Graph Neural Network approaches for single-cell data: A recent overview
Konstantinos Lazaros Dimitris E. Koumadorakis Panagiotis Vlamos Aristidis G. Vrahatis
Graph Neural Networks (GNN) are reshaping our understanding of biomedicine
and diseases by revealing the deep connections among genes and cells. As both
algorithmic and biomedical technologies have advanced significantly, we're
entering a transformative phase of personalized medicine. While pioneering
tools like Graph Attention Networks (GAT) and Graph Convolutional Neural
Networks (Graph CNN) are advancing graph-based learning, the rise of
single-cell sequencing techniques is reshaping our insights on cellular
diversity and function. Numerous studies have combined GNNs with single-cell
data, showing promising results. In this work, we highlight the GNN
methodologies tailored for single-cell data over the recent years. We outline
the diverse range of graph deep learning architectures that center on GAT
methodologies. Furthermore, we underscore the several objectives of GNN
strategies in single-cell data contexts, ranging from cell-type annotation,
data integration and imputation, gene regulatory network reconstruction,
clustering and many others. This review anticipates a future where GNNs become
central to single-cell analysis efforts, particularly as vast omics datasets
are continuously generated and the interconnectedness of cells and genes
enhances our depth of knowledge in biomedicine.