IEEE Journal of Biomedical and Health Informatics · 2023 · 40 citations · 65 references
Life SciencesBioinformaticsCell BiologyCell SignalingCell-cell CommunicationSignal TransductionCancer Cell SignalingCancer Cell-cell CommunicationCancer TranscriptomicsCell-cell Communication AnalysisLigand-receptor InteractionsSignal Transduction In CancerCell - Cell Communication ModelingCell-cell Communication InferenceCell-cell Communication VisualizationIntercellular Communication NetworksLigand-receptor Interaction AnalysisNetwork-based Communication ReconstructionSingle-cell Rna SequencingSingle-cell Transcriptomic Communication Analysis
Intercellular communication significantly influences tumor progression, metastasis, and therapy resistance. An intercellular communication inference method includes two main procedures: ligand-receptor interaction (LRI) curation and LRI-mediated intercellular communication strength measurement. The construction of a comprehensive, high-confident and well-organized LRI database contributes to intercellular communication inference. Here, we developed a computational framework named CellDialog to reconstruct an intercellular connectivity network based on the combined expression of ligands and receptors involved in sender and receiver cells. CellDialog first captures high-confident LRIs through LRI feature extraction, feature selection, and classification. Furthermore, CellDialog uses a three-point estimation approach to measure the LRI-mediated intercellular communication strength by combining LRI filtering and single-cell RNA sequencing data. A comparison analysis of CellDialog and the other tools was conducted, and it was found that CellDialog can efficiently decode intercellular communications. Additionally, CellDialog offers a heatmap view and network view for intercellular communication visualization. In summary, CellDialog provides a tool that allows researchers to analyze intercellular signal transduction. It is freely available at https://github.com/plhhnu/CellDialog.
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