%0 Journal Article
%T Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
%A Baghdassarian HM
%A Dimitrov D
%A Armingol E
%A Saez-Rodriguez J
%A Lewis NE
%J Cell Rep Methods
%V 4
%N 4
%D 2024 Apr 22
%M 38631346
暂无%R 10.1016/j.crmeth.2024.100758
%X 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.