Researchers have developed methods for integrating multiple spatial transcriptomics datasets from diverse sources, enabling spatial analysis and discovery. These approaches aim to provide interpretable and flexible tools for understanding spatial patterns and cell states in various biological contexts. Different techniques, such as dimension reduction and ensemble learning, are being explored to uncover spatial domains and bridge molecular and morphological data.