A statistical method for measuring activation of gene regulatory networks.

IF 0.8 4区数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-13 DOI:10.1515/sagmb-2016-0059

Gustavo H Esteves, Luiz F L Reis

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引用次数: 1

Abstract

Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks.

Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material.

Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

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一种测量基因调控网络激活的统计方法。

动机:基因表达数据分析对现代分子生物学非常重要，因为我们有能力测量成千上万个基因的表达谱，并使植根于系统生物学的研究成为可能。在这项工作中，我们提出了一个简单的统计模型来测量基因调控网络的激活，而不是传统的基因共表达网络。结果:我们提出了一个检验基因调控网络激活假设的统计程序的数学结构。检验统计量的真实概率分布通过基于排列的研究来评估。为了说明所提出的方法的功能，我们还提出了一个简单的例子，基于一个小的假设网络和两个KEGG网络的激活测量，两者都基于从胃和食管样本收集的基因表达数据。还对两个KEGG网络进行了分析，以便通过NCBI-GEO提供一个公共数据库，作为补充材料。可用性:该方法是在一个R包中实现的，可以在BioConductor项目网站上以maigesPack的名称获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY

自引率

11.10%

发文量

期刊介绍： Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.