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Leveraging stable diffusion to reconstruct missing core intervals
NiH-Catalyzed Homobenzylic Hydroalkylation of Aryl Alkenes Using Sulfoxonium Ylides
Nomogram predicts in-hospital mortality in patients with emergency gastrointestinal bleeding: A multicenter retrospective study
Shaping the Glycan Landscape: Hidden Relationships between Linkage and Ring Distortions Induced by Carbohydrate-Active Enzymes
GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data
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.