Gaining biological insights through supervised data visualization If you have ever run t-SNE or UMAP on a biological dataset and gotten a beautiful plot that had nothing to do with the question you cared about, you have met the core problem this paper tackles. Unsupervised embeddings preserve whatever variation dominates the data, which is often not the variation tied to your labels. The existing supervised fixes tend to overcorrect: they bake class membership into the distance metric and force artificially clean separation, which looks impressive but misleads you about the real structure. They also break on continuous labels and cannot place new unlabeled points. Jake Rhodes and coauthors propose RF-PHATE, which threads the needle. Instead of distorting distances by class, they train a random forest on the labels and extract RF-GAP proximities, similarities defined by how often points land in the same terminal nodes, weighted to reproduce the forest's out-of-bag predictions. These proximities already encode feature importance for the supervised task. They then feed them into a PHATE-style diffusion pipeline: row-normalize into a Markov operator, add a PageRank-like damping term so isolated clusters do not trap the random walk, power the operator for global structure, and map the potential distances to low dimensions with MDS. The result emphasizes label-relevant geometry while suppressing noise, and because random forests handle mixed and continuous targets natively, it works for classification and regression. What makes the paper convincing is the stress-testing. On synthetic data with 500 added noise variables, RF-PHATE recovers the true branching structure while unsupervised methods collapse and class-conditional methods shatter it into fake clusters. They also introduce three metrics that penalize hyperseparation, then show across 27 datasets that RF-PHATE preserves structure without inflating separation, unlike supervised UMAP and S-tSNE. In multiple sclerosis data it surfaces a nonbenign RRMS subgroup, and on RNA-seq it holds cell-type separation even at 75 percent dropout where PHATE and UMAP fail. The useful idea is that supervision can steer a visualization toward the variable you actually care about without manufacturing the separation you are trying to detect, the trap that makes most supervised embeddings useless for decision support. In drug discovery, clinical biomarker work, or materials screening, where you have noisy high-dimensional data plus a relevant label, this lets you explore structure that respects your target while staying honest about overlap. Paper: Rhodes et al., Nature Computational Science (2026) , journal license | nature.com/articles/s43588-0…