Statistical Method scDEED Detects Dubious t-SNE and UMAP Embeddings and Optimizes Hyperparameters

t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) are non-linear dimensionality reduction techniques for visualizing high-dimensional data, particularly in the context of single-cell analysis for visualizing cell clusters. However, it is important to note that t-SNE and UMAP may not always produce trustworthy representations of the relative distances between cell clusters. In our Nature Communications paper [1], we provide a framework for (1) identifying data distortions in projection from a high-dimensional to two-dimensional (2D) space and (2) optimizing hyperparameter settings in a 2D dimension-reduction method. <a href="https://towardsdatascience.com/statistical-method-scdeed-detects-dubious-t-sne-and-umap-embeddings-and-optimizes-hyperparameters-470fb9cb606f">Website</a>