OrgaCCC: Orthogonal graph autoencoders for constructing cell-cell communication networks on spatial transcriptomics data

Cell-cell communication (CCC) is a fundamental biological process essential for maintaining the functionality of multicellular organisms. It allows cells to coordinate their activities, sustain tissue homeostasis, and adapt to environmental changes. However, understanding the mechanisms underlying intercellular communication remains challenging. The rapid advancements in spatial transcriptomics (ST) have enabled the analysis of CCC within its spatial context. Despite the development of several computational methods for inferring CCCs from ST data, most rely on literature-curated gene or protein interaction lists, which are often inadequate due to the restricted gene coverage. In this work, we propose OrgaCCC, an orthogonal graph autoencoders approach for cell-cell communication inference based on deep generative models. OrgaCCC leverages the information of gene expression profiles, spatial locations and ligand-receptor relationships. It captures both cell/spot and gene features using two orthogonally coupled variational graph autoencoders across cell/spot and gene dimensions and combines them by maximizing the similarity between their reconstructed cell/spot features. Numerical experiments on five ST datasets demonstrate the superiority of OrgaCCC compared with state-of-the-art methods in CCC inference at the cell-type level, cell/spot level, and ligand-receptor level, in terms of inference accuracy and reliability.