IonFlow: a galaxy tool for the analysis of ionomics data sets
File(s)
Author(s)
Iacovacci, J
Lin, W
Griffin, JL
Glen, RC
Type
Journal Article
Abstract
Introduction
Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools.
Objective
Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data.
Methods
IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis.
Results and Conclusion
The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.
Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools.
Objective
Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data.
Methods
IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis.
Results and Conclusion
The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.
Date Issued
2021-10-01
Date Acceptance
2021-09-13
Citation
Metabolomics, 2021, 17 (10), pp.1-12
ISSN
1573-3882
Publisher
Springer
Start Page
1
End Page
12
Journal / Book Title
Metabolomics
Volume
17
Issue
10
Copyright Statement
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000699015800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
Ionomics
Network biology
Galaxy platform
LEAF IONOME
Publication Status
Published
Article Number
ARTN 91
Date Publish Online
2021-09-25