Bayesian Methods and Experimental Design for Molecular Biology Experiments

描述（由申请人提供）：本提案的目标是为使用分子生物学（组学）数据的生物信息学和系统生物学研究人员提供一套软件工具，以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共同问题是由于低复制而导致的低功率。当采用和使用特定平台的增加导致相关成本降低时，可以经济地缓解该问题，从而能够增加每次治疗分配的样本。然而，许多生物信息学实验仍然动力不足，因为研究人员使用降低成本的抵消来探索更复杂的问题。在设计实验时，将样本分配给治疗方案以及选择要测试的治疗方法是传统上唯一需要操作的变量。贝叶斯实验设计提供了一个框架，以找到最佳的设计出n个可能的设计受到效用函数，可以包括这样的项目，如时间和材料成本。 贝叶斯统计方法在生物信息学和系统生物学中获得了极大的青睐，因为它们为拟合和探索复杂模型提供了高度灵活的框架。贝叶斯模型还通过比传统p值更自然理解的后验概率为生物学家和医生等领域专家提供了易于解释的模型。虽然有一些基于贝叶斯模型的开放源码工具，但大多数工具最好应用于特定的研究数据分析问题或模型，而不是集成到一个完整的数据分析系统中。 我们建议研究和开发一个统计分析软件包S+OBAYES（用于S-PLUS和R），该软件包具有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的建模和模拟的通用工具。这些工具将提供为分子生物学实验寻找最佳选择和实验处理布局的功能，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，如差异基因，蛋白质和代谢产物丰度的检测验证软件。这项工作的好处将是一个商业质量的软件包与验证的统计方法和交互式可视化工具，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快基础科学、早期疾病检测以及药物发现和开发的发现。

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.