Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium (TM) platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

Automated summary

The study of cellular networks mediated by ligand-receptor interactions has been a hot topic recently due to single-cell omics. However, there is a lack of comparable bulk data and clinical information in single-cell studies. Spatial transcriptomic analyses offer a revolutionary tool in biology, particularly those relying on multicellular resolution. In this study, we introduce BulkSignalR, an R package that infers ligand-receptor networks from bulk data, integrates downstream pathways, and provides statistical significance estimation and visualization methods, including those specific to spatial data. Our results demonstrate the relevance and superior quality of BulkSignalR inferences compared to other spatial transcriptomic packages.