ST-Community Detection Methods for Spatial Transcriptomics Data Analysis

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