A systems biology approach for pathway level analysis
A systems biology approach for pathway level analysis
复制标题
DOI：
10.1101/gr.6202607
复制
发表时间：
2007-10-01
期刊：
影响因子：
7
通讯作者：
Romero, Roberto
中科院分区：
文献类型：
--
作者：
Draghici, Sorin;Khatri, Purvesh;Romero, Roberto
基因组学数据分析的一个共同挑战是试图理解在各种信号通路上发生的所有复杂相互作用背景下的潜在现象。使用各种模型的统计方法被普遍用于确定给定实验中最相关的途径。在这里，我们表明，现有的途径分析方法未能考虑到重要的生物学方面，并可能在某些情况下提供不正确的结果。通过使用系统生物学方法，我们开发了一种影响分析，其中包括经典统计数据，但也考虑了其他关键因素，如每个基因表达变化的幅度，它们在给定途径中的类型和位置，它们的相互作用等。影响分析是对更深层次的统计分析的一种尝试，与现有技术相比，它采用了更多的途径特异性生物学。在几个说明性数据集上，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。该分析方法已作为基于Web的工具Pathway- Express实现，该工具作为在线工具（http://vortex）的一部分免费提供。cs。韦恩。edu)。
A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By 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. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway- specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web- based tool, Pathway- Express, freely available as part of the Onto- Tools (http:// vortex. cs. wayne. edu).