Autor según el artículo: Sementé L; Baquer G; García-Altares M; Correig-Blanchar X; Ràfols P

Departamento: Enginyeria Electrònica, Elèctrica i Automàtica

Autor/es de la URV: Baquer Gómez, Gerard Sergi / Correig Blanchar, Francesc Xavier / Garcia-Altares Pérez, Maria / Ràfols Soler, Pere / Sementé Fernández, Lluc

Palabras clave: Virus-infection spectra r package

Resumen: Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.

Áreas temáticas: Spectroscopy Química Odontología Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General medicine Farmacia Environmental chemistry Engenharias iv Engenharias iii Engenharias ii Enfermagem Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Biochemistry Astronomia / física Analytical chemistry

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: xavier.correig@urv.cat maria.garcia-altares@urv.cat pere.rafols@urv.cat gerard.baquer@estudiants.urv.cat gerard.baquer@estudiants.urv.cat lluc.semente@urv.cat lluc.semente@urv.cat lluc.semente@urv.cat

Identificador del autor: 0000-0002-6902-3054 0000-0002-9240-4058 0000-0002-4433-4972 0000-0002-4433-4972

Fecha de alta del registro: 2023-02-23

Volumen de revista: 1171

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0003267021004955?via%3Dihub

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Analytica Chimica Acta. 1171

Referencia de l'ítem segons les normes APA: Sementé L; Baquer G; García-Altares M; Correig-Blanchar X; Ràfols P (2021). rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios. Analytica Chimica Acta, 1171(), -. DOI: 10.1016/j.aca.2021.338669

DOI del artículo: 10.1016/j.aca.2021.338669

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications