title: Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.
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 spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.
authors.affiliation: From the ‡Department of Statistics and.
authors.forename: Kyle D
authors.initials: KD
authors.lastname: Bemis
authors.affiliation: From the ‡Department of Statistics and.
authors.forename: April
authors.initials: A
authors.lastname: Harry
authors.affiliation: §Department of Chemistry, Purdue University, West Lafayette, IN 47907;
authors.forename: Livia S
authors.initials: LS
authors.lastname: Eberlin
authors.affiliation: §Department of Chemistry, Purdue University, West Lafayette, IN 47907;
authors.forename: Christina R
authors.initials: CR
authors.lastname: Ferreira
authors.affiliation: ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and.
authors.forename: Stephanie M
authors.initials: SM
authors.lastname: van de Ven
authors.affiliation: ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and.
authors.forename: Parag
authors.initials: P
authors.lastname: Mallick
authors.affiliation: ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and.
authors.forename: Mark
authors.initials: M
authors.lastname: Stolowitz
authors.affiliation: **College of Computer and Information Science, Northeastern University, Boston, MA 02115 o.vitek@neu.edu.
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007477:Ions / Q000032:analysis*; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D012377:Rodentia; D013552:Swine
chemical_list: D007477:Ions