DeepST: identifying spatial domains in spatial transcriptomics by deep learning

TLDR

Spatial transcriptomics offers unprecedented insight into tissue organization, yet accurately delineating spatial domains with similar gene expression and histology remains challenging. The study introduces DeepST, a deep learning framework aimed at accurately identifying spatial domains in spatial transcriptomics data. DeepST employs a universal deep learning architecture that integrates spatial transcriptomics data across batches and technologies, enabling domain identification and extensibility to other spatial omics. DeepST outperforms state‑of‑the‑art methods on human dorsolateral prefrontal cortex benchmarks and accurately delineates finer spatial domains in breast cancer tissue, demonstrating exceptional domain‑identification capability.

Abstract

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.