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

Dimitrov, Daniel; Tuerei, Denes; Garrido-Rodriguez, Martin; Burmedi, Paul L.; Nagai, James S.; Boys, Charlotte; Flores, Ricardo O. Ramirez; Kim, Hyojin; Szalai, Bence [Szalai, Bence (számítógépes rend...), szerző] Élettani Intézet (SE / AOK / I); Costa, Ivan G.; Valdeolivas, Alberto; Dugourd, Aurelien; Saez-Rodriguez, Julio ✉

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Megjelent: NATURE COMMUNICATIONS 2041-1723 2041-1723 13 (1) Paper: 3224 , 13 p. 2022
  • Regionális Tudományok Bizottsága: A nemzetközi
  • SJR Scopus - Biochemistry, Genetics and Molecular Biology (miscellaneous): D1
Azonosítók
Szakterületek:
  • Élettan és orvostechnológia
Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches. 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.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2024-07-14 18:18