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

基本信息

批准号：
10453133
负责人：
Dongjun Chung
金额：
$ 19.15万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10453133
关键词：
Address Affect Alzheimer&apos 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 Molecular Neurodegenerative Disorders Ontology Organ Outcome Pathogenicity Pattern Public Domains Regulator Genes Research Research Personnel Sample Size Scientist Slide Software Design Spatial Design Spottings Statistical Models Technology Tissues autoencoder base cell type computer framework computerized tools design experimental study flexibility gene expression variation high throughput analysis imaging Segmentation improved insight neuropsychiatry new technology novel therapeutics open source power analysis simulation single cell sequencing single-cell RNA sequencing statistics tool tool development transcriptomics user friendly software user-friendly web interface

项目摘要

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.

抽象的最近，高通量空间转录组学 (HST) 技术（例如 10X Genomics Visium、Slide-seq、和 Slide-seqV2）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。虽然经过一些计算 HST 数据分析工具最近已推出，这是一个用于 HST 设计的严格统计框架文献中仍然缺少实验。计划 HST 实验的研究人员需要确定各种实验设计参数，例如测序深度，这些选择会影响关键目标是否可以实现 HST 实验，例如组织结构的识别、空间可变基因和细胞- 细胞通讯。在本提案中，我们的目标是为 HST 开发严格的功耗分析框架实验。组建的团队在 HST 数据统计建模方面拥有强大且互补的专业知识，开发用于功率分析和高通量测序设计的统计框架和软件数据、单细胞基因组学技术、空间统计、计算工具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。我们将实现通过实施两个具体目标提出的目标。在目标 1 中，我们将开发严格的功耗分析框架用于 HST 实验。在目标 2 中，我们将开发一个交互式 Web 界面和一个用于功耗分析的 R 包 HST 实验。所提出的功率分析框架将使用仿真来开发和评估数据、公共领域的 HST 数据以及来自合作者的内部 HST 数据集。统计框架将在该项目中开发的，以及实现该框架的开源软件，将为未来 HST 实验的优化设计提供必要的工具。