Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.
Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile.
BMC Bioinformatics. 2018 Feb 20;19(1):56. doi: 10.1186/s12859-018-2053-1.
The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.
In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology.
The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.
模块化基因共表达网络分析是一种常用的方法,常用于发现基因的系统功能。此外,这些研究为发现不同疾病和病症的临床相关分子途径提供了基础。
本文提出了一个快速易用的 Bioconductor 包 CEMiTool,它统一了共表达模块的发现和分析。使用相同的真实数据集,我们证明 CEMiTool 优于现有工具,并提供了独特的结果,以用户友好的 html 报告和高质量的图形呈现。在其功能中,我们的工具评估模块是否包含特定途径过度表达的基因,或者特定样本组中改变的基因,以及它将转录组数据与互作组信息集成,识别每个网络上的潜在枢纽。我们成功地将 CEMiTool 应用于超过 1000 个转录组数据集,以及新的感染利什曼原虫的患者的 RNA-seq 数据集,揭示了该疾病病理生理学的新见解。
CEMiTool R 包为用户提供了一种简单易用的方法,可自动执行基因共表达网络分析,使用额外的下游分析获得发现的基因模块的关键信息,并通过高质量的交互式报告检索可发表的结果。