Statistical Power Calculation Framework for Spatially Resolved Transcriptomics Experiments

空间分辨转录组学实验的统计功效计算框架

• 批准号: 10629262
• 负责人: Dongjun Chung
• 金额: $ 23万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629262
• 关键词: Address; Affect; Alzheimer' s Disease; Architecture; Benchmarking; Bioconductor; Biological; Cell Communication; Cells; Clinical; Code; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Detection; Development; Dimensions; Discipline; Disease; Disease Progression; Environment; Experimental Designs; Foundations; Future; Gene Expression; Genes; Genomics; Goals; Graph; Heterogeneity; High-Throughput Nucleotide Sequencing; Immunologics; Immunooncology; Investigation; Literature; Location; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Modeling; Molecular; Neurodegenerative Disorders; Ontology; Organ; Outcome; Pathogenicity; Pattern; Process; Public Domains; Research; Research Personnel; Sample Size; Scientist; Slide; Software Design; Spatial Design; Spottings; Statistical Models; Techniques; Technology; Tissues; autoencoder; cell type; computer framework; computerized tools; design; distributed data; experimental study; flexibility; gene expression variation; gene network; gene regulatory network; high throughput analysis; imaging Segmentation; improved; insight; neuropsychiatry; new technology; novel therapeutic intervention; open source; power analysis; simulation; single cell sequencing; single-cell RNA sequencing; statistics; tool; tool development; transcriptomics; user friendly software; user-friendly; web interface; web server

Abstract Recently, high-throughput spatial transcriptomics (HST) technologies (e.g., 10X Genomics Visium, Slide-seq, and Slide-seqV2) have made it possible to simultaneously measure close-to-cell-level gene expressions and spatial locations of these cells within a tissue or organ. These new technologies have provided an unprecedented opportunity to investigate cellular heterogeneity and cell-cell communications. Although a few computational tools for HST data analysis have recently become available, a rigorous statistical framework for design of HST experiments is still missing in the literature. Researchers planning an HST experiment need to determine various experimental design parameters such as the sequencing depth, and these choices affect whether key goals of HST experiments can be achieved, e.g., identification of tissue architecture, spatially variable genes, and cell- cell communications. In this proposal, we aim to develop a rigorous power analysis framework for HST experiments. The assembled team has strong and complementary expertise in statistical modeling of HST data, development of statistical frameworks and software for power analysis and design of high throughput sequencing data, single-cell genomics technologies, spatial statistics, computational tool development, and utilization of these computational tools for investigation of molecular and immunologic basis of diseases. We will achieve the proposed goal by implementing two specific aims. In Aim 1, we will develop a rigorous power analysis framework for HST experiments. In Aim 2, we will develop an interactive web interface and an R package for power analysis of HST experiments. The proposed power analysis framework will be developed and evaluated using simulation data, HST data in the public domain, and in-house HST datasets from collaborators. The statistical framework that will be developed in this project, along with the open-source software implementing this framework, will provide essential tools for the optimal design of future HST experiments.

摘要 最近，高通量空间转录组学（HST）技术（例如，10X Genomics Visium，Slide-seq， 和Slide-seqV 2）使得同时测量接近细胞水平的基因表达成为可能， 这些细胞在组织或器官内的空间位置。这些新技术提供了前所未有的 研究细胞异质性和细胞间通讯的机会。虽然一些计算 HST数据分析的工具最近已经出现，HST设计的一个严格的统计框架 实验在文献中仍然缺失。计划HST实验的研究人员需要确定各种 实验设计参数，如测序深度，这些选择影响是否关键目标， 可以实现HST实验，例如，组织结构、空间可变基因和细胞- 手机通讯在本提案中，我们的目标是为HST开发一个严格的功耗分析框架 实验组建的团队在HST数据的统计建模方面拥有强大的互补专业知识， 开发统计框架和软件，用于功效分析和高通量测序设计 数据，单细胞基因组学技术，空间统计，计算工具的开发和利用， 这些计算工具用于研究疾病的分子和免疫学基础。我们将实现 提出了两个具体目标。在目标1中，我们将开发一个严格的功率分析框架 进行HST实验。在目标2中，我们将开发一个交互式Web界面和一个用于功率分析的R软件包 HST实验拟议的功率分析框架将使用模拟进行开发和评估 数据，公共领域的HST数据，以及来自合作者的内部HST数据集。统计框架 沿着实现该框架的开源软件， 为未来HST实验的优化设计提供必要的工具。