CoCo-ST detects global and local biological structures in spatial transcriptomics datasets v1

作者：Muhammad Aminu, Bo Zhu, Natalie I. Vokes, Jia Wu, Jianjun Zhang · 年份：2025 · DOI：10.17504/protocols.io.x54v95m6pl3e/v1 · 被引用次数：2 · 研究领域：Single-cell and spatial transcriptomics、Molecular Biology Techniques and Applications、Gene expression and cancer classification

A protocol describing the application of the CoCo-ST algorithm on spatial transcriptomics datasets to detect biologically relevant spatial structures. We demonstrate CoCo-ST's ability to detect low variance spatial structures driving tumor progression. Two main challenges with precancer analysis are: The high variance in global spatial patterns which might lead to the overlook of local, low-variance domains that hold significant biological relevance. The complexity of spatial domain detection across diverse tissue samples with varying spatial resolutions, necessitating a robust and scalable approach. Here, we show CoCo-ST’s contrastive learning approach for identifying both high and low-variance spatial structures, leveraging multi-sample data. This protocol is associated with our proposed CoCo-ST algorithm for spatial domain detection in Nature Cell Biology