Geometric interpretation of gene coexpression network analysis.
Geometric interpretation of gene coexpression network analysis.
复制标题
基因共表达网络分析的几何解释。
DOI：
10.1371/journal.pcbi.1000117
复制
发表时间：
2008-08-15
影响因子：
4.3
通讯作者：
Dong, Jun
中科院分区：
文献类型：
--
作者：
Horvath, Steve;Dong, Jun
网络理论与微阵列数据分析技术的结合催生了一个新的领域：基因共表达网络分析。虽然网络方法越来越多地用于生物学，但计算生物学家的网络词汇往往比社会网络理论家的词汇要有限得多。在这里，我们回顾并提出了几个潜在有用的网络概念。我们利用网络理论和微阵列数据分析领域之间的关系来阐明基因共表达网络中网络概念的含义和网络概念之间的关系。网络理论提供了大量直观的概念来描述基因之间的成对关系，这些关系可以用聚类树和热图来描述。相反，微阵列数据分析技术（奇异值分解，差异表达测试）也可以用来解决网络理论中的难题。我们描述了网络分析和微阵列数据分析技术之间存在密切关系的条件，并提供了一个粗略的字典，用于在两个领域之间进行翻译。使用角度的相关性的解释，我们提供了一个几何解释网络理论的概念，并得出意想不到的关系。我们使用模块表达数据的奇异值分解来表征近似可因子化的基因共表达网络，即，邻接矩阵的因素到节点的具体贡献。共表达网络的高层次和低层次视图使我们能够分别研究模块之间和模块基因之间的关系。我们表征共表达网络的枢纽基因是显着的微阵列样本性状，并表明，模块内连接的网络概念可以解释为模块成员的模糊测度。我们使用人类、小鼠和酵母微阵列基因表达数据来说明我们的结果。共表达网络方法与传统数据挖掘方法的结合，为系统生物学方法的应用和发展提供了指导。与自然语言一样，网络语言也在不断发展。虽然一些网络术语（概念）在基因共表达网络分析中被广泛使用，但仍需要开发其他术语以满足对描述基因转录本系统的不断增长的需求。有必要提供一个直观的几何解释网络的概念，并研究它们之间的关系。例如，我们表明，某些看似不同的网络概念原来是同义词的上下文中的共表达模块。我们展示了共表达网络语言如何影响我们对生物学的理解。例如，在重要的共表达模块中高度连接的枢纽基因往往很重要，以及一个模块中的枢纽基因在另一个不同的模块中不能成为枢纽基因，这些都有几何学上的原因。我们提供了一个简短的字典之间的翻译微阵列数据分析语言和网络理论语言，以促进这两个领域之间的沟通。我们描述了几个示例，说明这两个数据分析字段如何相互通知。
The merging of network theory and microarray data analysis techniques has spawned a new field: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods. Similar to natural languages, network language is ever evolving. While some network terms (concepts) are widely used in gene coexpression network analysis, others still need to be developed to meet the ever increasing demand for describing the system of gene transcripts. There is a need to provide an intuitive geometric explanation of network concepts and to study their relationships. For example, we show that certain seemingly disparate network concepts turn out to be synonyms in the context of coexpression modules. We show how coexpression network language affects our understanding of biology. For example, there are geometric reasons why highly connected hub genes in important coexpression modules tend to be important, and why hub genes in one module cannot be hubs in another distinct module. We provide a short dictionary for translating between microarray data analysis language and network theory language to facilitate communication between the two fields. We describe several examples that illustrate how the two data analysis fields can inform each other.
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DOI：
10.1073/pnas.150242097
发表时间：
2000-07-18
影响因子：
11.1
作者：
Holter, NS;Mitra, M;Fedoroff, NV
通讯作者：
Fedoroff, NV
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
64.8
作者：
Albert, R;Jeong, H;Barabási, AL
通讯作者：
Barabási, AL
影响因子：
4
作者：
Albert, R
通讯作者：
Albert, R