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scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.

Chenchen Jia¹, Haiyun Wang², Jianping Zhao¹

¹College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.

Plos Computational Biology

|May 7, 2025

Summary

This summary is machine-generated.

We developed scSDNE, a deep learning model to infer cell-cell communication via ligand-receptor interactions. It accurately identifies communication pathways by integrating gene regulation, enhancing biological insights.

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Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System

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Published on: May 22, 2020

Area of Science:

Cellular Biology
Bioinformatics
Genomics

Background:

Cell-cell communication is crucial for multicellular organisms, mediated by ligand-receptor (L-R) interactions.
Understanding these interactions is key to deciphering biological processes and cellular functions.
Existing methods require comprehensive understanding of these complex interactions.

Purpose of the Study:

To develop a novel computational model for inferring cell-cell interactions mediated by L-R pairs.
To enhance the accuracy and biological interpretability of intercellular communication analysis.
To provide a more comprehensive view of cell-cell interactions in biological systems.

Main Methods:

Constructed a manually curated ligand-receptor interaction database.
Developed a semi-supervised graph embedding deep learning model (scSDNE).
Mapped interacting cell genes into a shared latent space and integrated gene regulation information.

Main Results:

scSDNE reliably infers significant L-R pairs involved in intercellular communication.
The model demonstrates high reliability in detecting cell-cell interactions on scRNA-seq data.
Integration of gene regulation information improved inference accuracy and interpretability.

Conclusions:

scSDNE offers a powerful tool for analyzing cell-cell communication through L-R interactions.
The model provides novel insights into complex intercellular communication mechanisms.
This approach enhances the understanding of biological processes and cellular functions in multicellular organisms.