Statistical methods for spatial RNA sequencing experiments

摘要 空间RNA测序已经成为一种革命性的工具，使我们能够解决科学问题 这些在几年前还很难实现。具体而言，空间RNA测序技术具有潜力 彻底改变健康和疾病中组织结构和功能的研究。然而，大部分的潜力 由于缺乏分析空间RNA-seq数据的统计方法，对于许多类型的 分析，目前使用的方法模糊，在某些情况下，扭曲的生物信号。一些 必须解决统计和计算方面的挑战，以防止得出不准确的结论， 优化新发现。本提案针对这些挑战。特别是，虽然这项技术 强大，但并非没有错误;空间RNA-seq数据中存在相当大的污染。我们提出 方法来去除这种污染，从而确保稳健和准确的下游推断。我们 还提出了统计方法来调整由测序深度差异引起的技术可变性。 通过减少技术可变性，这些方法将提高感兴趣的信号可以被传输的功率。 研究了最后，我们提出的方法来表征基因集的依赖结构的变化。 需要这些类型的方法来提高我们对基因协调变化的理解 影响健康和疾病中的组织结构和功能。综上所述，本项目圆满完成 将有助于确保从强大的空间RNA-seq实验中获得最大的信息。

Abstract Spatial RNA-sequencing has emerged as a revolutionary tool that allows us to address scientific questions that were elusive just a few years ago. Specifically, the spatial RNA-sequencing technology has the potential to revolutionize studies of tissue structure and function in health and disease. However, much of the potential has yet to be realized as statistical methods to analyze spatial RNA-seq data are lacking. For many types of analyses, the methods currently in use obscure and, in some cases, distort biological signals. A number of statistical and computational challenges must be addressed to prevent inaccurate conclusions, and to optimize novel discovery. This proposal addresses those challenges. In particular, while the technology is powerful, it is not without error; and considerable contamination exists in spatial RNA-seq data. We propose methods to remove this contamination and thereby ensure robust and accurate downstream inference. We also propose statistical methods to adjust for technical variability induced by differences in sequencing depth. By reducing technical variability, these methods will improve the power with which signals of interest can be studied. Finally, we propose methods for characterizing changes in the dependence structure of sets of genes. These types of methods are required to improve our understanding of how coordinated changes in genes affect tissue structure and function in health and disease. Taken together, successful completion of this project will help to ensure that maximal information is obtained from powerful spatial RNA-seq experiments.