Statistical methods for spatial RNA sequencing experiments

空间RNA测序实验的统计方法

• 批准号: 10298679
• 负责人: Christina Kendziorski
• 金额: $ 33.77万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10298679
• 关键词: Address; Affect; Area; Bar Codes; Binding; Biological; Case Study; Cell Lineage; Cells; Communication; Communities; Computer software; Coupled; Data; Data Analyses; Dependence; Detection; Development; Diagnostic; Disease; Ensure; Gene Expression; Genes; Genome; Genomics; Health; Measurement; Measures; Messenger RNA; Methods; Molecular; Morphologic artifacts; Noise; Pathway interactions; Protocols documentation; Regulation; Research; Research Personnel; Signal Transduction; Slide; Solid; Source; Spottings; Statistical Methods; Structure; System; Technology; Tissue imaging; Tissues; Variant; Visualization; base; biological systems; differential expression; experimental study; improved; insight; interest; laboratory experiment; mRNA Expression; new technology; novel; prevent; simulation; single-cell RNA sequencing; tool; trait; transcriptome sequencing; transcriptomics

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.

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 就在几年前，这些还难以捉摸。具体地说，空间rna测序技术具有 为健康和疾病中组织结构和功能的研究带来革命性的变化。然而，许多潜在的 由于缺乏分析空间RNA-seq数据的统计方法，因此尚未实现。对于许多类型的 据分析，目前使用的方法模糊了生物信号，在某些情况下甚至扭曲了生物信号。一批 必须解决统计和计算方面的挑战，以防止不准确的结论，并 优化新发现。这项提议解决了这些挑战。特别是，虽然这项技术是 它强大，但也不是没有误差；而且空间RNA-seq数据中存在相当大的污染。我们建议 消除这种污染的方法，从而确保健壮和准确的下游推断。我们 还提出了统计方法，以调整由测序深度差异引起的技术变异性。 通过减少技术可变性，这些方法将提高感兴趣信号的处理能力 学习。最后，我们提出了描述基因集合依赖结构变化的方法。 需要这些类型的方法来提高我们对基因如何协调变化的理解 在健康和疾病中影响组织结构和功能。总而言之，这个项目圆满完成了 将有助于确保从强大的空间RNA-SEQ实验中获得最大信息。