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Bioinformatics Advance Access published online on November 14, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm533
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra

Dante Mantini 1,2, Francesca Petrucci 3,4, Piero Del Boccio 3,4,5, Damiana Pieragostino 3,4,5, Marta Di Nicola 3,4, Alessandra Lugaresi 3,6, Giorgio Federici 7,8, Paolo Sacchetta 3,4, Carmine Di Ilio 3,4 and Andrea Urbani 3,4,5,*

1Istituto Tecnologie Avanzate Biomediche (ITAB), Fondazione "G. d'Annunzio", Chieti-Pescara, Italy
2Dipartimento di Scienze Cliniche e Bioimmagini, Università "G. d'Annunzio", Chieti-Pescara, Italy
3Centro Studi sull'Invecchiamento (Ce.S.I.), Fondazione "G.d'Annunzio", Chieti-Pescara, Italy
4Dipartimento di Scienze Biomediche, Università "G. d'Annunzio", Chieti-Pescara, Italy
5Centro Europeo Ricerca sul Cervello (CERC), IRCCS-Fondazione S. Lucia, Roma, Italy
6Dipartimento di Oncologia e Neuroscienze, Università "G. d'Annunzio", Chieti-Pescara, Italy
7Dipartimento di Medicina Interna, Università di Roma "Tor Vergata", Roma, Italy
8Ospedale Pediatrico Bambino Gesù – IRCCS, Roma, Italy

*To whom correspondence should be addressed. Prof. Andrea Urbani, E-mail: a.urbani{at}unich.it


  Abstract

Motivation: Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra.

Results: The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology.

Availability: The MATLAB implementation of the method described in the paper and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.

Associate Editor: Prof. Anna Tramontano

Received on July 20, 2007; revised on October 16, 2007; accepted on October 16, 2007

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