Spectral gene set enrichment (SGSE)

TL;DR

SGSE is a new unsupervised method that assesses the association between gene sets and data spectral structure, outperforming clustering-based methods in identifying relevant gene sets in genomic data.

Contribution

The paper introduces SGSE, a novel spectral approach for unsupervised gene set enrichment analysis that improves over existing clustering-dependent methods.

Findings

SGSE accurately recovers spectral features from noisy data.

SGSE outperforms standard clustering techniques in real microarray data.

The method is available as an R package.

Abstract

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: rob.frost@dartmouth.edu or jason.h.moore@dartmouth.edu