Abstract
Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which is the first strategy to decipher context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand receptor pairs) linked to COVID-19 severities and Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
- Moved computational efficiency benchmarking to Supplemental Information; - Added new analyses using results from other CCC tools as input to build the tensor (Fig 3a). - Added new analyses evaluating the impact of gene expression preprocessing and batch correction over the tensor decomposition (Fig 3b). - Added downstream analyses to interpret results from Tensor-cell2cell (Methods, Fig 5b-d, Supplemental Fig S9). - Added a new study case associated with Autism Spectrum Disorders (Fig 5). - Added an analysis of the COVID-19 study by using CellChat (Supplementary Notes).
