Broeckling, C. D., Minhas, Fayyaz ul Amir Afsar, Neumann, S., Ben-Hur, A. and Prenni, J. E. (2014) RAMClust : A novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Analytical Chemistry, 86 (14). pp. 6812-6817. doi:10.1021/ac501530d ISSN 0003-2700.

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Abstract

Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature–feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title:	Analytical Chemistry
Publisher:	American Chemical Society
ISSN:	0003-2700
Official Date:	13 June 2014
Dates:	Date Event 13 June 2014 Published 13 June 2014 Accepted
Volume:	86
Number:	14
Page Range:	pp. 6812-6817
DOI:	10.1021/ac501530d
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	5 November 2019

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