Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

Cell Rep Methods. 2024 Apr 22;4(4):100758. doi: 10.1016/j.crmeth.2024.100758. Epub 2024 Apr 16.


In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Keywords: CP: Cell biology; CP: Systems biology; cell-cell communication; context dependent; ligand-receptor interactions; multiple conditions; single-cell RNA sequencing; tensor decomposition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Communication* / physiology
  • Computational Biology / methods
  • Humans
  • Single-Cell Analysis / methods
  • Software*