The limits of dimensionality reduction tools for single-cell analysis

Visualizing single-cell data often involves reducing its dimensionality with tools like t-SNE and UMAP. While these tools can be helpful in making high-dimensional data more visually accessible, reducing complexity can also create misleading results. Join us for a webinar with bioinformatician Dr. Tyler Burns to learn how to rigorously interrogate dimensionality reduction tools and avoid common pitfalls in their interpretation.

Dr. Tyler Burns is a computational biologist with over a decade of experience in the single-cell analysis field. With a PhD in Cancer Biology from the Stanford School of Medicine, Dr. Burns currently works at the intersection of wet lab biology, bioinformatics, and biomedical business strategy.

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