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      SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data

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          Abstract

          Summary

          SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results.

          Availability and implementation

          SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references12

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          Image quality assessment: from error visibility to structural similarity.

          Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.
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            Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-μm lateral resolution

            An instrumental setup for atmospheric pressure MALDI-based mass spectrometry imaging with improved lateral resolution enables subcellular-level details to be resolved.
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              FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry

              The authors present a computational framework for false-discovery-rate-controlled metabolite annotation from high-resolution imaging mass spectrometry data.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 January 2019
                13 July 2018
                13 July 2018
                : 35
                : 1
                : 178-180
                Affiliations
                [1]Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
                Author notes
                To whom correspondence should be addressed. p.inglese14@ 123456imperial.ac.uk or r.glen@ 123456imperial.ac.uk
                Article
                bty622
                10.1093/bioinformatics/bty622
                6298046
                30010780
                5a2d7b93-748a-456d-861b-ae6a387213ba
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 16 February 2018
                : 18 June 2018
                : 10 July 2018
                Page count
                Pages: 3
                Funding
                Funded by: Cancer Research UK 10.13039/501100000289
                Funded by: National Institute for Health Research 10.13039/501100000272
                Funded by: Imperial Biomedical Research Centre
                Funded by: National Institute for Health Research 10.13039/501100000272
                Funded by: Imperial Biomedical Research Centre
                Funded by: NHS
                Funded by: National Institute for Health Research 10.13039/501100000272
                Funded by: Department of Health 10.13039/501100000276
                Categories
                Applications Notes
                Bioimage Informatics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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