LAUSR

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Non-linear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of the massive datasets produced by MSI. These visualizations can offer us good initial insights regarding the heterogeneity and variety of molecular patterns present in the data, but they do not discern which molecules might be driving these observations. To prioritize the m/z-values associated with these biochemical profiles, we apply a bidirectional dimensionality reduction approach taking into account both the spectral and spatial information. The results show that both sources of information are instrumental to get a more comprehensive view on the relevant m/z-values and can support the reliability of the results obtained using UMAP. We illustrate our approach on heterogeneous pancreas tissues obtained from healthy mice.