A Novel Method for Signaling Pathway Analysis

描述（由申请人提供）：基因组学数据分析中的一个共同挑战是试图理解各种调控途径上所有复杂相互作用背景下的潜在现象。目前，普遍采用统计方法来确定给定实验中最相关的途径。这种方法只考虑了每条通路上存在的一组基因，而完全忽略了其他重要的生物学因素。在这里，我们表明，尽管它被普遍采用，并且独立于所使用的特定模型，这种统计分析是不令人满意的，并且经常可以提供不正确的结果。使用系统生物学方法，我们开发了包括经典统计在内的影响分析，但也考虑了其他关键因素，如每个基因表达变化的幅度，它们在给定途径中的类型和位置，它们的相互作用等。我们的初步工作表明，经典分析产生假阳性和假阴性，而影响分析提供了生物学上有意义的结果。在这个第一阶段的应用程序中，我们建议开发一个原型，该原型将演示基于这种新方法的商业软件分析包的可行性。我们的团队有一个非常强大的记录，证明了这一点：大量引用我们以前的出版物，我们以前开发的软件的大量用户基础（来自五大洲的5000多名科学家），以及非常强有力的支持信。1公共卫生相关性：经典的统计方法被普遍用于在给定的实验中确定最相关的生物学途径，只考虑每条途径上的di(R)次表达基因的数量，而完全忽略了其他重要的生物学因素。然而，尽管它被普遍采用，这些统计方法是不令人满意的，往往可以提供不正确的结果。我们提出了一种新的信号通路分析方法，包括经典的统计数据，但也考虑了其他关键因素，如每个基因表达变化的幅度，它们在给定通路中的类型和位置，它们的相互作用等。

DESCRIPTION (provided by applicant): A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions on various regulatory pathways. Currently, a statistical approach is universally used to identify the most relevant pathways in a given experiment. This approach only considers the set of genes present on each pathway and completely ignores other important biological factors. Here we show that in spite of its general adoption, and independently of the particular model used, this statistical analysis is unsatisfactory, and can often provide incorrect results. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. Our preliminary work shows that the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. In this Phase I application, we are proposing to develop a prototype that would demonstrate the feasibility of a commercial software analysis package based on this novel approach. Our team has a very strong track record as demonstrated by: a large number of citations to our previous publications, a large user-base for our previously developed software (over 5,000 scientists from all 5 continents), and very strong letters of support. 1 PUBLIC HEALTH RELEVANCE: The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignores other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.