Application of Non-negative Matrix Factorization to LC/MS data

Abstract : Liquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, non-negative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually contaminated with non-Gaussian noise and the intensities vary on several orders of magnitude. In this article, we show the feasibility of the NMF approach on these data. We also propose an adaptation of one of the algorithms aiming at specifically dealing with LC/MS data. We finally perform experiments and compare standard NMF algorithms on both simulated data and an annotated LC/MS dataset. This lets us evaluate the influence of the noise model and the data model on the recovery of the sources.
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Soumis le : mardi 24 mars 2015 - 15:25:42
Dernière modification le : jeudi 7 mars 2019 - 15:34:12
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Jérémy Rapin, Antoine Souloumiac, Jérôme Bobin, Anthony Larue, Chistophe Junot, et al.. Application of Non-negative Matrix Factorization to LC/MS data. Signal Processing: Image Communication, Elsevier, 2016, 123, pp.75-83. 〈10.1016/j.sigpro.2015.12.014〉. 〈hal-01099079〉



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