Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

• Verfasst von: Dimitrov, Daniel [VerfasserIn]
• Verfasst von: Türei, Dénes [VerfasserIn]
• Verfasst von: Garrido-Rodriguez, Martin [VerfasserIn]
• Verfasst von: Burmedi, Paul L. [VerfasserIn]
• Verfasst von: Nagai, James S. [VerfasserIn]
• Verfasst von: Boys, Charlotte [VerfasserIn]
• Verfasst von: Ramirez Flores, Ricardo O. [VerfasserIn]
• Verfasst von: Kim, Hyojin [VerfasserIn]
• Verfasst von: Szalai, Bence [VerfasserIn]
• Verfasst von: Costa, Ivan G. [VerfasserIn]
• Verfasst von: Valdeolivas, Alberto [VerfasserIn]
• Verfasst von: Dugourd, Aurélien [VerfasserIn]
• Verfasst von: Sáez Rodríguez, Julio [VerfasserIn]
• Titel: Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data
• Verf.angabe: Daniel Dimitrov, Dénes Türei, Martin Garrido-Rodriguez, Paul L. Burmedi, James S. Nagai, Charlotte Boys, Ricardo O. Ramirez Flores, Hyojin Kim, Bence Szalai, Ivan G. Costa, Alberto Valdeolivas, Aurélien Dugourd & Julio Saez-Rodriguez
• E-Jahr: 2022
• Jahr: 09 June 2022
• Umfang: 13 S.
• Fussnoten: Gesehen am 25.08.2022
• Titel Quelle: Enthalten in: Nature Communications
• Ort Quelle: [London] : Springer Nature, 2010
• Jahr Quelle: 2022
• Band/Heft Quelle: 13(2022), Artikel-ID 3224, Seite 1-13
• ISSN Quelle: 2041-1723
• DOI: doi:10.1038/s41467-022-30755-0
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Cancer genomics
• Sach-SW: Cellular signalling networks
• Sach-SW: Computational models
• Sach-SW: Computational platforms and environments
• K10plus-PPN: 1815170719
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.