Abstract
BackgroundLiquid chromatography-mass spectrometry is a popular technique for high-throughput protein, lipid, and metabolite comparative analysis. Such statistical comparison of millions of data points requires the generation of an inter-run correspondence. Though many techniques for generating this correspondence exist, few if any, address certain well-known run-to-run LC-MS behaviors such as elution order swaps, unbounded retention time swaps, missing data, and significant differences in abundance. Moreover, not all extant correspondence methods leverage the rich discriminating information offered by isotope envelope extraction informed by isotope trace extraction. To date, no attempt has been made to create a formal generalization of extant algorithms for these problems.ResultsBy enumerating extant objective functions for these problems, we elucidate discrepancies between known LC-MS data behavior and extant approaches. We propose novel objective functions that more closely model known LC-MS behavior.ConclusionsThrough instantiating the proposed objective functions in the form of novel algorithms, practitioners can more accurately capture the known behavior of isotope traces, isotopic envelopes, and replicate LC-MS data, ultimately providing for improved quantitative accuracy.
Highlights
Liquid chromatography-mass spectrometry is a popular technique for high-throughput protein, lipid, and metabolite comparative analysis
Each isotopic envelope trace is composed of a series of isotope traces, which are manifestations of the fact that each analyte is composed of chemically similar compounds that differ in the weight of certain isotopes
We review extant objective functions for isotope trace extraction, isotopic envelope extraction, and correspondence
Summary
We present a concise attempt to formalize LC-MS data clustering problems, describing the constructs of isotope traces and isotopic envelopes and their relational structure. We provide a review of current approaches to isotope trace extraction and LC-MS correspondence, and propose novel objective functions for both tasks that address shortcomings in current methods. Competing interests and declarations The authors declare that they have no competing interests. The publication costs for this article were funded by the University of Montana Office of Research and Sponsored Programs. Authors’ contributions RS, JTP and DV all contributed in writing this manuscript. Authors’ details 1Department of Computer Science, University of Montana, 59812 Missoula, USA. Authors’ details 1Department of Computer Science, University of Montana, 59812 Missoula, USA. 2Department of Chemistry, Brigham Young University, 84606 Provo, USA. 3Department of Computer Science, Brigham Young University, 84606 Provo, USA
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