SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics
Simon Cabello‐Aguilar, Mélissa Alame, Fabien Kon-Sun-Tack, Caroline Fau, Matthieu Lacroix, Jacques Colinge
Abstract
Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.