MAVTgsa: An R Package for Gene Set (Enrichment) Analysis
Abstract
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.
- Authors:
-
[3];
- Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, No. 200, Lane 208, Jijinyi Road, Anle District, Keelung 204, Taiwan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, 3900 NCTR Road, HFT-20, Jefferson, AR 72079, USA
- Department of Agronomy, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, 3900 NCTR Road, HFT-20, Jefferson, AR 72079, USA, Graduate Institute of Biostatistics and Biostatistics Center, China Medical University, Taichung, Taiwan
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1228276
- Resource Type:
- Published Article
- Journal Name:
- BioMed Research International
- Additional Journal Information:
- Journal Name: BioMed Research International Journal Volume: 2014; Journal ID: ISSN 2314-6133
- Publisher:
- Hindawi Publishing Corporation
- Country of Publication:
- Egypt
- Language:
- English
Citation Formats
Chien, Chih-Yi, Chang, Ching-Wei, Tsai, Chen-An, and Chen, James J. MAVTgsa: An R Package for Gene Set (Enrichment) Analysis. Egypt: N. p., 2014.
Web. doi:10.1155/2014/346074.
Chien, Chih-Yi, Chang, Ching-Wei, Tsai, Chen-An, & Chen, James J. MAVTgsa: An R Package for Gene Set (Enrichment) Analysis. Egypt. https://doi.org/10.1155/2014/346074
Chien, Chih-Yi, Chang, Ching-Wei, Tsai, Chen-An, and Chen, James J. Wed .
"MAVTgsa: An R Package for Gene Set (Enrichment) Analysis". Egypt. https://doi.org/10.1155/2014/346074.
@article{osti_1228276,
title = {MAVTgsa: An R Package for Gene Set (Enrichment) Analysis},
author = {Chien, Chih-Yi and Chang, Ching-Wei and Tsai, Chen-An and Chen, James J.},
abstractNote = {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 P values and FDR (false discovery rate) q -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.},
doi = {10.1155/2014/346074},
journal = {BioMed Research International},
number = ,
volume = 2014,
place = {Egypt},
year = {Wed Jan 01 00:00:00 EST 2014},
month = {Wed Jan 01 00:00:00 EST 2014}
}
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