Dimensionality Reduction for Visualizing Spatially Resolved Profiling Data Using Spasne

Background and objectiveSpatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single cell sequencing data, such as the t-SNE and UMAP, were not tailored for spatially resolved profiling data. MethodsHere we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from three experimental platforms and consisted of cells from different diseases, tissues, and cell types. ResultsTo compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to four spatially resolved profiling datasets obtained from three distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures. ConclusionsThis work demonstrates the broad application of SpaSNE for reliable and robust interpretation on cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.