Integrative statistical methods and tools for analysis of perturbation signatures

描述（由申请人提供）：通过对不同特征的综合分析来揭示不同生物扰动的影响之间基于机制的关联需要用于识别细胞状态的高维读数中的共性的统计方法。它还需要统计方法和计算算法来推断和比较扰动效应的条件依赖调节网络模型。 不同细胞类型中不同扰动的转录特征的产生和解释是目前资助的大规模生产扰动诱导的细胞特征（LINCS）中心（U54HG 006093）的焦点之一。另一个目前资助的中心（U54HG006097）将评估信号分子活性和化合物生化活性的特征。结合起来，这些不同的细胞状态的读数可以用于有效地引发条件依赖性调节网络（CORN）模型goveming细胞对扰动的反应。我们建议开发，测试，验证基准和实施一个通用的统计框架，以评估不同类型的扰动签名的一致性。我们的初步结果表明，在统计能力方面的惊人改进，目前可用的方法在确定一致的转录签名。他们还证明了实用性和强大的改进，统计能力，以确定共同的生物学途径，通过综合分析的签名，多个扰动和多种类型的细胞读数，而不是使用孤立的分析不同的签名。 基于这些结果，我们还提出了综合的统计方法，机械地解释扰动签名的背景下，已知的途径，并构建从头机械网络模型。最后，我们提出了一种策略，使用新开发的方法和LINCS扰动签名作为一种新的资源，用于解释疾病相关的基因组学数据。所有新的方法和算法将部署在现有的在线和离线计算平台集成数据，计算工具和功能知识库。 公共卫生相关性：我们提出了发展的统计方法和计算工具，推断机械网络模型的综合分析不同的扰动签名。这些方法和基础设施将开辟重要的新途径，通过将其与扰动签名和元签名进行比较，来解释疾病相关基因组学实验的结果。由此产生的基础设施将消除LINCS扰动签名和相关网络模型的有意义重复使用的方法和基础设施障碍，使世界各地的科学家能够作为资源来深入了解人类疾病的基因组条件。

DESCRIPTION (provided by applicant): Revealing mechanism-based associations among effects of disparate biological perturbations by integrative analysis of diverse signatures requires statistical methods for identifying commonalities in high dimensional readouts of cellular states. It also requires statistical methods and computational algorithms for inferring and comparing condition dependent regulatory network models of perturbation effects. Generation and interpretation of transcriptional signature of diverse perturbations in diverse cell types is the focus one of the currently funded Large Scale Production of Perturbagen-induced Cellular Signatures (LINCS) centers (U54HG006093). The other currently funded center (U54HG006097) will assess signatures of signaling molecule activities and compound biochemical activity. Jointly, these diverse readouts of cellular states can be used to effectively elicit condition dependent regulatory network (CORN) models goveming the cellular response to perturbations. We propose to develop, test, validate benchmark and implement a general statistical framework for assessing concordance in different types of perturbation signatures. Our preliminary results demonstrate stunning improvements in statistical power over currently available methods in identifying concordant transcriptional signatures. They also demonstrate the utility and strong improvements in statistical power for identifying common biological pathways by integrated analysis of signatures of multiple perturbations and multiple types of cellular readouts as opposed to using isolated analyses of different signatures. Based on these results we also propose integrative statistical methods for mechanistically explaining perturbation signatures in the context of known pathways and for constructing de-novo mechanistic network models. Finally, we propose a strategy for using newly developed methods and LINCS perturbation signatures as a novel resource for interpreting disease-related genomics data. All new methods and algorithms will be deployed within existing on- and off-line computational platforms integrating data, computational tools and the functional knowledge base. PUBLIC HEALTH RELEVANCE: We propose the development of statistical methods and computational tools for inferring mechanistic network models by integrative analysis of diverse perturbation signatures. These methods and infrastructure will open important new avenues for interpreting results from disease-related genomics experiments by comparing them to perturbation signatures and meta-signatures. The resulting infrastructure will remove methodological and infrastructural barriers for meaningful re-use of LINCS perturbation signatures and related network models, enabling scientists throughout the world to use as resource to gain insight into the genomic conditions underlying human disease.