SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

By S. Cabello-Aguilar, Fabien Kon Sun Tack, Mélissa Alame, Caroline Fau, M. Lacroix, Jacques Colinge. bioRxiv, 2019

349 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1e1e7ae89f9bee32acf7ba19b033a5d5718a0eee

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In Molecular Biology

1. Maksim V. Plikus
2. Suoqin Jin
3. Olivier Harismendy
4. Nathan E. Lewis
5. Adam Officer
6. Erick Armingol
7. Qing Nie

Citations in the corpus

• Deciphering cell–cell interactions and communication from gene expression (2020, methodology): “SingleCellSignalR 112 uses a regularized expression product to compute ligand–receptor interaction scores and is the only tool reviewed that provides explicit cut-off values for this score to achieve appropriate false discovery rates based on empirical results.”
• stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, methodology): “Existing methods to model cell-cell interactions are based on testing for enrichment in ligand-receptor (L-R) expression from bulk RNAseq or scRNAseq data 28,29 .”
• Best practices for single-cell analysis across modalities (2023, neutral): “3).”
• Inference and analysis of cell-cell communication using CellChat (2020, methodology): “We also quantitatively showed the differences and the strengths of CellChatDB in comparison to other existing analogous databases, including CellTalkDB 71 , CellPhoneDB 16 , iTALK 10 , SingleCellSignalR 9 , Ramilowski2015 72 , NicheNet 13 , and ICELLNET 73 .”
• Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “Many analytical tools have been developed for this task, such as SpaOTsc 21 , Giotto 71 , CellChat 72 , NicheNet 73 , ICELLNET 74 , and SingleCellSignalR 75 .”