Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding
2020
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.
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