Application of non-negative matrix factorization to LC/MS data
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 their processing. These data a...
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| Veröffentlicht in: | Signal processing 2016-06, Vol.123, p.75-83 |
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| container_title | Signal processing |
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| creator | Rapin, Jérémy Souloumiac, Antoine Bobin, Jérôme Larue, Anthony Junot, Chistophe Ouethrani, Minale Starck, Jean-Luc |
| description | 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 their processing. These data are however 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 paper, we propose an adaptation of a state-of-the-art NMF algorithms so as to specifically be able to deal with LC/MS data, by using a non-stationary noise model and a stochastic term. We finally perform experiments and compare standard NMF algorithms on both simulated data and an annotated LC/MS dataset. The results of these experiments highlight the significant improvement obtained by our adaptation over other NMF algorithms. |
| doi_str_mv | 10.1016/j.sigpro.2015.12.014 |
| format | Article |
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| identifier | ISSN: 0165-1684 |
| ispartof | Signal processing, 2016-06, Vol.123, p.75-83 |
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| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Adaptation Algorithms BSS Data Analysis, Statistics and Probability Engineering Sciences Factorization LC/MS Liquids Machine Learning Mass spectroscopy Mathematics Multiplicative noise NMF Noise Numerical Analysis Physics Signal and Image processing Sparsity Spectrometry Statistics |
| title | Application of non-negative matrix factorization to LC/MS data |
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