Bayesian Methods and Experimental Design for Molecular Biology Experiments

分子生物学实验的贝叶斯方法和实验设计

• 批准号: 7325828
• 负责人: Stella Wanjugu Karuri
• 金额: $ 10.4万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7325828
• 关键词: Address; Adoption; Algorithms; Animal Genetics; Arizona; Basic Science; Bayesian Analysis; Bayesian Method; Bioconductor; Bioinformatics; Biological Markers; Biological Sciences; Biometry; Biotechnology; Cations; Chromosome Mapping; Code; Communities; Complement; Complex; Computer software; Data; Data Analyses; Data Set; Department of Defense; Depth; Detection; Development; Disease; Educational process of instructing; Educational workshop; Employment; Ensure; Experimental Designs; Exposure to; Factor Analysis; Foundations; Funding; Future; Gene Expression; Gene Proteins; Genes; Genetic; Genomics; Goals; Government; Government Agencies; Health; Imagery; Industry; Information Systems; Institution; Iowa; Libraries; Linear Models; Machine Learning; Manuals; Manuscripts; Maps; Marketing; Mass Spectrum Analysis; Measures; Medical Informatics; Methodology; Methods; Microarray Analysis; Modeling; Molecular; Molecular Biology; Non-linear Models; Numbers; Pathway interactions; Phase; Physicians; Population Study; Principal Investigator; Probability; Property; Proteome; Proteomics; Proxy; Quantitative Trait Loci; Research; Research Personnel; Rice; Risk Factors; SNP genotyping; Sampling; Science; Scientist; Series; Services; Simulate; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Small Business Technology Transfer Research; Software Tools; Software Validation; Solutions; Speed; Standards of Weights and Measures; Statistical Methods; Statistical Models; Systems Biology; Techniques; Telecommunications; Testing; Time; Time Series Analysis; Training; Treatment Protocols; Universities; Validation; Washington; Wisconsin; Work; animal breeding; base; cost; design; drug discovery; experience; human subject; improved; interest; lecturer; models and simulation; open source; professor; programs; protein metabolite; research and development; research study; skills; software development; statistics; success; theories; tool; treatment effect

DESCRIPTION (provided by applicant): The goal of this proposal is to provide a suite of software tools for bioinformatics and systems biology researchers who are using molecular biology (Omics) data to identify the best experimental design and to analyze the resulting experimental data using Bayesian tools. A common problem for most bioinformatics experiments is low power due to low replication. This problem can be alleviated economically when an increase in adoption and use of a specific platform leads to a decrease in associated costs, thereby enabling an increase in samples allocated per treatment. Yet, many bioinformatics experiments remain underpowered as researchers use the offsets of decreased costs to explore more complex questions. When designing an experiment, the allocation of samples to treatment regimens, and the choice of treatments to test, are traditionally the only variables to manipulate. Bayesian experimental design provides a framework to find the optimal design out of n possible designs subject to a utility function that can include such items as time and material costs. Bayesian statistical methods have been gaining substantial favor in bioinformatics and systems biology as they provide a highly flexible framework for fitting and exploring complex models. Bayesian models also provides to domain experts such as biologists and physicians easily interpretable models through posterior probabilities which are more naturally understood than the traditional p-value. While a number of open source tools based on Bayesian models are available, most are applied best in the context of a specific research data analysis problem or model and are not integrated into a single, complete system for data analysis. We propose to research and develop a statistical analysis software package S+OBAYES (for S-PLUS and R) with generalized tools for Bayesian design of experiments, empirical and fully Bayesian analysis, and modeling and simulation using modern commercial software development practices. These tools will provide functionality for finding the optimal choice and layout of experimental treatments for molecular biology experiments and for fitting Bayesian linear and non-linear models to a variety of data types including time series. We propose to validate the software in molecular biology research problems such as the detection of differential gene, protein, and metabolite abundance. The benefits of this work will be a commercial-quality software package with validated statistical methodology and interactive visualization tools that will appeal to molecular biologists and systems biology investigators. The results of the proposed work will expedite discoveries in basic science, early disease detection, and drug discovery and development.

描述（由申请人提供）：该提案的目标是为生物信息学和系统生物学研究人员提供一套软件工具，这些研究人员使用分子生物学（组学）数据来确定最佳实验设计并使用贝叶斯工具分析所得实验数据。大多数生物信息学实验的一个常见问题是由于重复性低而导致功率低。当特定平台的采用和使用增加导致相关成本降低，从而增加每次处理分配的样本时，这个问题可以在经济上得到缓解。然而，许多生物信息学实验仍然动力不足，因为研究人员利用成本降低的抵消来探索更复杂的问题。在设计实验时，治疗方案的样本分配以及要测试的治疗方法的选择传统上是唯一需要操纵的变量。贝叶斯实验设计提供了一个框架，可以根据效用函数（包括时间和材料成本等项目）从 n 个可能的设计中找到最佳设计。 贝叶斯统计方法在生物信息学和系统生物学领域获得了极大的青睐，因为它们为拟合和探索复杂模型提供了高度灵活的框架。贝叶斯模型还为生物学家和医生等领域专家提供了通过后验概率轻松解释的模型，后验概率比传统的 p 值更容易理解。虽然有许多基于贝叶斯模型的开源工具可用，但大多数工具只能在特定研究数据分析问题或模型的背景下得到最佳应用，并且没有集成到单个完整的数据分析系统中。 我们建议研究和开发统计分析软件包 S+OBAYES（适用于 S-PLUS 和 R），其中包含用于贝叶斯实验设计、经验和完全贝叶斯分析以及使用现代商业软件开发实践进行建模和模拟的通用工具。这些工具将提供寻找分子生物学实验实验处理的最佳选择和布局以及将贝叶斯线性和非线性模型拟合到包括时间序列在内的各种数据类型的功能。我们建议在分子生物学研究问题（例如差异基因、蛋白质和代谢物丰度的检测）中验证该软件。这项工作的好处将是一个具有经过验证的统计方法和交互式可视化工具的商业质量软件包，这将吸引分子生物学家和系统生物学研究人员。拟议工作的结果将加速基础科学、早期疾病检测以及药物发现和开发的发现。