Exploring the Role of Dimensionality Reduction in High-Dimensional Genomic and Biomedical Statistical Analysis

Posted: Oct 06, 2023

• Elena Scott
• Elijah Rivera
• Ella Adams

Abstract

The rapid advancement of high-throughput technologies in genomics and biomedical research has generated datasets of unprecedented dimensionality and complexity. Modern genomic studies routinely involve measurements of tens of thousands of genes, proteins, or metabolites across hundreds or thousands of samples, creating statistical challenges that traditional methods are ill-equipped to handle. This research addresses the fundamental limitations of existing dimensionality reduction methods when applied to genomic and biomedical datasets. We propose that effective dimensionality reduction in these domains must move beyond purely mathematical optimization to incorporate biological knowledge and preserve the complex, non-linear relationships that characterize biological systems. We introduce a novel framework that integrates topological data analysis with manifold learning, creating a hybrid approach specifically designed for biomedical data. This framework incorporates biological prior knowledge through a multi-objective optimization process that balances statistical efficiency with biological relevance.