GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data
Scientific Reports, vol. 15, pp. 36914
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.
Authors 5
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University Health Network · University of Toronto · Princess Margaret Cancer Centre
Affiliation as printed
Department of Medical Biophysics, University of Toronto, Toronto, Canada
Peter Munk Cardiac Centre, Toronto, Canada
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
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Affiliation as printed
Institute of Experimental Medicine and Systems Biology, UniKlinik RWTH Aachen, Aachen, Germany
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University Health Network · Princess Margaret Cancer Centre
Affiliation as printed
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
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Bo Wang corresponding
University of Toronto · Vector Institute
Affiliation as printed
Department of Computer Science, University of Toronto, Toronto, Canada. bowang@vectorinstitute.ai
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada. bowang@vectorinstitute.ai
Peter Munk Cardiac Centre, Toronto, Canada. bowang@vectorinstitute.ai
Vector Institute for Artificial Intelligence, Toronto, Canada. bowang@vectorinstitute.ai
Department of Computer Science, University of Toronto, Toronto, Canada
Vector Institute for Artificial Intelligence, Toronto, Canada
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Benjamin Haibe‐Kains corresponding
University Health Network · University of Toronto · Princess Margaret Cancer Centre · Structural Genomics Consortium · Vector Institute
Affiliation as printed
Department of Computer Science, University of Toronto, Toronto, Canada. Benjamin.haibe.kains@utoronto.ca
Department of Medical Biophysics, University of Toronto, Toronto, Canada. Benjamin.haibe.kains@utoronto.ca
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Benjamin.haibe.kains@utoronto.ca
Structural Genomics Consortium, University Health Network, Toronto, Canada. Benjamin.haibe.kains@utoronto.ca
Vector Institute for Artificial Intelligence, Toronto, Canada. Benjamin.haibe.kains@utoronto.ca
Department of Computer Science, University of Toronto, Toronto, Canada
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
Vector Institute for Artificial Intelligence, Toronto, Canada
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