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

Hratch M Baghdassarian; Daniel Dimitrov; Erick Armingol; Julio Saez-Rodriguez; Nathan E Lewis

doi:10.1016/j.crmeth.2024.100758

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.

Authors

Hratch M Baghdassarian¹, Daniel Dimitrov², Erick Armingol¹, Julio Saez-Rodriguez³, Nathan E Lewis⁴

Affiliations

¹ Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
² Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
³ Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany. Electronic address: pub.saez@uni-heidelberg.de.
⁴ Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA. Electronic address: nlewisres@ucsd.edu.

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.

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*