PT  - JOURNAL ARTICLE
AU  - Tsuyuzaki, Koki
AU  - Ishii, Manabu
AU  - Nikaido, Itoshi
TI  - Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data
AID  - 10.1101/566182
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 566182
4099  - http://biorxiv.org/content/early/2019/03/04/566182.short
4100  - http://biorxiv.org/content/early/2019/03/04/566182.full
AB  - 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.