12/06/2026 - 14:30 - ISBA C.115 - 
 

Kris Sankaran 

(University of Wisconsin - Madison) 

Will give a presentation on : 

New Diagnostics for Dimensionality Reduction of Genomic Data  

Abstract: 
Dimensionality reduction helps organize high-dimensional genomics data into manageable low-dimensional representations, like differentiation trajectories in single cell data and community profiles in metagenomic sequencing. Such reductions are powerful but sensitive to hyperparameters and prone to misinterpretation. This talk addresses two risks in dimensionality reduction. First, we consider how to choose the number of topics K in topic modeling, a method from population genetics and language modeling, now common in microbiome analysis. While broadly useful, topic models require users to specify the number of topics K, which governs the resolution of learned topics. We discuss a new technique, topic alignment, for comparing topics across models with different resolutions. Simulation studies show that this approach distinguishes between true and spurious topics. Second, we examine the distortions introduced by nonlinear dimensionality reduction methods, like t-SNE and UMAP, when applied to single cell data. For example, these methods can introduce spurious clusters and fail to preserve cell density. We adopt the RMetric algorithm from manifold learning to measure local distortions. We also develop visualizations to explore these distortions. Representing cells as deformed ellipses highlights changes in local geometry, and an interactive interface selectively reveals evidence for distortion without overwhelming the viewer. Through case studies on simulated and real data, we find that the visualizations can flag fragmented neighborhoods, support hyperparameter tuning, and enable method selection. Topic alignment and distortion visualization are available as software packages, with case studies in online vignettes: https://go.wisc.edu/7h58r9, https://go.wisc.edu/ss5ts9.