ArticleBriefings in functional genomics2022
A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference.
Article in Briefings in functional genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- Review
- Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.Genome biology · 2026Article
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Finding spatially variable ligand-receptor interactions with functional support from downstream genes.Nature communications · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- Sugar-sensing swodkoreceptors and swodkocrine signaling.Animal models and experimental medicine · 2025Article
- HeartMAP: A multi-chamber spatial framework for cardiac cell-cell communication.Computational and structural biotechnology journal · 2025Article
- THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting.Scientific reports · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities.Current issues in molecular biology · 2024Review
- A survey of experimental and computational identification of small proteins.Briefings in bioinformatics · 2024Review
- SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.Journal of cellular and molecular medicine · 2024Article
- Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.Cell reports methods · 2024Article
- Single-Cell Informatics for Tumor Microenvironment and Immunotherapy.International journal of molecular sciences · 2024Review
- Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment.Current opinion in biotechnology · 2024Review
- The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics.Seminars in cancer biology · 2023Review
- Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.bioRxiv : the preprint server for biology · 2023Article
- SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics.Genome biology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.
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