Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany.
Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
Genome Biol. 2022 Apr 14;23(1):97. doi: 10.1186/s13059-022-02663-5.
The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features.
高度多重化空间技术的进步需要可扩展的方法，这些方法可以利用空间信息。我们提出了 MISTy，这是一个灵活、可扩展和可解释的机器学习框架，用于从任何空间组学数据中提取关系，从几十个到几千个测量标记。MISTy 构建了多个视图，侧重于不同的空间或功能背景，以剖析不同的影响。我们在通过成像质谱细胞术和空间转录组学测量的计算机模拟和乳腺癌数据集上评估了 MISTy。我们估计了来自乳腺癌不同空间背景的结构和功能相互作用，并展示了如何将 MISTy 的结果与临床特征联系起来。
