Hierarchical Deconvolution of Linear Mixtures of High-Dimensional Mass Spectra in Microbiology

Frank-Michael Schleif, Stephan Simmuteit, and Thomas Villmann


Self-Organizing Map, Evolving Tree, Linear Mixtures, Mass Spectrometry


This paper introduces a hierarchical model for the description and deconvolution of composite patterns. The patterns are described in a basis system of spectral basis functions.The mixture coefficients for the composite patterns are determined by solving a linear mixture model with nonnegative coefficients. In life science research, wet-lab mixed samples of possible known basis substances occur regularly and cause a challenge for identification tasks. Alsoin case of known basis functions the problem is still complex, if the used basis is very sparse and the number of basis functions is very large. Simple approaches either try combining different basis spectra or incorporate blind source separation. Our proposed method is to use nonnegative least squares combined with a hierarchical prototype based learning model. We evaluate our method on mixtures of real and simulated composite patterns of mass spectrometry data from bacteria. Results show remarkable success and can be taken as a promising step in the new field of automatic unmixing of mixed cultures.

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