Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms
Progress is being made continuously in the quest for stable biomarkers linked to complex diseases. Mass spectrometers are one of the devices for tackling this problem. The data profiles they produce are noisy and unstable. In these profiles, biomarkers are detected as signal regions (peaks), where control and disease samples behave differently. Mass spectrometry (MS) data generally contains a limited number of samples described by a high number of features. In this work, we present a novel class of evolutionary algorithms, estimation of distribution algorithms (EDA), as an efficient peak selector in this MS domain. There is a tradeoff between the reliability of the detected biomarkers, and the low number of samples for analysis. For this reason, we introduce a consensus approach, built upon the classical EDA scheme, that improves stability and robustness of the final set of relevant peaks. An entire data workflow is designed to yield unbiased results. Four publicly available MS datasets (two MALDI-TOF and another two SELDI-TOF) are analyzed. The results are compared to the original works, and a new plot (peak frequential plot) for graphically inspecting the relevant peaks is introduced. A complete online supplementary page includes extended info and results, in addition to Matlab scripts and references.
Armañanzas, R., Saeys, Y., Inza, I., Garcia-Torres, M., Bielza, C., Van de Peer, Y., Larrañaga, P. (2011) Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE-ACM Trans. Comput. Biol. Bioinform. 8,760-74.
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