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

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

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.

Authors 5

1. Emily So

   University of Toronto · University Health Network · 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

2. Sikander Hayat Aachen Institute of Experimental Medicine and Systems Biology

   Universitätsklinikum Aachen

   Affiliation as printed

   Institute of Experimental Medicine and Systems Biology, UniKlinik RWTH Aachen, Aachen, Germany

3. Sisira Kadambat Nair

   University Health Network · Princess Margaret Cancer Centre

   Affiliation as printed

   Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

4. Bo Wang corresponding

   University of Toronto · Vector Institute

   Affiliation as printed

   Department of Computer Science, University of Toronto, Toronto, Canada

   Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada

   Peter Munk Cardiac Centre, Toronto, Canada

   Vector Institute for Artificial Intelligence, Toronto, Canada

5. Benjamin Haibe‐Kains corresponding

   University of Toronto · University Health Network · Vector Institute · Ontario Institute for Cancer Research · Princess Margaret Cancer Centre

   Affiliation as printed

   Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada

   Department of Computer Science, University of Toronto, Toronto, Canada

   Department of Medical Biophysics, University of Toronto, Toronto, Canada

   Ontario Institute for Cancer Research, Toronto, Canada

   Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

   Vector Institute for Artificial Intelligence, Toronto, Canada