A model-based constrained deep learning approach for clustering spatial-resolved single-cell data.

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Abstract

Spatial-resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile the gene expression pattern in the tissue context. However, the development of computational methods does not catch up with the fast advances of technologies and fails to fully fulfill their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatial-constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process by two steps: 1) the spatial information is encoded by using a graphical neural network model; 2) cell-to-cell constraints are built based on the spatially expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottle-neck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model which can utilize the information from both the spatial coordinates and the marker genes to guide the cell/spot clustering. Extensive experiments on both simulated and real datasets demonstrate that DSSC boosts clustering performance significantly compared to the state-of-art methods. It has a robust performance over different datasets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.Published by Cold Spring Harbor Laboratory Press.