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Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues
MOTIVATION: Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612871/ https://www.ncbi.nlm.nih.gov/pubmed/31510675 http://dx.doi.org/10.1093/bioinformatics/btz345 |
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author | Guo, Dan Bemis, Kylie Rawlins, Catherine Agar, Jeffrey Vitek, Olga |
author_facet | Guo, Dan Bemis, Kylie Rawlins, Catherine Agar, Jeffrey Vitek, Olga |
author_sort | Guo, Dan |
collection | PubMed |
description | MOTIVATION: Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution. RESULTS: This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/Vitek-Lab/IonSpattern. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6612871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66128712019-07-12 Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues Guo, Dan Bemis, Kylie Rawlins, Catherine Agar, Jeffrey Vitek, Olga Bioinformatics Ismb/Eccb 2019 Conference Proceedings MOTIVATION: Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution. RESULTS: This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/Vitek-Lab/IonSpattern. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-07 2019-07-05 /pmc/articles/PMC6612871/ /pubmed/31510675 http://dx.doi.org/10.1093/bioinformatics/btz345 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ 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 |
spellingShingle | Ismb/Eccb 2019 Conference Proceedings Guo, Dan Bemis, Kylie Rawlins, Catherine Agar, Jeffrey Vitek, Olga Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title | Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title_full | Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title_fullStr | Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title_full_unstemmed | Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title_short | Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
title_sort | unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues |
topic | Ismb/Eccb 2019 Conference Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612871/ https://www.ncbi.nlm.nih.gov/pubmed/31510675 http://dx.doi.org/10.1093/bioinformatics/btz345 |
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