MeTEor: an R Shiny app for exploring longitudinal metabolomics data.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae178

Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski

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引用次数: 0

Abstract

Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.

Results: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.

Availability and implementation: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.

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MeTEor：一个用于探索纵向代谢组学数据的R Shiny应用程序。

动机：在代谢组学研究中，纵向组学数据的可用性正在增加。随着时间的推移查看代谢组学数据可以详细了解生物过程，并促进对系统如何随时间反应的理解。然而，纵向代谢组学数据的分析在统计评估和可视化方面都面临着各种挑战。结果：为了使没有计算机科学和编程背景的研究人员能够很容易地对纵向数据进行探索性分析，我们提出了代谢物轨迹探索者（MeTEor）。MeTEor是一个R Shiny的应用程序，提供了一套全面的统计分析方法。为了证明MeTEor的能力，我们复制了之前发表的一项关于COVID-19患者的研究的代谢组学数据分析。可用性和实现：MeTEor可以作为R包和Docker镜像使用。可以在GitHub （https://github.com/scibiome/meteor）上找到设置应用程序的源代码和说明。Docker镜像可以在Docker Hub （https://hub.docker.com/r/gordomics/meteor）上获得。MeTEor已经在Microsoft Windows、Unix/Linux和macOS上进行了测试。

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

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量