Title: Community Detection Analysis of Spatial Transcriptomics Data Show Chinese title

Title: 空间转录组数据的社区检测分析

Abstract: The spatial transcriptomics (ST) data produced by recent biotechnologies, such as CosMx and Xenium, contain huge amount of information about cancer tissue samples, which has great potential for cancer research via detection of community: a collection of cells with distinct cell-type composition and similar neighboring patterns. But existing clustering methods do not work well for community detection of CosMx ST data, and the commonly used kNN compositional data method shows lack of informative neighboring cell patterns for huge CosMx data. In this article, we propose a novel and more informative disk compositional data (DCD) method, which identifies neighboring patterns of each cell via taking into account of ST data features from recent new technologies. After initial processing ST data into DCD matrix, a new innovative and interpretable DCD-TMHC community detection method is proposed here. Extensive simulation studies and CosMx breast cancer data analysis clearly show that our proposed DCD-TMHC method is superior to other methods. Based on the communities detected by DCD-TMHC method for CosMx breast cancer data, the logistic regression analysis results demonstrate that DCD-TMHC method is clearly interpretable and superior, especially in terms of assessment for different stages of cancer. These suggest that our proposed novel, innovative, informative and interpretable DCD-TMHC method here will be helpful and have impact to future cancer research based on ST data, which can improve cancer diagnosis and monitor cancer treatment progress. Show Chinese abstract

Abstract: 空间转录组（ST）数据由最近的生物技术，如CosMx和Xenium产生，包含关于癌症组织样本的大量信息，通过检测社区（具有不同细胞类型组成和相似邻近模式的细胞集合）在癌症研究中具有巨大潜力。 但现有的聚类方法在CosMx ST数据的社区检测中表现不佳，常用的kNN组成数据方法对于庞大的CosMx数据缺乏有信息量的邻近细胞模式。 在本文中，我们提出了一种新颖且更具信息量的圆盘组成数据（DCD）方法，该方法通过考虑来自最新新技术的ST数据特征来识别每个细胞的邻近模式。 在将初始处理后的ST数据转化为DCD矩阵后，这里提出了一种新的创新且可解释的DCD-TMHC社区检测方法。 广泛的模拟研究和CosMx乳腺癌数据的分析清楚地表明，我们提出的DCD-TMHC方法优于其他方法。 基于DCD-TMHC方法对CosMx乳腺癌数据检测到的社区，逻辑回归分析结果表明DCD-TMHC方法明显可解释且优越，尤其是在评估癌症不同阶段方面。 这些结果表明，本文提出的新型、创新、信息丰富且可解释的DCD-TMHC方法将有助于基于ST数据的未来癌症研究，从而提高癌症诊断并监测癌症治疗进展。