Presenter: Brian Luke, Ph.D., Advanced Biomedical Computational Science, FNLCR

Visualizing high-dimensional data can be problematic. A common method is Principal Component Analysis (PCA), but other methods include Multi-Dimensional Scaling (MDS), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). This talk discusses the differences between these methods without going into complicated mathematics and will hopefully allow you to determine which type of plot is best for your data.

This session is part of a complementary event with the BTEP Coding Club session "Visualizing High-Dimensional Data: MDS, PCA, t-SNE, and UMAP (Part 2)", which focuses on the practical implementation using R. Attending both sessions is encouraged for a comprehensive understanding of the topic.

This will be a hybrid event. Please register at this link.

This session will be recorded, and materials will be shared with attendees a few days after the event.

For additional details and questions, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science group, Frederick National Laboratory for Cancer Research.