LipidSigR: a R-based solution for integrated lipidomics data analysis and visualization.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2025-03-10 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf047
Chia-Hsin Liu, Pei-Chun Shen, Meng-Hsin Tsai, Hsiu-Cheng Liu, Wen-Jen Lin, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung, Wei-Chung Cheng
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引用次数: 0

Abstract

Motivation: Lipidomics is a rapidly expanding field focused on studying lipid species and classes within biological systems. As the field evolves, there is an increasing demand for user-friendly, open-source software tools capable of handling large and complex datasets while keeping pace with technological advancements. LipidSig, a widely used web-based platform, has been instrumental in data analysis and visualization of lipidomics. However, its limitations become evident when users want to build customized workflows. To address the limitation, we developed a companion R package, LipidSigR, based on the R code of the LipidSig web platform.

Results: LipidSigR offers greater flexibility, allowing researchers with basic R programming skills to modify and adapt workflows according to their needs. It has been rigorously tested following CRAN guidelines to ensure compatibility and reproducibility. In demonstrating its functionality, we analyze the case with commonly used experimental design, case versus control, in lipidomics studies. Researchers can follow the use case to explore the key capabilities and build customized lipidomics data analysis workflows using LipidSigR.

Availability and implementation: LipidSigR is freely available from https://lipidsig.bioinfomics.org/lipidsigr/index.html and https://github.com/BioinfOMICS/LipidSigR.

LipidSigR:基于r的集成脂质组学数据分析和可视化解决方案。
动机:脂质组学是一个快速发展的领域,专注于研究生物系统中的脂质种类和类别。随着该领域的发展,对用户友好的开源软件工具的需求越来越大,这些工具能够处理大型复杂的数据集,同时跟上技术进步的步伐。LipidSig是一个广泛使用的基于网络的平台,在脂质组学的数据分析和可视化方面发挥了重要作用。然而,当用户想要构建自定义工作流时,它的局限性就变得明显了。为了解决这个限制,我们基于LipidSig web平台的R代码开发了一个配套的R包LipidSigR。结果:LipidSigR提供了更大的灵活性,允许具有基本R编程技能的研究人员根据他们的需要修改和适应工作流程。它已经按照CRAN指南进行了严格的测试,以确保兼容性和可重复性。为了证明其功能,我们分析了脂质组学研究中常用的实验设计,病例与对照。研究人员可以遵循用例来探索关键功能,并使用LipidSigR构建定制的脂组学数据分析工作流程。可用性和实现:LipidSigR可以从https://lipidsig.bioinfomics.org/lipidsigr/index.html和https://github.com/BioinfOMICS/LipidSigR免费获得。
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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CiteScore
1.60
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