Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.复杂生物系统可被表述为海量细胞间相互作用（cell-cell interactions, CCIs）的集合。近年来的单细胞RNA测序（single-cell RNA-sequencing）研究多聚焦于基于配体-受体（ligand-receptor, L-R）基因共表达的细胞间相互作用分析。然而，当前相关分析方法仍未成熟：这类方法要么无法同时检测细胞间相互作用及其对应的配体-受体对，要么难以适用于多对多模式的细胞间相互作用检测任务。本研究提出了一种名为scTensor的新型分析方法，可用于提取涵盖配体表达、受体表达及相关配体-受体对的代表性三元关系（亦称超图）。通过针对模拟数据集与实证数据集开展的大量验证实验，我们证实scTensor能够检测到传统方法无法识别的部分超图结构，在处理多对多模式的细胞间相互作用时优势尤为显著。