Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

Molecular & Cellular Proteomics : MCP
Kyle D BemisOlga Vitek

Abstract

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in ...Continue Reading

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Citations

May 1, 2019·Journal of Mass Spectrometry : JMS·Ethan YangPierre Chaurand
Sep 19, 2019·Journal of the American Society for Mass Spectrometry·Patrick M WehrliJörg Hanrieder
Oct 12, 2019·Mass Spectrometry Reviews·Nico VerbeeckRaf Van de Plas
Jul 5, 2019·Science Translational Medicine·Corbin G ThompsonAngela D M Kashuba
Dec 10, 2019·GigaScience·Melanie Christine FöllOliver Schilling
Feb 6, 2020·Nature Microbiology·Benedikt GeierManuel Liebeke
Mar 2, 2021·Analytical and Bioanalytical Chemistry·Wanqiu ZhangNico Verbeeck
Jun 8, 2021·Journal of the American Society for Mass Spectrometry·Shuangshuang TianGuangming Huang
Jun 1, 2021·Annual Review of Biomedical Data Science·Theodore Alexandrov
Apr 25, 2020·Journal of the American Society for Mass Spectrometry·Gordon T LuuLaura M Sanchez
Sep 15, 2020·Journal of the American Society for Mass Spectrometry·Emily R SekeraTroy D Wood
Aug 18, 2020·Journal of the American Society for Mass Spectrometry·Andrea R KelleyGeorge Perry
Dec 4, 2021·Proceedings of the National Academy of Sciences of the United States of America·Berkley M EllisJohn A McLean

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Software Mentioned

Mac
PLS
Bioconductor
R package Cardinal
SASA
Cardinal
Linux
ClinProTools
DA
Windows

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