ArticleGenome research2023
ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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57 citing papers in PubMed.
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
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- Identification and experimental validation of biomarkers associated with exercise in intervertebral disc degeneration through bulk RNA and single-cell RNA sequencing analysis.Scientific reports · 2026Article
- Integrative multi-omics analysis identifies key ubiquitination regulators in prostate cancer.Translational oncology · 2026Article
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- Integrated Single-Cell and Bulk RNA Sequencing Identifies Macrophage Heterogeneity and Mitophagy-Related Biomarkers in Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2026Article
- scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.Genome biology · 2026Article
- Transcriptome combined with single-cell data to construct a prognostic model for glycosylation-related genes in osteosarcoma.Discover oncology · 2026Article
- Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.Bioinformatics (Oxford, England) · 2026Article
- Calibrating tissue level PDE models of ligand dynamics using single cell and spatial transcriptomics data.NPJ systems biology and applications · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Advancing spatial cellular communication inference with ligand diffusion and transport model.Communications biology · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Identification of succinylation-related genes in bladder cancer: integration of single-cell and transcriptomic data.Frontiers in immunology · 2026Article
- Integrating bulk RNA-seq, Mendelian randomization and single-cell RNA-seq to elucidate the roles of lactate metabolism related markers-SLC25A4 and keratinocyte in the pathogenesis of psoriasis.Frontiers in immunology · 2026Article
- Analysis of immune and autophagy-related genes and their regulatory mechanisms in osteoporosis patients post-menopause.Frontiers in endocrinology · 2026Article
- Development and Validation of a Six-Gene Signature of Myeloid Antigen Presentation Dysfunction Based on Single-Cell and Multi-Cohort Transcriptomics for Predicting Prognosis and Recurrence of Hepatocellular Carcinoma.Cancer informatics · 2026Article
- Identification of potential vinorelbine-associated prognostic genes in breast cancer through integrative bioinformatics and experimental validation.Frontiers in oncology · 2026Article
- Identification of Integrated Stress Response-Related Biomarkers in Sepsis-Induced Acute Respiratory Distress Syndrome.Mediators of inflammation · 2026Article
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4 authors.
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Abstract
Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.
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