Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data.
Proteomics
; 10(14): 2564-72, 2010 Jul.
Article
em En
| MEDLINE
| ID: mdl-20432481
ABSTRACT
The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI-TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre-processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espectrometria de Massas
/
Análise de Componente Principal
/
Bases de Dados de Proteínas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Proteomics
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
França