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.

识别生物过程中受影响的途径和变化非常重要，因为它提供了对差异表达基因检测之外的生物学基础条件的见解。由于此类分析的重要性，迄今为止已经开发了 100 多种路径分析方法。然而，由于所有这些方法都偏向于经过充分研究的疾病，例如癌症疾病，因此通路分析方法的准确性受到严重影响，特别是在研究非癌症疾病和表型时。更重要的是，现有方法仅限于对单个队列或数据类型的分析，使得它们对生物异质性敏感，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一种分析管道来弥补这些差距，该分析管道将在一个计算框架中解决偏差校正和数据集成问题。这将有助于识别疾病的假定分子原因，以及识别潜在的治疗干预措施及其可能的副作用，从而对许多研究和公共卫生领域产生巨大影响。该项目还包括一项系统性的外展计划，涉及内华达州的主要本科院校 (PUI)，特别是为少数族裔和西班牙裔服务的机构：南内华达学院和内华达州立学院。这里介绍的应用程序将使学生能够进行令人兴奋的科学研究，而无需进行湿实验室实验。其他计划的外展活动包括为瓦肖县学区的 K-12 当地学校举办夏季研讨会。大多数路径注释数据库都有重要的局限性。其中一些限制与领域有关（例如，仅关注癌症），其他限制与所包含的数据类型有关（例如，仅表达数据），还有一些限制与选择描述现象的详细程度有关。此外，由于不切实际的假设和过度拟合，路径分析方法容易受到系统偏差的影响。另一个痛点是无法在同一分析中轻松包含多个队列和多种类型的组学数据。该项目将提供一个框架，允许研究人员保留他们首选的途径方法，同时纠正方法偏差并集成多种数据类型和数据集。该项目的目标将通过两个重点来实现：1）设计一种路径方法的偏差校正和共识分析方法，2）开发一种灵活整合多队列和多组学数据的新方法。该框架的设计使其可以与任何现有的路径分析方法结合应用，以纠正偏差、整合来自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 100 多个具有已知机制的数据集对所提出的方法进行系统评估计划。研究团队将提供支持多种模式生物体的多种广泛使用方法的实施方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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