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DD-CC-II: Data Driven Cell-Cell Interaction Inference and Its Application to COVID-19.

Heewon Park1,2,3, Satoru Miyano2,3

  • 1School of Mathematics Statistics and Data Science, Sungshin Women's University, Seoul 01133, Republic of Korea.

International Journal of Molecular Sciences
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

A new computational framework, data-driven cell-cell interaction inference (DD-CC-II), accurately models cell-cell interactions. This approach reveals disease-specific communication patterns, improving our understanding of tissue homeostasis and disease progression.

Keywords:
COVID-19 severity stagecell–cell interactionsdisease progression of COVID-19eigen-cellover-representation analysis

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Cell-cell interactions are crucial for tissue homeostasis and disease.
  • Existing ligand-receptor databases struggle with context-specific signaling.

Purpose of the Study:

  • To introduce a novel data-driven computational framework (DD-CC-II) for inferring cell-cell interactions (CCIs).
  • To overcome limitations of traditional methods in capturing dynamic and context-specific signaling.

Main Methods:

  • Developed a graph-based model using 'eigen-cells' to represent cell groups.
  • Constructed correlation coefficient networks to model associations between eigen-cells.
  • Validated statistical significance using over-representation analysis and hypergeometric testing.

Main Results:

  • DD-CC-II demonstrated superior performance in inferring CCIs compared to ligand-receptor-based methods via Monte Carlo simulations.
  • Analysis of COVID-19 data identified severity-specific interaction patterns.
  • Specific markers (FOS, CXCL8, HLA-A for high severity; IL1B, CD3D, CCL5 for low severity) and the systemic lupus erythematosus pathway were linked to disease severity.

Conclusions:

  • DD-CC-II offers a data-centric approach for mapping cellular communication.
  • This framework enhances understanding of disease progression at the intercellular level.