ArticleScientific reports2025
GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- GRAIL-heart: A graph attention network for inferring ligand-receptor interactions in spatial transcriptomics.MethodsX · 2026Article
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- Deciphering immune-inflammatory dysregulation in the endometriotic microenvironment: insights from single-cell omics and artificial intelligence.Frontiers in immunology · 2026Review
- The Need for a Systems Biology Approach in Cancer Explained.International journal of molecular sciences · 2025Review
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.Genome research · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.Briefings in bioinformatics · 2023Review
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5 authors.
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Abstract
Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell-cell communication (CCC) is mediated by underlying gene-gene networks, however most current methods are unable to account for complex interactions within the cell as well as incorporate the effect of pathway and protein complexes on interactions. This results in the inability to infer overarching signalling patterns within a dataset as well as limit the ability to successfully explore other data types such as spatial cell dimension. Therefore, to represent transcriptomic data as intricate networks, complementing gene expression with information from cells to ligands and receptors for relevant CCC inference, we present GraphComm-a new graph-based deep learning method for predicting CCC in single-cell RNAseq datasets. GraphComm improves CCC inference by capturing detailed information such as cell location and intracellular signalling patterns from a database of more than 30,000 protein interaction pairs. With this framework, GraphComm is able to predict biologically relevant results in datasets previously validated for CCC, datasets that have undergone chemical or genetic perturbations and datasets with spatial cell information.
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