FET: III: Small: Innovative Approaches for Bias Correction and Systems-level Analysis in Integrated Multi-omics Data

FET：III：小型：集成多组学数据中的偏差校正和系统级分析的创新方法

• 批准号: 2203236
• 负责人: Juli Petereit
• 金额: $ 60万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203236&HistoricalAwards=false
• 关键词: FET; III; Small; Innovative; Approaches

Identifying impacted pathways and changes in biological processes is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway-analysis methods have been developed thus far. However, as all these methods are biased toward well-studied conditions, such as cancer diseases, the accuracy of pathway-analysis methods is severely compromised, especially when investigating non-cancer diseases and phenotypes. More importantly, existing methods are limited to the analysis of a single cohort or data type, making them sensitive to biological heterogeneity and unable to analyze complex diseases that involve multiple molecular levels. This project aims to bridge these gaps by designing an analysis pipeline that will tackle bias correction and data integration in one computational framework. This will have a great impact in many research and public health areas by facilitating the identification of putative molecular causes of disease, as well as the identification of potential therapeutic interventions and their possible side effects. This project also includes a systematic outreach program involving Primarily Undergraduate Institutions (PUI) across Nevada, especially minority- and Hispanic-serving institutions: College of Southern Nevada and Nevada State College. The applications presented here will enable students to conduct exciting scientific research without the need to perform wet-lab experiments. Other planned outreach activities involve summer workshops for K-12 local schools from Washoe County School District. Most pathway-annotation databases have important limitations. Some of these limitations are related to the domain (e.g., focused on cancer alone), others to the types of data included (e.g., only expression data), and still others to the level of detail chosen to describe the phenomenon. Further, pathway-analysis methods are subject to systematic bias due to unrealistic assumptions and overfitting. Another pain point is the inability to easily include multiple cohorts and multiple types of -omics data in the same analysis. This project will provide a framework that allows researchers to retain their preferred pathway methods while correcting for method bias and integrating multiple data types and datasets. The goal of this project will be achieved by two thrusts: 1) design a methodology for bias correction and consensus analysis of pathway methods, and 2) develop a novel approach for the flexible integration of multi-cohort and multi-omics data. The framework will be designed so that it can be applied in conjunction with any existing pathway-analysis method to correct for bias, incorporate knowledge from different databases and integrate data of different types. The project also includes a systematic evaluation plan of the proposed methodologies using more than 100 datasets with known mechanisms. The research team will deliver an implementation that supports several widely used methods for many model organisms.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.

识别影响的途径和生物过程的变化很重要，因为它为生物学疾病提供了洞察力，而不是发现差异表达基因的基础。由于这种分析的重要性，到目前为止，已经开发了100多种途径分析方法。但是，由于所有这些方法都偏向良好的疾病，例如癌症疾病，因此途径 - 分析方法的准确性受到严重损害，尤其是在研究非癌症疾病和表型时。更重要的是，现有方法仅限于单个队列或数据类型的分析，使其对生物异质性敏感，并且无法分析涉及多个分子水平的复杂疾病。该项目旨在通过设计分析管道来弥合这些差距，该管道将在一个计算框架中处理偏差校正和数据集成。通过促进鉴定推定的疾病分子原因，以及鉴定潜在的治疗干预措施及其可能的副作用，这将在许多研究和公共卫生领域产生重大影响。该项目还包括一项系统的外展计划，涉及内华达州的主要本科机构（PUI），尤其是少数民族和西班牙裔服务机构：内华达州南部和内华达州立大学。此处介绍的应用程序将使学生无需进行湿lab实验而进行令人兴奋的科学研究。其他计划的外展活动涉及Washoe县学区的K-12当地学校的夏季研讨会。大多数通路通道数据库都有重要的局限性。这些局限性中的一些与域有关（例如，仅关注癌症），其他局限性与所包括的数据类型（例如，仅表达数据）以及其他用于描述现象的细节水平有关。此外，由于不切实际的假设和过度拟合，途径 - 分析方法受到系统的偏见。另一个痛苦点是在同一分析中无法轻松将多种同类和多种类型数据包含在内。该项目将提供一个框架，使研究人员可以在纠正方法偏差并集成多种数据类型和数据集的同时保留其首选途径方法。该项目的目标将通过两个推力来实现：1）设计一种偏见校正和共识分析的方法，以及2）开发一种新的方法，以灵活整合多核心和多摩学数据。将设计该框架，以便可以与任何现有的途径 - 分析方法一起应用，以纠正偏见，结合不同数据库的知识并整合不同类型的数据。该项目还包括使用具有已知机制的100多个数据集对拟议方法的系统评估计划。研究团队将提供一项支持许多模型生物的广泛使用方法的实施。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。

项目成果

DWEN: A novel method for accurate estimation of cell type compositions from bulk data samples

DWEN：一种从大量数据样本中准确估计细胞类型组成的新方法

• DOI: 10.1109/kse56063.2022.9953757
• 发表时间: 2022
• 期刊: 2022 14th International Conference on Knowledge and Systems Engineering (KSE
• 作者: Tran, Duc;Nguyen, Ha;Nguyen, Hung;Nguyen, Tin
• 通讯作者: Nguyen, Tin