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Explore how Random Matrix Theory can be applied to extract meaningful biological signals from noisy single-cell RNA sequencing data in this 44-minute conference talk. Learn about the mathematical challenges inherent in studying single-cell systems, where noise originates from both technical artifacts and biological processes like stochastic gene expression. Discover how single-cell RNA-seq data can be modeled using a threefold mathematical structure consisting of a random matrix, sparsity-induced signals, and biological signals. Understand how the universality properties of spectral distributions and localization properties of eigenvectors in Random Matrix Theory enable researchers to isolate genuine biological signals while filtering out noise and sparsity-induced artifacts. Gain insights into cutting-edge mathematical approaches for analyzing complex biological data presented at IPAM's Mathematics of Cancer workshop by Luis Aparicio from Columbia University.
