title: RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.
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.
authors.affiliation: Proteomics and Metabolomics Facility, Colorado State University , Fort Collins, Colorado 80523, United States.
authors.forename: C D
authors.initials: CD
authors.lastname: Broeckling
authors.forename: F A
authors.initials: FA
authors.lastname: Afsar
authors.forename: S
authors.initials: S
authors.lastname: Neumann
authors.forename: A
authors.initials: A
authors.lastname: Ben-Hur
authors.forename: J E
authors.initials: JE
authors.lastname: Prenni
mesh_terms: D000818:Animals; D002555:Cerebrospinal Fluid / Q000378:metabolism; D016000:Cluster Analysis; D016208:Databases, Factual; D006736:Horses; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012984:Software; D053719:Tandem Mass Spectrometry