Department of Immunotechnology, Lund University Biomedical Centre D13, SE-221 84 Lund, Sweden.
J Proteome Res. 2012 May 4;11(5):2955-67. doi: 10.1021/pr300038b. Epub 2012 Apr 21.
Functional analysis of quantitative expression data is becoming common practice within the proteomics and transcriptomics fields; however, a gold standard for this type of analysis has yet not emerged. To grasp the systemic changes in biological systems, efficient and robust methods are needed for data analysis following expression regulation experiments. We discuss several conceptual and practical challenges potentially hindering the emergence of such methods and present a novel method, called FEvER, that utilizes two enrichment models in parallel. We also present analysis of three disparate differential expression data sets using our method and compare our results to other established methods. With many useful features such as pathway hierarchy overview, we believe the FEvER method and its software implementation will provide a useful tool for peers in the field of proteomics. Furthermore, we show that the method is also applicable to other types of expression data.
定量表达数据分析在蛋白质组学和转录组学领域已经成为一种常见的做法；然而，这种类型的分析还没有一个黄金标准。为了掌握生物系统的系统变化，需要有效的和强大的方法来进行表达调控实验后的数据分析。我们讨论了一些可能阻碍这类方法出现的概念和实际挑战，并提出了一种新的方法，称为 FEvER，该方法同时利用了两个富集模型。我们还使用我们的方法对三个不同的差异表达数据集进行了分析，并将我们的结果与其他已建立的方法进行了比较。我们相信，FEver 方法及其软件实现将为蛋白质组学领域的同行提供一个有用的工具，因为它具有许多有用的功能，如途径层次结构概述。此外，我们还表明该方法也适用于其他类型的表达数据。
