Integrative statistical methods and tools for analysis of perturbation signatures

揭示不同生物扰动效应之间基于机制的关联， 不同特征的综合分析需要统计方法来识别高维特征的共性。 细胞状态的读数。它还需要统计方法和计算算法， 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本领域的研究重点。 专注于目前资助的大规模生产微扰诱导的细胞特征之一 （LINCS）中心（U54HG006093）。另一个由currentiy资助的中心（U54HG006097）将评估签名 信号分子活性和化合物生化活性。结合起来，这些不同的细胞读数 状态可以用来有效地引出条件依赖调节网络（CORN）模型， 细胞对扰动的反应我们建议开发、测试、验证、基准测试和实施一个 评估不同类型扰动特征一致性的一般统计框架。我们 初步结果表明，与现有方法相比，统计能力有了惊人的提高 识别一致的转录特征。他们还展示了实用性和强大的 通过综合分析提高确定共同生物途径的统计能力 多个扰动和多种类型的细胞读数的特征， 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， 已知途径背景下的扰动特征并用于构建从头机械网络 模型最后，我们提出了一种使用新开发的方法和LINCS扰动的策略 签名作为解释疾病相关基因组学数据的新资源。所有新方法和 算法将被部署在现有的在线和离线计算平台内整合数据， 计算工具和功能知识库。

Revealing mechanism-based associations among effects of disparate biological perturbations by integrative analysis of diverse signatures requires statistical methods for identifying commonalities in highdimensional 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 cunrentiy 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 pertrubation signatures as a novel resource for intepreting 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.