Integrated analysis of gene expression by association rules discovery
Integrated analysis of gene expression by association rules discovery
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DOI：
10.1186/1471-2105-7-54
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发表时间：
2006-02-07
影响因子：
3
通讯作者：
Pascual-Montano, A
中科院分区：
文献类型：
--
作者：
Carmona-Saez, P;Chagoyen, M;Pascual-Montano, A
背景：微阵列技术正在不同的实验条件下生成有关数千个基因甚至整个基因组表达水平的大量数据。为了提取生物学知识并充分理解此类数据集，必须将有关基因和基因产物的外部生物学信息纳入表达数据的分析中。然而，目前大多数分析微阵列数据的方法主要集中在对实验数据的分析，并将外部生物信息作为后处理过程。 结果：在本研究中，我们提出了一种基于关联规则发现数据挖掘技术的微阵列数据综合分析方法。该方法集成了基因注释和表达数据，以根据共现模式发现两个数据源之间的内在关联。我们将所提出的方法应用于基因表达数据集的分析，其中基因用代谢途径、转录调节因子和基因本体类别进行注释。自动提取的关联揭示了这些基因属性和表达模式之间的显着关系，其中许多关系得到了最近报道的工作的明确支持。结论：外部生物信息和基因表达数据的整合可以提供有关与基因表达程序相关的生物过程的见解。在本文中，我们表明所提出的方法能够将多个基因注释和表达数据整合到同一分析框架中，并提取异质数据源之间有意义的关联。该方法的实现包含在 Engene 软件包中。
Background: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.Results: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.Conclusion: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.