样本基因的差异表达是生物学和医学研究的核心。提供一种新的多变量基因富集分析方法，称为主角富集分析(PAEA)。PAEA方法的主要特点是使用"主角"几何学概念，来量化样本基因的富集特点。利用真实的基因数据，把其他基因富集分析工具和PAEA进行对比测试。从大量的转录因子基因敲除或过表达研究中，用不同方法对基因表达特征进行富集分析，检测各种转录因子的富集度；同时使用ChIP－Seq数据创建的基因组库富集分析相同的转录因子。通过对比发现，PAEA优于一系列包括GSEA在内的常用基因富集分析工具；不仅如此，PAEA能很好地对老龄化相关表型的词条进行排序，特别是那些采用年轻个体组织样本与老年受试者相比较的基因差异表达研究中，效果尤其显著。作为一个界面友好的基因集富集网络分析工具，PAEA拥有超过70个可用于富集分析的基因组分析库，并且提供从GEO提取的超过700种疾病基因表达特征谱。

Functional analysis of genome-wide differential expression is central to biological investigations. Here we present a new multivariate approach to gene-set enrichment called Principal Angle Enrichment Analysis (PAEA), which uses the geometrical concept of the principal angle to quantify gene-set enrichment. Real data were used to benchmark PAEA in contrast to other enrichment analysis methods. We examined the ranking of transcription factors by performing enrichment analysis on gene expression signatures from many studies that knocked-down, knocked-out or over-expressed transcription factors, and performed the enrichment analysis with a library of gene sets created from ChIP-Seq data profiling the same transcription factors. We found that PAEA outperformed a selection of commonly used gene-set enrichment methods including GSEA. We also found that PAEA was able to rank better aging-related phenotype-terms from a collection of gene expression profiling studies where tissue from young adults was compared to tissue of elderly subjects. PAEA is implemented as a user-friendly R Shiny gene-set enrichment web application with over 70 gene set libraries available for enrichment analysis. Canned enrichment analysis for over 700 disease signatures extracted from GEO is provided.