SETPath: Spiked Eigenvalue Test for Pathway data

Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes. Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal. A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".

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