Deciphering the molecular bases underlying cell-cell interactions

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No cell is an island. All cells interact with others to build higher-order phenotypes. To enable the study of complex cell-cell interactions from omics data, we have developed the cell2cell package. This includes two main sub-tools:

  • The regular cell2cell, which includes the main approaches described here (Armingol*, Officer*, et al. Nat Rev Genetics, 2021).
  • Tensor-cell2cell, a novel method that uses tensor factorization to deconvolve patterns of cell-cell communication across multiple cellular contexts (e.g., time points, disease states, cellular location, patients, etc.) (see Armingol*, Baghdassarian*, et al., Nature Communications, 2022).

Project link:

cell2cell is a user-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins. It allows the use of both bulk and single-cell data, and uses any list of ligand-receptor interactions and a selection of computational methods for analyzing the intercellular interactions. Cell2cell also includes multiple visualization options that facilitate the interpretation of results.
Example of application in C. elegans:

Tensor-cell2cell is an unsupervised method using tensor decomposition, which allows one 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. This dimensionality reduction approach is able to use scores from any other tool for inferring cell-cell interactions and communication, and detect context-driven patterns from these scores.
It is implemented as part of our cell2cell suite, along with Tutorials and Documentation: