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

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 https://ccc-protocols.readthedocs.io/. 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.
Competing Interests: Declaration of interests J.S.-R. reports funding from GSK, Pfizer, and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer, and Grunenthal. N.E.L. reports funding during the course of this work from Sanofi, Amgen, Sartorius, and Ionis and is a co-founder of NeuImmune, Inc., and Augment Biologics.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)