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Evaluation of distance metrics and spatial autocorrelation in Uniform Manifold Approximation and Projection applied to Mass Spectrometry Imaging data.

08:00 EDT 15th April 2019 | BioPortfolio

Summary of "Evaluation of distance metrics and spatial autocorrelation in Uniform Manifold Approximation and Projection applied to Mass Spectrometry Imaging data."

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.

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This article was published in the following journal.

Name: Analytical chemistry
ISSN: 1520-6882
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