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Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

View ORCID ProfileKoki Tsuyuzaki, Manabu Ishii, View ORCID ProfileItoshi Nikaido
doi: https://doi.org/10.1101/566182
Koki Tsuyuzaki
1Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, 351-0198, Saitama, Japan
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Manabu Ishii
1Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, 351-0198, Saitama, Japan
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Itoshi Nikaido
1Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, 351-0198, Saitama, Japan
2Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako, 351-0198, Saitama, Japan
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  • For correspondence: itoshi.nikaido@riken.jp
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Abstract

Complex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we propose scTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) ligand-expression, (ii) receptor-expression, and (iii) L-R pairs. When applied to simulated and empirical datasets, scTensor was able to detect some hypergraphs including paracrine/autocrine CCI patterns, which cannot be detected by previous methods.

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Posted March 04, 2019.
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Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data
Koki Tsuyuzaki, Manabu Ishii, Itoshi Nikaido
bioRxiv 566182; doi: https://doi.org/10.1101/566182
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Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data
Koki Tsuyuzaki, Manabu Ishii, Itoshi Nikaido
bioRxiv 566182; doi: https://doi.org/10.1101/566182

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