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MSIWarp : A General...
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Eriksson, Jonatan O.Lund University,Lunds universitet,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Clinical Protein Science and Imaging,Forskargrupper vid Lunds universitet,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Lund University Research Groups
(author)
MSIWarp : A General Approach to Mass Alignment in Mass Spectrometry Imaging
- Article/chapterEnglish2020
Publisher, publication year, extent ...
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2020-12-02
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American Chemical Society (ACS),2020
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LIBRIS-ID:oai:lup.lub.lu.se:3eb98788-0223-4ba9-9e43-e85b281af822
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https://lup.lub.lu.se/record/3eb98788-0223-4ba9-9e43-e85b281af822URI
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https://doi.org/10.1021/acs.analchem.0c03833DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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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.
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Sánchez Brotons, AlejandroUniversity of Groningen
(author)
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Rezeli, MelindaLund University,Lunds universitet,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Clinical Protein Science and Imaging,Forskargrupper vid Lunds universitet,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Lund University Research Groups(Swepub:lu)elma-mrl
(author)
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Suits, FrankIBM Australia Ltd
(author)
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Markó-Varga, GyörgyLund University,Lunds universitet,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Clinical Protein Science and Imaging,Forskargrupper vid Lunds universitet,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Lund University Research Groups(Swepub:lu)akem-gmv
(author)
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Horvatovich, PeterLund University,Lunds universitet,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,University of Groningen(Swepub:lu)pe4034ho
(author)
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Institutionen för biomedicinsk teknikInstitutioner vid LTH
(creator_code:org_t)
Related titles
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In:Analytical Chemistry: American Chemical Society (ACS)92:24, s. 16138-161480003-27001520-6882
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