An R package for the integrated analysis of metabolomics and spectral data
An R package for the integrated analysis of metabolomics and spectral data
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DOI：
10.1016/j.cmpb.2016.01.008
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发表时间：
2016-06-01
影响因子：
6.1
通讯作者：
Rocha, Miguel
中科院分区：
文献类型：
--
作者：
Costa, Christopher;Maraschin, Marcelo;Rocha, Miguel
最近，人们对代谢组学领域的兴趣越来越大，这体现在实验技术、可用数据和相关生物学应用的显著增长上。事实上，核磁共振、气相或液相色谱、质谱、红外和紫外可见光谱等技术提供了广泛的数据集，可以帮助生物和生物医学发现、生物技术和药物开发等任务。然而，与其他组学数据一样，代谢组学数据集的分析在方法学和开发适当的计算工具方面都面临着多重挑战。事实上，从现有的软件工具，没有解决现有的技术和数据分析任务的多样性，在这项工作中，我们提供了一个新的R包，命名为specmine，它提供了一套代谢组学数据分析的方法，包括不同格式的数据加载，预处理，代谢物识别，单变量和多变量数据分析，机器学习和特征选择。重要的是，实现的方法为来自不同实验技术的数据分析提供了足够的支持，将来自几个R软件包的大量功能集成在一个功能强大但易于使用的环境中。该软件包已经在CRAN中可用，并附有一个网站，用户可以在其中存款数据集，脚本和分析报告与社区共享，促进代谢组学数据分析管道的有效共享。(C)2016爱思唯尔爱尔兰有限公司版权所有。
Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as nuclear magnetic resonance, gas or liquid chromatography, mass spectrometry, infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks.In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, preprocessing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment.The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines. (C) 2016 Elsevier Ireland Ltd. All rights reserved.