A Novel Method for Signaling Pathway Analysis

描述(由申请人提供)：基因组数据分析中的一个常见挑战是试图理解在各种调控途径上所有复杂相互作用的背景下的潜在现象。目前，统计方法被普遍用于确定给定实验中最相关的路径。这种方法只考虑了每条途径上存在的一组基因，而完全忽略了其他重要的生物因素。在这里，我们表明，尽管它被普遍采用，并且与所使用的特定模型无关，但这种统计分析并不令人满意，而且经常会提供不正确的结果。利用系统生物学的方法，我们发展了一种影响分析，包括经典统计，但也考虑了其他关键因素，如每个基因表达变化的大小，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，经典分析既产生假阳性，也产生假阴性，而影响分析提供了具有生物学意义的结果。在这个第一阶段的应用中，我们建议开发一个原型，它将展示基于这种新方法的商业软件分析包的可行性。我们的团队有非常好的记录，这体现在：对我们以前的出版物的大量引用，我们以前开发的软件的大量用户基础(来自所有五大洲的5000多名科学家)，以及非常强烈的支持信。1公共卫生相关性：经典的统计学方法普遍用于确定特定实验中最相关的生物途径，它们只考虑每条途径上双(R)表达基因的数量，完全忽略了其他重要的生物因素。然而，尽管普遍采用了这些统计方法，但这些方法并不令人满意，而且经常会提供不正确的结果。我们提出了一种新的信号通路分析方法，它不仅包含经典的统计数据，而且还考虑了其他关键因素，如每个基因表达变化的大小、它们在给定通路中的类型和位置、它们之间的相互作用等。

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.