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

Title:
Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
Authors:
Baghdassarian HM; 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., Dimitrov D; Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany., Armingol E; 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., Saez-Rodriguez J; Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany. Electronic address: pub.saez@uni-heidelberg.de., Lewis NE; 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.
Source:
Cell reports methods [Cell Rep Methods] 2024 Apr 22; Vol. 4 (4), pp. 100758. Date of Electronic Publication: 2024 Apr 16.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918227360606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2667-2375 (Electronic) Linking ISSN: 26672375 NLM ISO Abbreviation: Cell Rep Methods Subsets: MEDLINE
Imprint Name(s):
Original Publication: [New York] : Elsevier Inc., [2021]-
MeSH Terms:
Cell Communication*/physiology , Software*, Humans ; Computational Biology/methods ; Single-Cell Analysis/methods
Comments:
Update of: bioRxiv. 2023 Apr 30:2023.04.28.538731. doi: 10.1101/2023.04.28.538731.. (PMID: 37162916)
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Grant Information:
R35 GM119850 United States GM NIGMS NIH HHS
Contributed Indexing:
Keywords: CP: Cell biology; CP: Systems biology; cell-cell communication; context dependent; ligand-receptor interactions; multiple conditions; single-cell RNA sequencing; tensor decomposition
Entry Date(s):
Date Created: 20240417 Date Completed: 20240424 Latest Revision: 20250730
Update Code:
20250731
PubMed Central ID:
PMC11046036
DOI:
10.1016/j.crmeth.2024.100758
PMID:
38631346
Database:
MEDLINE

Weitere Informationen

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.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.