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

最近出现的各种空间分辨转录组学技术使得能够研究 在组织切片或单细胞内的空间转录组景观，催化新的发现， 生物学的许多领域。尽管空间转录组学技术发展迅速，然而， 用于分析空间转录组学数据的统计方法还远远不发达。分析空间 转录组数据面临着重要的统计挑战，这些挑战来自于转录组数据的复杂性和独特性， 这些数据。在此，我们建议解决这一新兴领域的一些关键统计挑战 通过开发一套新颖的统计方法。具体而言，我们将（1）开发高斯预测 处理模型，以计算有效的方式对空间相关结构进行建模， 具有空间表达模式的基因;（2）开发整合方法以整合参考单细胞 RNA测序数据沿着与空间转录组学中的空间相关结构， （3）开发Potts模型以执行组织分割和检测 组织区域和微环境中的一个从头开始的方式。我们将开发、分发和支持用户友好的 开放源码软件，执行所提出的方法，并将其传播给科学界， 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 从不同的技术平台收集的多个公共空间转录组学数据， 不同的尺度。我们还将进行深入分析，并对空间 转录组学数据作为研究乳腺癌中干细胞生物学失调的作用的一部分而收集。 非裔美国女性的癌症差异。

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.