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

@article{citeulike:2801157, 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 article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.}, address = {Istituto Tecnologie Avanzate Biomediche (ITAB), Fondazione G. d'Annunzio, Roma, Italy.}, author = {Mantini, D. and Petrucci, F. and Del Boccio, P. and Pieragostino, D. and Di Nicola, M. and Lugaresi, A. and Federici, G. and Sacchetta, P. and Di Ilio, C. and Urbani, A. }, citeulike-article-id = {2801157}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, month = {January}, number = {1}, pages = {63--70}, posted-at = {2008-05-15 10:36:49}, priority = {2}, title = {Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18003646}, volume = {24}, year = {2008} }

TY - JOUR ID - citeulike:2801157 L3 - citeulike-article-id:2801157 TI - Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra. JF - Bioinformatics (Oxford, England) VL - 24 IS - 1 SP - 63 EP - 70 AD - Istituto Tecnologie Avanzate Biomediche (ITAB), Fondazione G. d'Annunzio, Roma, Italy. SN - 1460-2059 N2 - 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 article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/. AU - Mantini, D AU - Petrucci, F AU - Del Boccio, P AU - Pieragostino, D AU - Di Nicola, M AU - Lugaresi, A AU - Federici, G AU - Sacchetta, P AU - Di Ilio, C AU - Urbani, A PY - 2008/01/01/ UR - http://view.ncbi.nlm.nih.gov/pubmed/18003646 ER -