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GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

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Veröffentlicht in:bioRxiv (Dec 21, 2024)
1. Verfasser: So, Emily
Weitere Verfasser: Sikander Hayat, Nair, Sisira Kadambat, Wang, Bo, Haibe-Kains, Benjamin
Veröffentlicht:
Cold Spring Harbor Laboratory Press
Schlagworte:
Gene expression
Cell interactions
Deep learning
Datasets
Transcriptomics
Communication
Signal transduction
Intracellular signalling
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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 cell-cell communication inference, we present GraphComm - a new graph-based deep learning method for predicting cell-cell communication 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.Competing Interest StatementBHK is a shareholder and paid consultant for Code Ocean Inc. ES is paid consultant for Code Ocean Inc.Footnotes* Figure 4F replaced with new results; Extended Data Figure 1 replaced with new results.
ISSN:2692-8205
DOI:10.1101/2023.04.26.538432
Quelle:Biological Science Database