Gene set analysis exploiting the topology of a pathway.
Gene set analysis exploiting the topology of a pathway.
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DOI：
10.1186/1752-0509-4-121
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发表时间：
2010-09-01
影响因子：
--
通讯作者：
Romualdi C
中科院分区：
文献类型：
--
作者：
Massa MS;Chiogna M;Romualdi C
近年来，微阵列数据分析的一个重要方向是对基因集的研究。一个基因集是由在某种程度上功能上相关的基因定义的。例如，出现在已知生物学途径中的基因自然地定义基因集。基因集通常是从先验生物学知识中识别的。如今，许多生物信息学资源存储了这类知识（例如，参见京都基因和基因组百科全书等）。虽然路径图携带的基因之间的相关性结构的重要信息，不应该被忽视，目前可用的多变量基因集分析方法没有充分利用它。这样的分析，基于图形模型，明确纳入了依赖结构的基因突出的拓扑结构的途径。该分析旨在用于在不同实验条件下对途径变化进行全面监测。事实上，在不同的情况下，不仅基因在通路中的表达，而且它们之间的关系强度也可能发生变化。从该建议产生的方法允许测试的变化的强度的链接，并正确地考虑到异方差在通常的测试差异表达。使用图形模型可以更深入地了解可以单独测试和比较的途径的组成部分。通过这种方式，可以测试该途径的单个组分，并仅突出那些参与其去调节的组分。
Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it. We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression. The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation.
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DOI：
10.1111/1467-9868.00346
发表时间：
2002-01-01
影响因子：
5.8
作者：
Storey, JD
通讯作者：
Storey, JD
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
3
作者：
Ackermann, Marit;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
影响因子：
5.8
作者：
Goeman, Jelle J.;Mansmann, Ulrich
通讯作者：
Mansmann, Ulrich
影响因子：
5.8
作者：
Tsai, Chen-An;Chen, James J.
通讯作者：
Chen, James J.