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Updated: Jul 2, 2025

A Gradient-generating Microfluidic Device for Cell Biology
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Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable

Lihong Peng1, Pengfei Gao1, Wei Xiong1

  • 1College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.

Computers in Biology and Medicine
|February 17, 2024
PubMed
Summary

CellGiQ is a new framework that identifies high-confidence ligand-receptor interactions (LRIs) for analyzing cell-cell communication. It uses machine learning to predict LRIs and integrates single-cell RNA sequencing data for robust intercellular communication analysis.

Keywords:
BoostingEnsemble learningIntercellular communicationLigand–receptor interaction

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Cell-cell communication is crucial for biological processes, primarily mediated by ligand-receptor interactions (LRIs).
  • Accurate LRI prediction is vital for understanding intercellular communication, but lacks a "gold standard" dataset for evaluation.
  • Existing methods struggle with high-confidence LRI identification and robust validation.

Purpose of the Study:

  • To introduce CellGiQ, a novel framework for high-confidence ligand-receptor interaction (LRI) prediction.
  • To enhance intercellular communication analysis by integrating predicted LRIs with single-cell RNA sequencing (scRNA-seq) data.
  • To provide a validated computational tool for dissecting LRI-mediated cell-cell communication at single-cell resolution.

Main Methods:

  • CellGiQ employs LRI feature extraction with BioTriangle, LRI selection using LightGBM, and classification via an ensemble of gradient boosted neural networks and interpretable boosting machines.
  • High-confidence LRIs are filtered using scRNA-seq data and applied to intercellular communication inference using a quartile scoring strategy.
  • Validation involved AUC/AUPR metrics, Venn diagrams, molecular docking, Jaccard index comparisons, and literature retrieval.

Main Results:

  • CellGiQ outperformed six competing LRI prediction models on four datasets based on AUC and AUPR.
  • Predicted LRIs were validated by five other intercellular communication inference methods and showed high Jaccard indices with state-of-the-art tools.
  • Inferred HNSCC-related intercellular communication results were validated against classical models and literature.

Conclusions:

  • CellGiQ provides a novel, machine learning-based approach for identifying high-confidence LRIs, addressing a critical need in computational LRI prediction.
  • The framework offers robust validation strategies, including molecular docking and comparison with existing methods.
  • CellGiQ is an open-source tool that facilitates LRI-mediated intercellular communication analysis at single-cell resolution.