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BioMed Research International
Volume 2014 (2014), Article ID 346074, 11 pages
Research Article

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

1Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, No. 200, Lane 208, Jijinyi Road, Anle District, Keelung 204, Taiwan
2Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, 3900 NCTR Road, HFT-20, Jefferson, AR 72079, USA
3Department of Agronomy, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan
4Graduate Institute of Biostatistics and Biostatistics Center, China Medical University, Taichung, Taiwan

Received 2 April 2014; Revised 27 May 2014; Accepted 4 June 2014; Published 3 July 2014

Academic Editor: Tzu-Ya Weng

Copyright © 2014 Chih-Yi Chien et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the values and FDR (false discovery rate) -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.