When Seeing Is Not Believing: Understanding Uniform Manifold Approximation and Projection (UMAP) and t-Distributed...

DNA methylation profiling has become an important tool for tumor classification across multiple subspecialties of pathology. To visualize relationships among samples, many studies employ dimensionality-reduction techniques such as uniform manifold approximation and projection and t-distributed stochastic neighbor embedding. Although these techniques create visually compelling plots that summarize complex molecular data, they are visualization tools, not clustering or classification algorithms, and their appearance depends heavily on sample selection, data preprocessing, and parameter choices. This technical review outlines key considerations for the interpretation and reporting of visualization and classification methods in methylation-based tumor studies. We recommend a set of reporting standards covering full transparency of preprocessing and algorithmic parameters, pairing visualizations with quantitative clustering or classification analyses, and validating observed groupings with independent clinical, histologic, and molecular data. An accompanying online notebook allows readers to explore different settings on public tumor methylation data sets to visualize their effects on uniform manifold approximation and projection and t-distributed stochastic neighbor embedding. We focus on how to interpret methylation-based visualizations and classifier output in diagnostic settings, rather than providing a comprehensive comparison of profiling methods, classification tools, or clustering algorithms. Understanding these principles will help ensure the reproducibility and scientific rigor of pathology research incorporating methylation-based tumor classification.