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<title lang=en>Using collective expert judgements to evaluate quality measures of mass spectrometry images</title>
<creator>Palmer, Andrew</creator>
<creator>Ovchinnikova, Ekaterina</creator>
<creator>Thuné, Mikael</creator>
<creator>Lavigne, Régis</creator>
<creator>Guével, Blandine</creator>
<creator>Dyatlov, Andrey</creator>
<creator>Vitek, Olga</creator>
<creator>Pineau, Charles</creator>
<creator>Borén, Mats</creator>
<creator>Alexandrov, Theodore</creator>
<contributor>Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )</contributor>
<contributor>Plateforme Protéomique-Biogenouest (PPB) ; Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ) - Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ) - Proteomics Core Facility (Protim) ; Université de Rennes 1 (UR1) - Plateforme Génomique Santé Biogenouest® - Plateforme Génomique Santé Biogenouest®</contributor>
<contributor>Center of Industrial Mathematics - University of Bremen ; University of Bremen</contributor>
<contributor>Steinbeis Innovation Center SCiLS Research ; SCILS Gmbh</contributor>
<contributor>Skaggs School of Pharmacy and Pharmaceutical Sciences ; University of California [San Diego] (UC San Diego)</contributor>
<description>International audience</description>
<source>Bioinformatics (Oxford, England)</source>
<identifier>hal-01163754</identifier>
<identifier>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01163754</identifier>
<source>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01163754</source>
<source>Bioinformatics (Oxford, England), 2015, 31 (12), pp.i375--i384. 〈10.1093/bioinformatics/btv266〉</source>
<identifier>DOI : 10.1093/bioinformatics/btv266</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btv266</relation>
<identifier>PUBMED : 26072506</identifier>
<relation>info:eu-repo/semantics/altIdentifier/pmid/26072506</relation>
<language>en</language>
<subject>[SDV] Life Sciences [q-bio]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>MOTIVATION: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. RESULTS: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts' optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff's alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality. AVAILABILITY AND IMPLEMENTATION: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality. CONTACT: theodore.alexandrov@embl.de</description>
<date>2015</date>
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