ABI Innovation: EAGER: Towards an optimal experimental design framework with Omics data

该项目的目标是创建新的方法，帮助研究人员收集和整合现有的数据集，以便更好地为未来的实验设计提供信息。理想情况下，新的实验包括一些复制，填补空白，产生新的知识，但当现有的数据集位于不同的位置或以非常不同的方式组织时，这种平衡很难实现。 因此，为了实现这一目标，提出了两个目标：1）开发从公共实验中创建有凝聚力的生物数据集的方法，以便它们适合于训练计算模型; 2）开发指示应该使用什么样的实验条件来收集新数据集的方法，以便最有可能产生关于研究生物体生物学特性的重要信息，如结构和行为。实现这一目标将使我们能够更有效、更经济地了解生物体的重要生命规则，通过专注于给我们带来最大价值的实验来花费资金。这个探索性项目将专注于全基因组转录谱分析方法（例如微阵列，RNA-Seq）产生的数据，为以后的扩展建立计算基础。首先，将评估创建综合纲要的最佳数据处理技术，以选择构建机器学习方法训练数据集的最佳方法。其次，数据驱动的计算模型将在数据汇编上进行训练，并评估其在描述和微生物行为方面的成功。第三，给定标准化的纲要（在转录组学数据空间中），将原型化最优实验设计方法，以推荐要执行的最佳实验组，以产生拟合和测试生物模型所需的完整数据组。将使用合成数据对实验设计方法进行基准测试，然后通过探索设计推荐的抗生素和防腐剂组合（共10种）对微生物行为的影响进行评价。这将与目前使用的方法设计的实验结果进行比较。成功指标将侧重于实验空间中所需信息的收集速度，以及每次实验完成后模型中的不确定性水平。

The goal of this project is to create novel methods that help researchers gather and integrate existing data sets in order to better inform the design of future experiments. Ideally new experiments include some replication, fill in gaps and produce new knowledge, but this balance is hard to achieve when existing data sets are in different locations or organized in very different ways. Thus to achieve the goal, two aims are proposed: 1) develop methods for creating cohesive biological datasets from public experiments such that they are suitable for training computational models; 2) develop methods that indicate what experimental conditions should be used to collect new datasets so that are the most likely to yield important information about the study organism's biological properties, like structure and behavior. Achieving this goal will enable an understanding of important rules of life for organisms more efficiently and economically, by focusing on the experiments that give us the most value for the funds spent.This exploratory project will focus on data arising from genome-wide transcriptional profiling methods (e.g. microarrays, RNA-Seq), building a computational foundation for later expansion. First, optimal data processing techniques for creating integrated compendia will be assessed, in order to select the best method for building training datasets for machine learning methods. Second, data-driven computational models will be trained on the data compendia and evaluated for success in describing and microbial behavior. Third, given the normalized compendia (in the transcriptomics data space) an optimal experimental design methodology will be prototyped, to recommend the best set of experiments to perform to yield the complete set of data needed to fit and test the biological model. The experimental design methodology will be benchmarked using synthetic data, and then evaluated by exploring the effect of design- recommended combinations of antibiotics and antiseptics (10 in all) on microbial behavior. This will be compared to the outcomes of experiments designed by methods currently used. Success metrics will focus on how quickly the required information in the experimental space is gathered and what level of uncertainty in a model remains after each experiment is completed.