The University of Tennessee Health Science Center
08/17/2026 | News release | Distributed by Public on 08/17/2026 09:50
TODAY! Department of Preventive Medicine Biostatistics Seminar Series — Fast and Lean: Julia Tools for High-Dimensional Omics
The Division of Biostatistics of the Department of Preventive Medicine, UTHSC, invites you to attend TODAY's seminar.
Time: Monday, August 17, 2026, 2:00PM - 3:00PM CT
Register in advance for this meeting to get the Zoom link.
Fast and Lean: Julia Tools for High-Dimensional Omics
Abhisek Banerjee
University of Missouri Columbia
Modern omics studies measure thousands of correlated biological features, creating a need for dimension-reduction and multivariate methods that are both statistically reliable and computationally practical. We present the Julia packages BigRiverEssence.jl and BigRiverPlots.jl for extracting and visualizing structure in high-dimensional data. They are part of the BigRiver package ecosystem for high-dimensional omics data analysis. BigRiverEssence.jl implements methods including principal component analysis (PCA), sparse decompositions, partial least squares (PLS)-based models, canonical correlation analysis (CCA), and joint and individual variation explained (JIVE). The package preserves numerical accuracy while improving computational efficiency through preallocated workspaces, in-place linear algebra, efficient decompositions, and algebraic reformulations that avoid large intermediate matrices. In our benchmarks, these implementations achieved speedups ranging from 1.1× to 160× over corresponding R and Julia reference implementations. They maintained strong numerical agreement and substantially reduced memory allocations and use. BigRiverPlots.jl is a lightweight collection of plotting recipes for exploring and communicating multivariate results. Its plotting tools work with ordinary arrays, tables, data frames, and compatible outputs from other analytical packages. Together, BigRiverEssence.jl and BigRiverPlots.jl demonstrate how Julia can support a fast, memory-aware workflow for high-dimensional data analysis, from statistical computation to visualization.
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The University of Tennessee Health Science Center published this content on August 17, 2026, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on August 17, 2026 at 15:50 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]