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Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding

preprint
submitted on 17.12.2020, 13:40 and posted on 21.12.2020, 05:44 by hang hu, ruichuan yin, Hilary Brown, Julia Laskin

Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis are treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map is assembled from segment candidates generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.

Funding

NSF-1808136

Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry

National Heart Lung and Blood Institute

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History

Email Address of Submitting Author

jlaskin@purdue.edu

Institution

Purdue University

Country

United States

ORCID For Submitting Author

0000-0002-4533-9644

Declaration of Conflict of Interest

no conflict of interest

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