DMS/NIGMS 1: Statistical Methods for Design and Analysis of Clinical-scale Single Cell Studies

DMS/NIGMS 1：临床规模单细胞研究设计和分析的统计方法

• 批准号: 2245575
• 负责人: Nancy Zhang
• 金额: $ 60万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245575&HistoricalAwards=false
• 关键词: DMS; NIGMS; Statistical; Methods; Design

Over the last two decades, scientific discoveries in biology and medicine have increasingly relied on the statistical analysis of large genomic data sets. Thus, the development of rigorous statistical methods for reproducible and scalable analyses on such data sets, and the interdisciplinary training of a next generation of scientists who can straddle the computational and biomedical sciences, is crucial for our scientific advancement. This project focuses on challenges in the analysis of data from single cell experiments, which are made possible by recent technological developments enabling genome-scale measurements to be made on individual cells, in high throughput across millions of cells at one go. Such single cell experiments are now the bread-and-butter of biomedical research, playing a cardinal role in our quest for a more complete understanding of cell biology as well as our pursuit of cures for every disease, from infectious diseases such as COVID-19 to cancer to aging-related maladies such as neurodegeneration. Despite the promise of these new technologies, current methods for single cell data analysis are not designed for clinical-scale disease research, and do not adequately harness the information provided by the many recently completed single cell reference atlases, the latter realized through years of consortia-level efforts and millions of dollars in national funding. This project aims to bridge this computational analysis gap, specifically by addressing two critical limitations in the field: (1) The lack of a principled approach for the removal of unwanted technical variation from clinical-scale single cell sequencing studies, and (2) the need for an integrated approach to clinical-scale proteomic profiling that harness single cell reference atlases to achieve more precise cell type composition analysis. For both problems, the project’s goal is to develop principled, transparent, and scalable methods for reproducible scientific research. Broader impacts of this project are its potential impact in improving the well-being of individuals in society; its contributions in STEM education for undergraduate and graduate students; and in the involvement and participation in research of women and under-represented minorities in STEM fields.In more technical detail, the first aim of the project focuses on the removal of technical “batch effects”. Technical batch correction is an unavoidable and challenging step in *all* single cell data analysis pipelines. Many methods have been proposed for batch effect correction in single cell experiments, but they were designed to align cells across all batches, ignoring design principles such as control cohorts, longitudinal sampling, and biological replicates. Without utilizing these design principles, existing methods can not adequately estimate batch effects and often confound them with real biological signals. Aim 1 of the project formulates new statistical methods for batch effect correction that are adaptable to a multitude of experimental designs and develops statistical inference procedures for quantifying the strength of biological signals (e.g. differential expression or emergence of a new cell type) accounting for the uncertainty in batch correction. Aim 2 of the project tackles a different challenge arising in clinical-scale single cell proteomic profiling: Despite decreasing sequencing costs, flow and mass cytometry are still orders-of-magnitude faster and cheaper, and thus remains the method of choice in clinical-scale immunological studies where large cohorts need to be profiled on a tight timeline. However, each flow/mass cytometry run only measures a limited panel of proteins, and thus does not allow cell-type labelling at the level of detail afforded by single cell transcriptomics. Aim 2 develops a new approach to cell type profiling that integrates multiple flow/mass cytometry runs, with complementary panels, on the same sample, with the goal of achieving cell-type tabulation accuracy matching state-of-the-art single cell sequencing protocols at only a fraction of time and cost. This aim leverages the growing compendium of single cell reference atlases, providing a roadmap for the future use of these atlases in population-level cell type censusing projects. Through collaborations, the developed methods will be applied to multiple ongoing large-cohort studies that have direct clinical impact. Methods developed in this project will be released as open-source software, and the datasets generated will be uploaded to public repository for general use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的二十年里，生物学和医学的科学发现越来越依赖于对大型基因组数据集的统计分析。 因此，开发严格的统计方法，对这些数据集进行可重复和可扩展的分析，以及对能够跨越计算科学和生物医学科学的下一代科学家进行跨学科培训，对我们的科学进步至关重要。 该项目的重点是单细胞实验数据分析方面的挑战，这是通过最近的技术发展实现的，这些技术发展使基因组规模的测量能够在单个细胞上进行，一次性高通量地跨越数百万个细胞。 这种单细胞实验现在是生物医学研究的基础，在我们寻求更完整地理解细胞生物学以及寻求治愈每一种疾病方面发挥着至关重要的作用，从COVID-19等传染病到癌症再到与衰老相关的疾病，如神经退行性疾病。 尽管这些新技术有希望，但目前的单细胞数据分析方法并不是为临床规模的疾病研究而设计的，也没有充分利用许多最近完成的单细胞参考图谱提供的信息，后者是通过多年的联盟级努力和数百万美元的国家资金实现的。 该项目旨在弥合这一计算分析差距，特别是通过解决该领域的两个关键限制：（1）缺乏从临床规模的单细胞测序研究中去除不必要的技术变化的原则性方法，以及（2）需要一种利用单细胞参考图谱进行临床规模蛋白质组学分析的综合方法，以实现更精确的细胞类型组成分析。 对于这两个问题，该项目的目标是为可重复的科学研究开发原则性，透明和可扩展的方法。该项目更广泛的影响是其在改善社会个人福祉方面的潜在影响;其在本科生和研究生STEM教育方面的贡献;以及在STEM领域妇女和代表性不足的少数群体参与和参与研究方面的贡献。 技术批次校正是 * 所有 * 单细胞数据分析流程中不可避免且具有挑战性的步骤。 已经提出了许多方法用于单细胞实验中的批次效应校正，但它们被设计为在所有批次中对齐细胞，忽略了设计原则，如对照组，纵向采样和生物重复。 如果不利用这些设计原则，现有的方法不能充分估计批量效应，往往混淆他们与真实的生物信号。 该项目的目标1为批量效应校正制定了新的统计方法，这些方法适用于多种实验设计，并开发了统计推断程序，用于量化生物信号的强度（例如，差异表达或新细胞类型的出现），以解释批量校正中的不确定性。 该项目的目标2解决了临床规模单细胞蛋白质组学分析中出现的不同挑战：尽管测序成本降低，但流式细胞术和质谱仪仍然是数量级更快，更便宜的方法，因此仍然是临床规模免疫学研究中的首选方法，其中需要在紧凑的时间轴上对大型队列进行分析。然而，每个流式/质谱细胞术运行仅测量有限的蛋白质组，因此不允许在单细胞转录组学提供的细节水平上进行细胞类型标记。Aim 2开发了一种新的细胞类型分析方法，该方法将多个流式/质谱细胞术运行与互补组集成在同一样品上，其目标是以一小部分时间和成本实现与最先进的单细胞测序方案相匹配的细胞类型制表准确度。 这一目标利用了不断增长的单细胞参考图谱汇编，为未来在人群水平的细胞类型普查项目中使用这些图谱提供了路线图。 通过合作，开发的方法将应用于多个正在进行的具有直接临床影响的大型队列研究。该项目开发的方法将作为开源软件发布，生成的数据集将上传到公共存储库供一般使用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。