CellVGAE uses the connectivity between cells (such as k-nearest neighbour graphs or KNN) with gene expression values as node features to learn high-quality cell representations in a lower-dimensional space, with applications in downstream analyses like (density-based) clustering, visualisation, gene set enrichment analysis and others.
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Buterez D, Bica I, Tariq I, Andrés-Terré H, Liò P.
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks.
Bioinformatics. 2021 Dec 2:btab804. doi: 10.1093/bioinformatics/btab804. Epub ahead of print. PMID: 34864884.