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.
