2009/07/20 by Theodore Alexandrov, Alexandrov, Theodore, Klaus Steinhorst +5
Mathematics · Physics and Astronomy · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Medical Physics (physics.med-ph) #Methodology (stat.ME) #physics.med-ph #stat.AP #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.0907.3426
10 pages, 6 figures
arxiv created 2009/10/05 · arxiv updated 2009/12/01
Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS investigation. We present a new procedure of the peak picking based on a sparse coding algorithm. Given a set of spectra of different classes, i.e. with different positions and heights of the peaks, this procedure can extract peaks by means of unsupervised learning. Instead of an l1-regularization penalty term used in the original sparse coding algorithm we propose using an elastic-net penalty term for better regularization. The evaluation is done by means of simulation. We show that for a large region of parameters the proposed peak picking method based on the sparse coding features outperforms a mean spectrum-based method. Moreover, we demonstrate the procedure applying it to two real-life datasets.