Random Matrix Theory
Structured disease covariance perturbations are detectable in the sample spectrum only when their magnitude exceeds the square-root gamma threshold. In the spiked covariance model, a covariance spike separates from noise only above the BBP…
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Structured disease covariance perturbations are detectable in the sample spectrum only when their magnitude exceeds the square-root gamma threshold. In the spiked covariance model, a covariance spike separates from noise only above the BBP threshold. The Marchenko-Pastur law is used to distinguish structured disease signal from sampling noise in high-dimensional biomarker data. Study design should keep biomarker dimensionality small enough relative to sample size for disease covariance signals to rise above noise.