Resolving single-cell analysis challenges via data-driven decision frameworks and novel statistical methods

通过数据驱动的决策框架和新颖的统计方法解决单细胞分析挑战

• 批准号: 10707308
• 负责人: Rhonda Bacher
• 金额: $ 37.21万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707308
• 关键词: Basic Science; Biological; Complex; Computer software; Data; Data Analyses; Disease; Experimental Designs; Genomics; Goals; Methods; Reproducibility; Research; Research Personnel; Scientist; Statistical Methods; Technology; Visualization; experimental study; improved; insight; novel; programs; single cell analysis; single-cell RNA sequencing; tool; web server

Resolving single-cell analysis challenges via data-driven decision frameworks and novel statistical methods Abstract: Despite the power of single-cell RNA-seq, the data presents a multitude of analytical challenges and researchers continue to struggle with data analysis. The long-term goals of this research program are to develop robust, efficient, and scalable statistical methods and tools that enable all scientists to obtain accurate biological inferences. Specifically, we propose to develop interactive data-driven decision-frameworks to guide researchers through analyses and make informed analytical decisions. We also propose developing methods that retain interpretability while accommodating complex experimental designs. All of our approaches will be developed as highly accessible statistical software with interactive visualization and analysis modules available via webservers. Our proposed methods will result in richer analyses and biological insights, as well as, improved reproducibility and reliability of scientific results.

通过数据驱动的决策框架和新的统计方法解决单细胞分析挑战 方法 摘要： 尽管单细胞rna-seq的力量强大，但这些数据带来了大量的分析挑战和 研究人员继续在数据分析方面苦苦挣扎。这项研究计划的长期目标是 开发强大、高效和可扩展的统计方法和工具，使所有科学家能够获得准确的 生物推论。具体地说，我们建议开发交互式数据驱动的决策框架来指导 研究人员通过分析并做出明智的分析决定。并提出了开发方法。 在适应复杂的实验设计的同时保持可解释性。我们所有的方法都将是 开发为高度可访问的统计软件，提供交互式可视化和分析模块 通过网络服务器。我们建议的方法将产生更丰富的分析和生物学见解，以及， 提高了科学结果的重复性和可靠性。