Integrative methods for high-dimensional genomics data

高维基因组数据的整合方法

• 批准号: 8685000
• 负责人: Veerabhadran Baladandayuthapani
• 金额: $ 45.11万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-23 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8685000
• 关键词: Bayesian Method; Bioinformatics; Biological; Biological Assay; Biology; Cancer Patient; Clinical; Clinical Data; Communication; Computational Biology; Computer Simulation; Computer software; Data; Data Analyses; Data Set; Dependence; Dependency; Development; Diagnosis; Documentation; Ensure; Family; Genetic; Genomics; Goals; Human Resources; Interdisciplinary Study; Joints; Laboratories; Letters; Link; Malignant Neoplasms; Methodology; Methods; Modeling; Multivariate Analysis; Patient Care; Prevention strategy; Principal Investigator; Process; Reproducibility; Research; Research Personnel; Risk; Scientist; Selection for Treatments; Statistical Methods; Statistical Models; Structure; Work; cancer prevention; computerized tools; epigenomics; experience; improved; indexing; novel; outcome forecast; response; software development; transcriptomics; treatment strategy; user-friendly

DESCRIPTION (provided by applicant): The primary objective of this proposal is to develop adaptive and exible statistical models for analyses of multivariate, functional and spatial data from high-throughput biomedical studies. These studies raise computational, modeling, and inferential challenges with respect to high-dimensionality as well as structured dependency induced by the various aspects of the processes generating the data. Our work is motivated by, and will be applied to, data from a variety of high- throughput cancer-related studies that were conducted by our biomedical collaborators, in genomics, epigenomics and transcriptomics; although our methods are generally applicable to other contexts. The short-term objective of this research is to develop novel statistical methods and computational tools for statistical and probabilistic modeling of such high-throughput data with particular emphasis on integrative methods to combine information within and across dierent assays as well as clinical data to answer important biological questions. Our long-term goal is to improve risk prediction and treatment selection in cancer prevention, diagnosis and prognosis. We will accomplish the objective of this application by pursuing the following ve specic aims (1) develop new methodology for Bayesian adaptive generalized functional linear mixed models, allowing for local and nonlinear association structures between scalar responses and functional predictors (2) develop hierarchical Bayesian joint models for integrating diverse types of multivariate and functional data. (3) develop Bayesian spatial-functional process models for spatially indexed high-dimensional functional data, methods for data requiring a broader class of within-function and between-function covariance structures using exible families of covariance functions. (4) develop multivariate Bayesian spatial-functional models for joint modeling of multiple spatially indexed functional data. (5) develop ecient, user-friendly and freely available software for the proposed methods.

描述（由申请人提供）：该提案的主要目标是开发适应性和灵活的统计模型，用于分析来自高通量生物医学研究的多变量、功能和空间数据。这些研究在高维性以及由生成数据的过程的各个方面引起的结构化依赖方面提出了计算、建模和推理挑战。我们的工作受到我们的生物医学合作者在基因组学、表观基因组学和转录组学领域进行的各种高通量癌症相关研究的数据的推动，并将应用于这些数据；尽管我们的方法通常适用于其他情况。这项研究的短期目标是开发新颖的统计方法和计算工具，用于此类高通量数据的统计和概率建模，特别强调综合方法，以结合不同分析内部和不同分析以及临床数据之间的信息，以回答重要的问题生物学问题。我们的长期目标是改善癌症预防、诊断和预后的风险预测和治疗选择。我们将通过追求以下五个具体目标来实现此应用程序的目标（1）开发贝叶斯自适应广义函数线性混合模型的新方法，允许标量响应和函数预测变量之间的局部和非线性关联结构（2）开发分层贝叶斯联合用于集成不同类型的多元和函数数据的模型。 (3) 为空间索引的高维函数数据开发贝叶斯空间函数过程模型，以及使用灵活的协方差函数族需要更广泛的函数内和函数间协方差结构的数据方法。 (4) 开发多元贝叶斯空间函数模型，用于多个空间索引函数数据的联合建模。 （5）为所提出的方法开发高效、用户友好且免费可用的软件。

项目成果

A Two-Sample Test for Equality of Means in High Dimension.

高维均值相等的双样本检验。

• 发表时间: 2015-06-01
• 影响因子: 3.7
• 作者: Gregory, Karl Bruce;Carroll, Raymond J;Baladandayuthapani, Veerabhadran;Lahiri, Soumendra N
• 通讯作者: Lahiri, Soumendra N

Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.

贝叶斯分层结构化变量选择方法在乳腺癌 MIP 研究中的应用。

• 发表时间: 2014-08
• 作者: Zhang, Lin;Baladandayuthapani, Veerabhadran;Mallick, Bani K;Manyam, Ganiraju C;Thompson, Patricia A;Bondy, Melissa L;Do, Kim
• 通讯作者: Do, Kim

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

面向预测的标记选择（PROMISE）：在高维回归中的应用。

• 发表时间: 2017-06
• 作者: Kim, Soyeon;Baladandayuthapani, Veerabhadran;Lee, J Jack
• 通讯作者: Lee, J Jack

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.

整合临床和多组学数据以进行人类癌症的预后评估。

• 发表时间: 2017
• 影响因子: 4.6
• 作者: Zhu, Bin;Song, Nan;Shen, Ronglai;Arora, Arshi;Machiela, Mitchell J;Song, Lei;Landi, Maria Teresa;Ghosh, Debashis;Chatterjee, Nilanjan;Baladandayuthapani, Veera;Zhao, Hongyu
• 通讯作者: Zhao, Hongyu

INTEGRATIVE BAYESIAN ANALYSIS OF NEUROIMAGING-GENETIC DATA THROUGH HIERARCHICAL DIMENSION REDUCTION.

通过层次降维对神经影像遗传数据进行综合贝叶斯分析。

• 发表时间: 2016-04
• 作者: Azadeh, S;Hobbs, B P;Ma, L;Nielsen, D A;Moeller, F G;Baladandayuthapani, V
• 通讯作者: Baladandayuthapani, V