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MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging.
Eriksson, Jonatan O; Sánchez Brotons, Alejandro; Rezeli, Melinda; Suits, Frank; Markó-Varga, György; Horvatovich, Peter.
Afiliação
  • Eriksson JO; Department of Biomedical Engineering, Lund University, Lund 221 00, Sweden.
  • Sánchez Brotons A; Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
  • Rezeli M; Department of Biomedical Engineering, Lund University, Lund 221 00, Sweden.
  • Suits F; IBM Research - Australia, 60 City Road, Southbank, VIC 3006, Australia.
  • Markó-Varga G; Department of Biomedical Engineering, Lund University, Lund 221 00, Sweden.
  • Horvatovich P; Department of Biomedical Engineering, Lund University, Lund 221 00, Sweden.
Anal Chem ; 92(24): 16138-16148, 2020 12 15.
Article em En | MEDLINE | ID: mdl-33317272
ABSTRACT
Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample's molecular composition. Our approach, MSIWarp (https//github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article