SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics
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 predict...
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| Veröffentlicht in: | Nucleic acids research 2020-06, Vol.48 (10), p.e55-e55 |
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| container_title | Nucleic acids research |
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| creator | Cabello-Aguilar, Simon Alame, Mélissa Kon-Sun-Tack, Fabien Fau, Caroline Lacroix, Matthieu Colinge, Jacques |
| description | 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. |
| doi_str_mv | 10.1093/nar/gkaa183 |
| format | Article |
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| subjects | Animals Epidermis Epidermis - metabolism Gene Expression Profiling Gene Expression Profiling - methods Life Sciences Ligands Methods Online Mice Signal Transduction Single-Cell Analysis Single-Cell Analysis - methods Software Workflow |
| title | SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics |
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