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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O14 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O14. ENHANCEMENT OF IMAGING MASS SPECTROMETRY DATA THROUGH REMOVAL OF SPARSE INTENSITY VARIATIONS Yousef El Aalamat 1,2* , Xian Mao 1,2 , Nico Verbeeck 3 , Junhai Yang 4 , Bart De Moor 1,2 , Richard M. Caprioli 4 , Etienne Waelkens 5,6 & Raf Van de Plas 3,4 . Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven 1 ; iMinds Medical IT, KU Leuven 2 ; Delft Center for Systems and Control, Delft University of Technology 3 ; Mass Spectrometry Research Center (MSRC),Vanderbilt University 4 ; Department of Cellular and Molecular Medicine, KU Leuven 5 ; Sybioma, KU Leuven 6 . *yelaalam@esat.kuleuven.be Imaging mass spectrometry (IMS) is rapidly evolving as a label-free, spatially resolved molecular imaging tool for the direct analysis of biological samples. However, mass spectrometry (MS) measurements are subject to different types of noise. In IMS, one of the most abundant noise types in ion images is the presence of localized intensity spikes, known also as sparse intensity variations, which occur on top of the biological ion distribution pattern. In this study, we develop a method that addresses the issue of sparse intensity noise. We use low-rank approximations of the IMS data to separate and filter sparse intensity variations from the MS signals. The efficiency of the developed method is tested using MS measurements of coronal sections of mouse brain and strong de-noising performance is demonstrated both along the spatial and the spectral domain. INTRODUCTION Imaging mass spectrometry (IMS) provides unique capabilities for biomedical and biological research. However, its measurements tend to be subject to different types of noise. One of the more abundant noise types in IMS are localized intensity spikes, which can be seen as sparse intensity variations on top of the true biological ion patterns. This kind of noise can have a substantial impact, particularly on low ion intensity measurements where the signal-to-noise ratio (SNR) can be significantly affected. We present a method to filter sparse intensity variations from IMS data, and demonstrate its use to de-noise IMS measurements both along the spatial and the spectral domain. METHODS We introduce a de-noising algorithm based on low-rank approximation, a concept from linear algebra. The method can separate sparse intensity variations from biological and tissue sample patterns, which hold up across multiple ions and pixels. This approach decomposes IMS data into two parts, namely a structured data matrix and a sparse data matrix. Since the noise tends to be sparse in nature, it will have a propensity to be collected into the sparse data part. The structured part tends to capture the de-noised IMS signals, effectively de-noising the ion images and the spectral profiles in the process. This de-noising method allows us to automatically filter sparse intensity variations from the underlying tissue signal without requiring any parameter tuning. RESULTS & DISCUSSION The filter method is demonstrated on two IMS experiments (one lipid-focused and one protein-focused) acquired from coronal sections of mouse brain. For the protein experiment, the tissue section was coated with sinapinic acid, and measurements were acquired using a Bruker AutoFlex MALDI-TOF/TOF in positive linear mode at a spatial resolution of 100 μm and with a mass range extending from m/z 3000 to 22000. For the lipid experiment, the tissue section was sublimated with 1,5- diaminonaphthalene, and the measurements were acquired using a Bruker AutoFlex MALDI-TOF/TOF in negative reflectron mode at a spatial resolution of 80 μm and with a mass range extending from m/z 400 to 1000. The case studies demonstrate robust de-noising performance, retrieving the underlying tissue signal efficiently and consistently using the structured data matrix. On the spatial side, we observe a clean-up effect in the spatial distributions of both high- and low-intensity ions. The effect is especially impactful for low-intensity ions, showing a strong increase in the amount of spatial structure that can be retrieved from low SNR measurements and revealing patterns that would have gone unnoticed otherwise. On the spectral side, we observe an improved SNR after applying the method. Thus, at the cost of computational analysis, the de-noising method described here provides a means of increasing the amount of information that can be extracted from an IMS experiment, without requiring user interaction or additional measurement. FIGURE 1. Impact on both spatial and spectral domain. Top: example of de-noised ion image. Bottom: plot of a spectrum before (blue) and after (red) removal of sparse intensity variations. 34
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O15 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O15. DETERMINANTS OF COMMUNITY STRUCTURE IN THE PLANKTON INTERACTOME Gipsi Lima-Mendez 1,2* , Karoline Faust 1,2,3 , Nicolas Henry 4 , Johan Decelle 4 , Sébastien Colin 4 , Fabrizio Carcillo 2,3,5 , Simon Roux 6 , Gianluca Bontempi 5 , Matthew B. Sullivan 6 , Chris Bowler 7 , Eric Karsenti 7,8 , Colomban de Vargas 4 & Jeroen Raes 1,2 . Department of Microbiology and Immunology, Rega Institute KU Leuven 1 ; VIB Center for the Biology of Disease 2 ; Laboratory of Microbiology, Vrije Universiteit Brussel, Belgium 3 ; CNRS, UMR 7144, Station Biologique de Roscoff 4 ; Interuniversity Institute of Bioinformatics in Brussels (IB) 2 , Machine Learning Group, Université Libre de Bruxelles 5 ; Department of Ecology and Evolutionary Biology, University of Arizona, USA 6 ; Ecole Normale Supérieure, Institut de Biologie (IBENS), France 7 ; European Molecular Biology Laboratory 8 .*Gipsi.limamendez@vib-kuleuven.be Identifying the abiotic and biotic factors that shape species interactions are fundamental yet unsolved goals in ecology. Here, we integrate organismal abundances and environmental measures from Tara Oceans to reconstruct the first global photic-zone co-occurrence network. Environmental factors are incomplete predictors of community structure. Putative biotic interactions are non-randomly distributed across phylogenetic groups, and show both local and global patterns. Known and novel interactions were identified among grazers, primary producers, viruses and symbionts. The high prevalence of parasitism suggests that parasites are important regulators in the ocean food web. Together, this effort provides a foundational resource for ocean food web research and integrating biological components into ocean models. INTRODUCTION Determining the relative importance of both biotic and abiotic processes represents a grand challenge in ecology. Here we analyze sequence on plankton organisms and environmental data from the Tara-Oceans project. We applied network inference methods to construct a globalocean cross-kingdom species interaction network and disentangled the biotic and abiotic signals shaping this interactome (Lima-Mendez, et al., 2015). METHODS Methods are described in details in (Lima-Mendez, et al., 2015). Briefly: Network inference. Taxon-taxon networks were constructed as in (Faust, et al., 2012), selecting Spearman and Kullback-Leibler dissimilarity. Edges with merged multiple-test-corrected p- values below 0.05 were kept. Taxon-environment networks were computed with the same procedure and merged with taxon-taxon networks for environmental triplet detection. Indirect taxon edge detection. For each triplet consisting of two taxa and one environmental parameter, we computed the interaction information (II) and taxon edges were considered indirect when II
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