TY - JOUR AU - Frost, H. Robert AU - Li, Zhigang AU - Moore, Jason H. PY - 2015 DA - 2015/08/19 TI - Principal component gene set enrichment (PCGSE) JO - BioData Mining SP - 25 VL - 8 IS - 1 AB - 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. SN - 1756-0381 UR - https://doi.org/10.1186/s13040-015-0059-z DO - 10.1186/s13040-015-0059-z ID - Frost2015 ER -