ArticleGenome biology2022
Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 1 of them a synthesis that pooled it.
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Who cites it
123 citing papers in PubMed, 1 synthesis or guideline pooled it, 149 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
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Single-cell and spatial omics in liver identify cell-cell communication regulators in aging and insulin resistance.Metabolism: clinical and experimental · 2026Article
- Identification and Experimental Validation of Key Genes Related to Neuroendocrine in Periodontitis Based on Single-Cell and Bulk Transcriptome Data.Journal of cellular and molecular medicine · 2026Article
- Spatiotemporal transcriptomics of human cardiovascular progenitors in pig hearts identifies Midkine as a positive regulator of neovascularization.Nature cardiovascular research · 2026Article
- Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.Reproduction (Cambridge, England) · 2026Review
- Addressing multiple facets of ligand-receptor network inference including single-cell proteomics.Nucleic acids research · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial transcriptomics.BMC bioinformatics · 2026Article
- A Counterfactual Framework for Directional Cell-Cell Interaction Analysis in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Review
- Single-cell insights into maladaptive endothelial plasticity and therapeutic targets in diabetic vascular complications.Cardiovascular diabetology · 2026Review
- Charting spatial ligand-target activity using Renoir.Nature communications · 2026Article
- Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.Genome biology · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Keratinocytes: pleiotropic orchestrators of cutaneous immunity and Inflammation - Emerging therapeutic paradigms in dermatological pathologies.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Review
- Review
- Predictive modeling of molecular activity underlying physical cell-cell interactions.Cell reports methods · 2026Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools.
resultsWe benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods.
conclusionsOur study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.