Fig. 1. LIANA+ framework overview.

a, LIANA+ re-implements and adapts eight ligand–receptor methods to infer interactions from single-cell data, along with a flexible consensus that can integrate any combination of these methods. b, LIANA+ implements multi-view learning as well as eight local metrics to respectively capture global and local interactions from spatially resolved omics data. c, LIANA+ includes diverse strategies to identify deregulated CCC events across conditions: (1) differential CCC analysis with PyDESeq2 (refs. ^47,48) for hypothesis-driven exploration and (2) unsupervised (hypothesis-free) approaches including standard NMF or higher-order factorizations via Tensor-cell2cell²⁰ and multi-view factor analysis⁴³. d, LIANA+ connects intercellular interactions to intracellular signalling pathways using sign-coherent network optimization. e, LIANA+ is built on a rich knowledge base—OmniPath²² and BioCypher⁵⁹—which comprise ligand–receptor interactions and annotations, including such mediated by metabolites⁶⁸, as well as intracellular knowledge, such as signalling pathways and TFs. Finally, all components (a–e) of LIANA+ are applicable to both dissociated single-cell and spatially resolved multi-omics data. ^aFor spatially weighted Spearman correlation, along with the standard metric, we implemented its masked version from scHOT³²; for Moran’s R we adapted both the global and local versions from SpatialDM⁸ and for the spatially weighted product, we also included a max-normalized version.