich dataset of multisubunit complex ligand-receptor pairs and a nonparametric test for identifying significant intercellular communications.

Main Results:

scHyper demonstrates superior performance and functionality compared to existing tools for cell-cell communication inference.
Experimental validation on human tumor microenvironment and immune cells showcases scHyper's reliability and unique analytical capabilities.
The method effectively captures high-order interaction patterns, overcoming limitations of low-order interaction models.

Conclusions:

scHyper provides an effective strategy for building high-order interaction patterns in intercellular communication networks.
The approach offers a more accurate interpretation of the complexity of cell-cell communications.
This method enhances the analysis of biological systems by providing a comprehensive global network view of cellular interactions.