Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, 200433, China.
Nat Commun. 2024 Jan 5;15(1):207. doi: 10.1038/s41467-023-44367-9.
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
组织结构识别是空间组学数据分析中的一项关键任务，为此越来越复杂的模型，如图神经网络和贝叶斯网络，被应用于该任务。然而，模型复杂度的增加是否能有效提高性能，这在该领域是一个值得注意的问题。受各种空间技术中细胞邻域结构的一致观察的启发，我们提出了用于组织结构识别的多范围细胞上下文解析器（MENDER）。在 3 个脑区数据集和全脑图谱上的应用表明，MENDER 具有生物学驱动的设计，在自动对齐切片标签的同时，提供了比现代复杂模型实质性的改进，尽管运行时间比第二快的模型少得多。MENDER 的识别能力允许揭示以前被忽视的与大脑衰老强烈相关的空间域。MENDER 的可扩展性使其能够自由应用于百万级别的大脑空间图谱。MENDER 的判别能力能够区分被单细胞分析掩盖的乳腺癌患者亚型。
