SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics

Abstract

ABSTRACT Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor interactions underlying cellular networks. We introduce a new, curated ligand-receptor database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted ligand-receptor 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 with 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.

Keywords

InferenceComputer scienceFunction (biology)False positive paradoxComputational biologyVariety (cybernetics)IntracellularBiologyArtificial intelligenceCell biology

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Publication Info

Year: 2019
Type: preprint
Citations: 43
Access: Closed

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APA Style

                            
                                    Simon Cabello‐Aguilar, 
                                
                                    Fabien Kon Sun Tack, 
                                
                                    Mélissa Alame
                                
                                et al.
                            
                            (2019). 
                            SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics. 
                            bioRxiv (Cold Spring Harbor Laboratory)
                            
                            .
                            https://doi.org/10.1101/2019.12.11.872895

Identifiers

DOI: 10.1101/2019.12.11.872895