Chen Pingjun, Aminu Muhammad, Hussein Siba El, Khoury Joseph D, Wu Jia
Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Pathology, University of Rochester Medical Center, NY, USA.
Softw Impacts. 2021 Nov;10. doi: 10.1016/j.simpa.2021.100156. Epub 2021 Oct 9.
We present CellSpatialGraph, an integrated clustering and graph-based framework, to investigate the cellular spatial structure. Due to the lack of a clear understanding of the cell subtypes in the tumor microenvironment, unsupervised learning is applied to uncover cell phenotypes. Then, we build local cell graphs, referred to as supercells, to model the cell-to-cell relationships at a local scale. After that, we apply clustering again to identify the subtypes of supercells. In the end, we build a global graph to summarize supercell-to-supercell interactions, from which we extract features to classify different disease subtypes.
我们提出了CellSpatialGraph，这是一个集成的聚类和基于图的框架，用于研究细胞空间结构。由于对肿瘤微环境中的细胞亚型缺乏清晰的了解，因此应用无监督学习来揭示细胞表型。然后，我们构建局部细胞图，即超级细胞，以在局部尺度上模拟细胞间关系。之后，我们再次应用聚类来识别超级细胞的亚型。最后，我们构建一个全局图来总结超级细胞间的相互作用，并从中提取特征以对不同疾病亚型进行分类。
