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一种用于识别生物相互作用网络中活性模块的网络嵌入方法。

A network embedding approach to identify active modules in biological interaction networks.

机构信息

Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, I3S - UMR7271 - UNS CNRS, Les Algorithmes - bât. Euclide B, Sophia Antipolis, France

Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, I3S - UMR7271 - UNS CNRS, Les Algorithmes - bât. Euclide B, Sophia Antipolis, France.

出版信息

Life Sci Alliance. 2023 Jun 20;6(9). doi: 10.26508/lsa.202201550. Print 2023 Sep.

DOI:10.26508/lsa.202201550
PMID:37339804
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10282331/
Abstract

The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine.

摘要

从转录组实验中识别特定条件的基因集对于揭示与特定细胞反应相关的调控和信号机制非常重要。设计用于评估单个基因变化的差异表达分析统计方法，难以突出小变化基因的模块，这些基因的相互作用对于表征表型变化至关重要。为了识别这些高度信息丰富的基因模块，近年来提出了几种方法，但它们存在许多限制，使得它们对生物学家几乎没有用处。在这里，我们提出了一种有效的方法，用于识别这些作用于结合基因表达和相互作用数据的数据嵌入中的活性模块。在真实数据集上进行的应用表明，我们的方法可以识别出与传统方法未揭示的功能相对应的新的高感兴趣基因组。软件可在 https://github.com/claudepasquier/amine 获得。

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