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：
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
项目类别：
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&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 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

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，10倍基因组学森林，幻灯片seq，和幻灯片seqv2）使得可以同时测量近距离的基因表达式和这些细胞在组织或器官中的空间位置。这些新技术提供了前所未有的研究细胞异质性和细胞通信的机会。虽然一些计算 HST数据分析的工具最近已成为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，这些选择都会影响可以实现HST实验，例如，鉴定组织结构，空间可变基因和细胞 - 细胞通信。在此提案中，我们旨在为HST开发严格的功率分析框架实验。组装团队在HST数据的统计建模方面具有强大而互补的专业知识，开发用于高通量测序的功率分析和设计的统计框架和软件数据，单细胞基因组技术，空间统计，计算工具开发和利用这些用于研究疾病分子和免疫学基础的计算工具。我们将实现提出的目标通过实施两个具体目标。在AIM 1中，我们将开发一个严格的功率分析框架用于HST实验。在AIM 2中，我们将开发一个交互式Web界面和一个用于电源分析的R软件包 HST实验。建议的功率分析框架将使用模拟制定和评估数据，公共领域中的HST数据以及来自协作者的内部HST数据集。统计框架这将在该项目中开发，以及实施此框架的开源软件将为未来HST实验的最佳设计提供必不可少的工具。