DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies

最近出现的各种空间分辨转录本技术使研究 跨组织切片或在单个细胞内的空间转录景观，催化在 生物学的许多领域。然而，尽管空间转录技术发展迅速， 分析空间转录数据的统计方法非常不发达。分析空间 转录数据面临着因其复杂性和独特性而产生的重大统计挑战 这些数据。在这里，我们建议解决这一新兴领域中的一些关键统计挑战 通过开发一套新的统计方法。具体地说，我们将(1)开发高斯预测 过程模型，以计算有效的方式对空间相关性结构进行建模，以快速识别 具有空间表达模式的基因；(2)开发整合参考单细胞的方法 RNA测序数据与空间转录中的空间相关性结构一起使准确 细胞类型在组织上的去卷积；(3)建立Potts模型来执行组织分割和检测 组织区域和微环境以从头开始的方式。我们将开发、分发和支持用户友好型 实施所提出的方法的开源软件，并将其传播给 社区。我们将进行严格和全面的模拟，并应用我们的方法来分析 从不同技术平台收集的多个公共空间转录数据， 不同的尺度。我们还将进行深入的分析，并对空间进行补充实验 作为失调干细胞生物学在乳房中的作用研究的一部分，正在收集转录组数据 非裔美国女性患癌症的差异。

The recent emergence of various spatially resolved transcriptomic technologies have enabled the study of spatial transcriptomic landscape across a tissue section or within single cells, catalyzing new discoveries in many areas of biology. Despite the fast development of spatial transcriptomic technologies, however, statistical methods for analyzing spatial transcriptomic data are vastly underdeveloped. Analyzing spatial transcriptomic data faces important statistical challenges that arise from the complexities and unique features of these data. Here, we propose to address some of these key statistical challenges in this emerging field through developing a suite of novel statistical methods. Specifically, we will (1) develop Gaussian predictive process models to model the spatial correlation structure in a computational effective way to rapidly identify genes with spatial expression patterns; (2) develop integrative methods to incorporate reference single cell RNA sequencing data along with spatial correlation structure in spatial transcriptomics to enable accurate deconvolution of cell types on the tissue; (3) develop Potts models to perform tissue segmentation and detect tissue regions and microenvironment in a de novo fashion. We will develop, distribute, and support user-friendly open-source software implementing the proposed methods and disseminate them to the scientific community. We will perform rigorous and comprehensive simulations and apply our methods to analyze multiple public spatial transcriptomics data that are collected from different technical platforms and are of different scales. We will also perform an in-depth analysis with supplemental experiments on the spatial transcriptomics data being collected as part of the study of the role of dysregulated stem cell biology in breast cancer disparities in African American women.