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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication

Yongjian Yang1, Guanxun Li2, Yan Zhong3

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Cell Systems
|February 14, 2023
PubMed
Summary

scTenifoldXct is a new computational tool that maps cell-cell communication by identifying ligand-receptor interactions. It reveals subtle yet important communication pathways missed by other methods.

Keywords:
cell-cell interactioncellular communicationgene regression networkmachine learningmanifold alignmentneural networksscRNA-seqsingle-cell RNA sequencing

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Cell-cell communication is crucial for understanding biological processes.
  • Existing methods for analyzing ligand-receptor interactions have limitations in detecting subtle signals.

Purpose of the Study:

  • To develop a novel computational tool, scTenifoldXct, for detecting ligand-receptor (LR)-mediated cell-cell interactions.
  • To map cellular communication graphs and identify differential interactions between biological samples.

Main Methods:

  • scTenifoldXct employs a semi-supervised approach based on manifold alignment.
  • It uses ligand-receptor pairs as correspondences to embed interacting cell gene expression into a unified latent space.
  • Neural networks are utilized to minimize gene distances and preserve gene regression network structures.

Main Results:

  • scTenifoldXct demonstrates high consistency with existing methods in detecting cell-cell interactions.
  • The tool successfully identifies weak but biologically relevant interactions often missed by other approaches.
  • Application to real datasets shows scTenifoldXct's capability in comparing samples (e.g., healthy vs. diseased) to find differential interactions.

Conclusions:

  • scTenifoldXct provides a robust and sensitive method for analyzing cellular communication networks.
  • The tool facilitates the discovery of novel ligand-receptor interactions and their functional implications.
  • scTenifoldXct aids in comparing cellular communication states across different conditions, advancing biological insights.