Collaborative Research: Integrating multi-dimensional omics data for quantifying disease heterogeneity

协作研究：整合多维组学数据以量化疾病异质性

• 批准号: 1916199
• 负责人: Jian Huang
• 金额: $ 12.03万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916199&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; multi; dimensional

Many complex diseases such as cancer demonstrate significant across-patient heterogeneity. For a better understanding of disease biology and optimally selecting treatment strategies, it is important to properly model disease heterogeneity. This project will develop a novel framework for modeling disease heterogeneity through the effective integration of information from multiple types of highly complex omics measurements. The proposed analysis framework and approaches will have significant broader impact. Applications of the methods will lead to more accurate identification of heterogeneous patient groups as well as their omics characteristics, which will facilitate the identification/definition of disease subtypes, treatment selection, and clinical decision-making. Data on skin and lung cancer will be analyzed leading to heterogeneity models that will be valuable to basic science researchers and clinicians. The project also involves education and training of graduate students at Yale University and the University of Iowa.High-dimensional omics data have been shown to be highly effective for heterogeneity analysis. Taking advantage of recent developments in multi-dimensional profiling under which data are collected on multiple types of omics measurements, the investigators will systematically develop novel integrated analysis strategies and approaches. Specifically, three sets of methods will be developed under the novel PFR (penalized fused regression) framework. Model averaging will be further developed to facilitate computation and provide additional insights into the proposed approaches. Extensive and rigorous methodological, computational, and theoretical investigations will be conducted. This project will make fundamental contributions to high-dimensional statistics and disease heterogeneity analysis.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.

许多复杂的疾病，如癌症，在患者之间表现出显著的异质性。为了更好地了解疾病生物学和选择最佳治疗策略，正确建模疾病异质性是很重要的。该项目将通过有效整合来自多种高度复杂组学测量的信息，为疾病异质性建模开发一个新的框架。拟议的分析框架和方法将产生重大而广泛的影响。这些方法的应用将导致更准确地识别异质患者群体及其组学特征，这将有助于疾病亚型的识别/定义，治疗选择和临床决策。将对皮肤癌和肺癌的数据进行分析，得出对基础科学研究人员和临床医生有价值的异质性模型。该项目还涉及耶鲁大学和爱荷华大学研究生的教育和培训。高维组学数据已被证明是非常有效的异质性分析。利用多维分析的最新发展，在多维分析下收集多种类型的组学测量数据，研究人员将系统地开发新的综合分析策略和方法。具体而言，将在新的PFR（惩罚融合回归）框架下开发三套方法。模型平均将进一步发展，以方便计算，并为所建议的方法提供更多的见解。将进行广泛而严格的方法、计算和理论研究。本项目将对高维统计和疾病异质性分析做出基础性贡献。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors

• DOI: 10.1016/j.jeconom.2021.05.006
• 发表时间: 2021-06
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Dongxiao Han;Jian Huang;Yuanyuan Lin;Guohao Shen

Projection‐based and cross‐validated estimation in high‐dimensional Cox model

• DOI: 10.1111/sjos.12515
• 发表时间: 2021-03
• 期刊: Scandinavian Journal of Statistics
• 影响因子: 1
• 作者: Haixiang Zhang;Jian Huang;Liuquan Sun

Model pursuit and variable selection in the additive accelerated failure time model

• DOI: 10.1007/s00362-020-01205-0
• 发表时间: 2020-10
• 期刊: Statistical Papers
• 影响因子: 1.3
• 作者: Li Liu;Hao Wang;Yanyan Liu;Jian Huang