Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.28.317073v1?rss=1 Authors: Smets, T., De Keyser, T., Tousseyn, T., Waelkens, E., De Moor, B. Abstract: High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While H&E stainings are still the gold standard to diagnose disease, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualise the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as Mass Spectrometry Imaging to the cellular resolution. Copy rights belong to original authors. Visit the link for more info