Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data
By Daniel Dimitrov, D. Türei, Martín Garrido-Rodríguez, Paul L. Burmedi, J. Nagai, C. Boys, R. O. Ramirez Flores, Hyojin Kim, B. Szalai, I. Costa and 3 more. Nature Communications, 2022
469 citations recorded by the source corpus.
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. 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.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/abb7218aa6062dc1fa5f83e6f9210c63270cb0fa
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Citations in the corpus
- Best practices for single-cell analysis across modalities (2023, unknown): “3).”
- Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “In particular, we envision the integration of GRNs with intracellular and intercellular communication processes 195–197 into spatially aware models 198,199 .”
- CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics (2024, unknown): “With limited benchmarking studies 10,11,18 , the question of how to better validate the inferred signaling networks and their downstream gene outputs remains to be answered.”