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In this work Uniform Manifold Approximation and Projection (UMAP) is applied for non-linear dimensionality reduction and visualisation of Mass Spectrometry Imaging (MSI) data. We evaluate the performance of the UMAP algorithm on MSI datasets acquired in mouse pancreas and human lymphoma samples and compare it to those of principal component analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE) and the Barnes-Hut (BH) approximation thereof. Furthermore, we compare different distance metrics in (BH) t-SNE and UMAP, and propose the use of spatial autocorrelation as a means of comparing the resulting low-dimensional embeddings. The results indicate that UMAP is competitive with t-SNE in terms of visualisation, and is well-suited for the dimensionality reduction of large (>100 000 pixels) MSI datasets. With an almost fourfold decrease in runtime, it is more scalable in comparison to the current state-of-the-art, t-SNE or the Barnes-Hut approximation thereof. In what seems to be the first application of UMAP to MSI data, we assess the value of applying alternative distance metrics such as the correlation, cosine, and the Chebyshev metric in contrast to the traditionally used Euclidean distance metric. Furthermore, we propose 'histomatch' as an additional custom distance metric for the analysis of MSI data.
This article was published in the following journal.
Name: Analytical chemistry
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Methods used to take into account and incorporate spatial autocorrelation and regional variation into regression analysis models of data that has spatial dependency, and also to provide information on the spatial relationships among the variables.
Integration of spatial information perceived by visual and/or auditory CUES.
A process through which individuals encode information about their environmental CUES to facilitate SPATIAL NAVIGATION.
The act of knowing or the recognition of a distance by recollective thought, or by means of a sensory process which is under the influence of set and of prior experience.
Memory related to spatial information.
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