title: Principal component gene set enrichment (PCGSE).
abstract: BACKGROUND
  Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing interpretation methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with mainly zero loadings. Although useful when just a few variables dominate the population PCs, these methods can perform poorly on genomic data, where interesting biological features are frequently represented by the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs.
  
  
  RESULTS
  We describe a novel approach, principal component gene set enrichment (PCGSE), for unsupervised gene set testing relative to the sample PCs of genomic data. The PCGSE method computes the statistical association between gene sets and individual PCs using a two-stage competitive gene set test. To demonstrate the efficacy of the PCGSE method, we use simulated and real gene expression data to evaluate the performance of various gene set test statistics and significance tests.
  
  
  CONCLUSIONS
  Gene set testing is an effective approach for interpreting the PCs of high-dimensional genomic data. As shown using both simulated and real datasets, the PCGSE method can generate biologically meaningful and computationally efficient results via a two-stage, competitive parametric test that correctly accounts for inter-gene correlation.
authors: Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Lebanon, 03756 NH USA ; Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine, Lebanon, 03756 NH USA ; Department of Genetics, Dartmouth College, Hanover, 03755 NH USA.
authors: H Robert
authors: HR
authors: Frost
authors: Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Lebanon, 03756 NH USA ; Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine, Lebanon, 03756 NH USA.
authors: Zhigang
authors: Z
authors: Li
authors: Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Lebanon, 03756 NH USA ; Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine, Lebanon, 03756 NH USA ; Department of Genetics, Dartmouth College, Hanover, 03755 NH USA.
authors: Jason H
authors: JH
authors: Moore