1. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
- Author
-
Baghdassarian, Hratch, Dimitrov, Daniel, Armingol, Erick, Saez-Rodriguez, Julio, and Lewis, Nathan
- Subjects
CP: Cell biology ,CP: Systems biology ,cell-cell communication ,context dependent ,ligand-receptor interactions ,multiple conditions ,single-cell RNA sequencing ,tensor decomposition ,Cell Communication ,Humans ,Software ,Computational Biology ,Single-Cell Analysis - Abstract
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.
- Published
- 2024