  Motivation: Gene set testing is typically performed in a supervised context
to quantify the association between groups of genes and a clinical phenotype.
In many cases, however, a gene set-based interpretation of genomic data is
desired in the absence of a phenotype variable. Although methods exist for
unsupervised gene set testing, they predominantly compute enrichment relative
to clusters of the genomic variables with performance strongly dependent on the
clustering algorithm and number of clusters. Results: We propose a novel
method, spectral gene set enrichment (SGSE), for unsupervised competitive
testing of the association between gene sets and empirical data sources. SGSE
first computes the statistical association between gene sets and principal
components (PCs) using our principal component gene set enrichment (PCGSE)
method. The overall statistical association between each gene set and the
spectral structure of the data is then computed by combining the PC-level
p-values using the weighted Z-method with weights set to the PC variance scaled
by Tracey-Widom test p-values. Using simulated data, we show that the SGSE
algorithm can accurately recover spectral features from noisy data. To
illustrate the utility of our method on real data, we demonstrate the superior
performance of the SGSE method relative to standard cluster-based techniques
for testing the association between MSigDB gene sets and the variance structure
of microarray gene expression data. Availability:
http://cran.r-project.org/web/packages/PCGSE/index.html Contact:
<EMAIL_ADDRESS>or<EMAIL_ADDRESS>