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）：该提案的主要目的是开发自适应和可观的统计模型，以分析高通量生物医学研究的多元，功能和空间数据。这些研究提出了有关高维度以及由生成数据的过程的各个方面引起的结构性依赖性方面的计算，建模和推论挑战。我们的工作是由我们的生物医学合作者，基因组学，表观基因组学和转录组学进行的各种高吞吐量癌症相关研究的数据的动机，并将应用于各种高通量癌症相关的研究。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的统计方法和计算工具，用于对此类高通量数据进行统计和概率建模，并特别强调集成方法，以结合跨不同测定和临床数据内的信息，以回答重要的生物学问题。我们的长期目标是改善预防癌症，诊断和预后中的风险预测和治疗选择。我们将通过追求以下特定目的（1）为贝叶斯自适应通用功能线性混合模型开发新的方法来实现此应用的目标，从而允许标量响应与功能预测变量之间的本地和非线性关联结构（2）开发层次结构贝叶斯联合模型，以集成多个多个多个类型和功能性数据。 （3）开发用于空间索引高维功能数据的贝叶斯空间功能过程模型，用于需要使用协方差函数族的数据，需要更广泛的功能内部和功能之间的协方差结构。 （4）开发多元贝叶斯空间功能模型，用于多个空间索引功能数据的联合建模。 （5）为提出的方法开发Ecient，用户友好且自由使用的软件。

项目成果

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

• DOI: 10.1080/01621459.2014.934826
• 发表时间: 2015-06-01
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Gregory KB;Carroll RJ;Baladandayuthapani V;Lahiri SN
• 通讯作者: Lahiri SN

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

• DOI: 10.1007/s12561-016-9169-5
• 发表时间: 2017-06
• 期刊: Statistics in biosciences
• 影响因子: 1
• 作者: Kim S;Baladandayuthapani V;Lee JJ
• 通讯作者: Lee JJ

STATISTICAL TESTS FOR LARGE TREE-STRUCTURED DATA.

• DOI: 10.1080/01621459.2016.1240081
• 发表时间: 2017
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7

Bayesian Variable Selection in Linear Regression in One Pass for Large Data Sets.

大型数据集一次性线性回归中的贝叶斯变量选择。

• DOI: 10.1145/2629617
• 发表时间: 2014
• 期刊: ACM transactions on knowledge discovery from data
• 影响因子: 3.6
• 作者: Ordonez,Carlos;Garcia-Alvarado,Carlos;Baladandayuthapani,Veerabhadran
• 通讯作者: Baladandayuthapani,Veerabhadran

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。

• DOI: 10.1111/rssc.12053
• 发表时间: 2014
• 期刊: Journal of the Royal Statistical Society. Series C, Applied statistics
• 作者: Zhang,Lin;Baladandayuthapani,Veerabhadran;Mallick,BaniK;Manyam,GanirajuC;Thompson,PatriciaA;Bondy,MelissaL;Do,Kim-Anh
• 通讯作者: Do,Kim-Anh