A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data

Abstract

We propose a new method for soft spatial segmentation of matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) data which is based on probabilistic clustering with subsequent smoothing. Clustering of spectra is done with the Latent Dirichlet Allocation (LDA) model. Then, clustering results are smoothed with a Markov random field (MRF) resulting in a soft probabilistic segmentation map. We show several extensions of the basic MRF model specifically tuned for MALDI-IMS data segmentation. We describe a highly parallel implementation of the smoothing algorithm based on GraphLab framework and show experimental results. 1998 ACM Subject Classification I.4.6 Segmentation, J.3 LIFE AND MEDICAL SCIENCES, J.2 PHYSICAL SCIENCES AND ENGINEERING, H.2.8 Database Applications

DOI: 10.4230/OASIcs.GCB.2012.39

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@inproceedings{Chernyavsky2012ATS, title={A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data}, author={Ilya Chernyavsky and Theodore Alexandrov and Peter Maass and Sergey I. Nikolenko}, booktitle={GCB}, year={2012} }