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RAMClust : A novel feature clustering method enables spectral-matching-based annotation for metabolomics data
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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
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Official URL: http://dx.doi.org/10.1021/ac501530d
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 > Computer Science | ||||||
| Journal or Publication Title: | Analytical Chemistry | ||||||
| Publisher: | American Chemical Society | ||||||
| ISSN: | 0003-2700 | ||||||
| Official Date: | 13 June 2014 | ||||||
| Dates: |
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| Date of first compliant deposit: | 5 November 2019 | ||||||
| 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 |
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