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CGCom: a framework for inferring Cell-cell Communication based on Graph Neural Network. | LitMetric

AI Article Synopsis

  • - Cell-cell communication is essential for cellular function and is explored through a new framework called CGCom, which utilizes graph neural networks (GNN) and spatial transcriptomics to analyze how cells interact based on their proximity and molecular signaling.
  • - CGCom was tested on mouse embryo seqFISH datasets, showing an ability to accurately infer communication patterns between individual cells and predict interactions across different cell types, outperforming existing methods like CellChat and CellPhoneDB in certain aspects.
  • - The framework identified common ligand-receptor communication patterns, supporting its validity, while suggesting it reduces false positives by focusing on specific cell interactions instead of general cell types, thus enhancing the accuracy of predicting cell-cell communication.

Article Abstract

Cell-cell communication is crucial in maintaining cellular homeostasis, cell survival and various regulatory relationships among interacting cells. Thanks to recent advances of spatial transcriptomics technologies, we can now explore if and how cells' proximal information available from spatial transcriptomics datasets can be used to infer cell-cell communication. Here we present a cell-cell communication inference framework, called CGCom, which uses a graph neural network (GNN) to learn communication patterns among interacting cells by combining single-cell spatial transcriptomic datasets with publicly available ligand-receptor information and the molecular regulatory information down-stream of the ligand-receptor signaling. To evaluate the performance of CGCom, we applied it to mouse embryo seqFISH datasets. Our results demonstrate that CGCom can not only accurately infer cell communication between individual cell pairs but also generalize its learning to predict communication between different cell types. We compared the performance of CGCom with two existing methods, CellChat and CellPhoneDB, and our comparative study revealed both common and unique communication patterns from the three approaches. Commonly found communication patterns include three sets of ligand-receptor communication relationships, one between surface ectoderm cells and spinal cord cells, one between gut tube cells and endothelium, and one between neural crest and endothelium, all of which have already been reported in the literature thus offering credibility of all three methods. However, we hypothesize that CGCom is superior in reducing false positives thanks to its use of cell proximal information and its learning between specific cell pairs rather than between cell types. CGCom is a GNN-based solution that can take advantage of spatially resolved single-cell transcriptomic data in predicting cell-cell communication with a higher accuracy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680670PMC
http://dx.doi.org/10.1101/2023.11.10.566642DOI Listing

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