Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach
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Detecting and aligning peaks in mass spectrometry data with applications to MALDI
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Gaussian mixture decomposition in the analysis of MALDI-TOF spectra
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Effective peak alignment for mass spectrometry data analysis using two-phase clustering approach
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Motivation: Early cancer detection has always been a major research focus in solid tumor oncology. Early tumor detection can theoretically result in lower stage tumors, more treatable diseases and ultimately higher cure rates with less treatment-related morbidities. Protein mass spectrometry is a potentially powerful tool for early cancer detection. We propose a novel method for sample classification from protein mass spectrometry data. When applied to spectra from both diseased and healthy patients, the 'peak probability contrast' technique provides a list of all common peaks among the spectra, their statistical significance and their relative importance in discriminating between the two groups. We illustrate the method on matrix-assisted laser desorption and ionization mass spectrometry data from a study of ovarian cancers. Results: Compared to other statistical approaches for class prediction, the peak probability contrast method performs as well or better than several methods that require the full spectra, rather than just labelled peaks. It is also much more interpretable biologically. The peak probability contrast method is a potentially useful tool for sample classification from protein mass spectrometry data. Supplementary Information: http://www.stat.stanford.edu/~tibs/ppc