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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
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.
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.
