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Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data.

Koki Tsuyuzaki1,2, Manabu Ishii3, Itoshi Nikaido4,5

  • 1Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan. koki.tsuyuzaki@gmail.com.

BMC Bioinformatics
|November 7, 2023
PubMed
Summary

scTensor is a new method that identifies complex cell-cell interactions (CCIs) by analyzing ligand-receptor gene co-expression. It effectively detects many-to-many relationships missed by traditional methods.

Keywords:
Cell–cell interactionDimension reductionHypergraphNon-negative Tucker2 decompositionR/BioconductorSingle-cell RNA-sequencingTensor decomposition

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Complex biological systems involve numerous cell-cell interactions (CCIs).
  • Current single-cell RNA-sequencing methods for CCIs, based on ligand-receptor (L-R) gene co-expression, struggle with many-to-many relationships.

Purpose of the Study:

  • To introduce scTensor, a novel computational method for extracting complex CCI patterns.
  • To address the limitations of existing methods in detecting many-to-many L-R interactions.

Main Methods:

  • scTensor extracts representative triadic relationships, also known as hypergraphs.
  • These hypergraphs integrate ligand expression, receptor expression, and associated L-R pairs.

Main Results:

  • scTensor successfully identifies hypergraphs undetectable by conventional CCI methods.
  • The method demonstrates superior performance in detecting many-to-many cell-cell communication patterns.

Conclusions:

  • scTensor offers a powerful new approach for analyzing complex CCIs, particularly many-to-many interactions.
  • The scTensor method is available as an open-source R/Bioconductor package for broader scientific use.