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Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

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Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai School, One Gustave L. Levy Place, New York, NY, 10029, USA; Big Data to Knowledge (BD2K) Library of Integrated Network-based Cellular Signatures (LINCS) Data Coordination and Integration Center (DCIC); Mount Sinai Knowledge Management Center (KMC) for Illuminating the Druggable Genome (IDG).
Warsaw School of Information Technology under the auspices of the Polish Academy of Sciences, 6 Newelska St., 01-447, Warsaw, Poland.


Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.


dimensionality reduction; gene set analysis; multivariate; online enrichment analysis tool

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