Overview of Proust for detecting discrete domains using spatial multi-omics data. For the purposes of clarity, we introduce Proust with two specific omic data modalities—RNA and protein—but these ideas can be generalized to other multi-omics, such as RNA and brightfield images. First, Proust constructs a graph structure based on the Euclidean distance between spatial coordinates. Next, graph-based convolutional autoencoders are trained separately for gene expression and protein information extracted from an immunofluorescence (IF) image. The latent embeddings are refined using contrastive self-supervised learning (CSL). The top principal components (PCs) from the reconstructed gene and image features are concatenated to create a hybrid profile for downstream clustering analysis.
