Peaks detection and alignment for mass spectrometry data
[Détection et alignement de pics en spectrométrie de masse]
Journal de la société française de statistique, Tome 151 (2010) no. 1, pp. 17-37.

Le but de cet article est de faire une revue des méthodes existantes pour l’analyse de données protéomiques issues de spectromètres de masse, et de présenter une nouvelle méthodologie pour l’extraction automatique de pics significatifs (bio-marqueurs). Pour les étapes de pré-traitement nécessaires pour des données issues de spectres MALDI-TOF ou SELDI-TOF, nous utilisons une approche purement nonparamétrique qui combine la transformée en ondelettes invariante par translation pour le débruitage et la régression quantile pénalisée à partir de splines pour la correction de la ligne de base. Nous présentons ensuite une technique d’alignement multi-échelle qui est basée sur l’identification des pics statistiquement significatifs dans un ensemble de spectres. Cette méthode permet de trouver les pics communs à un ensemble de spectres qui peuvent être associés aux protéines des individus. Ceux-ci peuvent servir de bio-marqueurs utiles pour des applications médicales, ou bien de vecteurs de caractéristiques pour une analyse statistique multi-dimensionnelle des individus. Des spectres MALDI-TOF obtenus à partir d’échantillons de sérum sont utilisés à travers tout l’article pour illustrer la méthodologie.

The goal of this paper is to review existing methods for protein mass spectrometry data analysis, and to present a new methodology for automatic extraction of significant peaks (biomarkers). For the pre-processing step required for data from MALDI-TOF or SELDI-TOF spectra, we use a purely nonparametric approach that combines stationary invariant wavelet transform for noise removal and penalized spline quantile regression for baseline correction. We further present a multi-scale spectra alignment technique that is based on identification of statistically significant peaks from a set of spectra. This method allows one to find common peaks in a set of spectra that can subsequently be mapped to individual proteins. This may serve as useful biomarkers in medical applications, or as individual features for further multidimensional statistical analysis. MALDI-TOF spectra obtained from serum samples are used throughout the paper to illustrate the methodology.

Keywords: nonparametric regression, wavelets, regression quantiles, landmark detection, curve alignment, biomarkers identification
Mot clés : regression nonparamétrique, ondelettes, régression quantile, détection de pic, alignement de courbes, identification de biomarqueurs 
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Antoniadis, Anestis; Bigot, Jérémie; Lambert-Lacroix, Sophie. Peaks detection and alignment for mass spectrometry data. Journal de la société française de statistique, Tome 151 (2010) no. 1, pp. 17-37. http://www.numdam.org/item/JSFS_2010__151_1_17_0/

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