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Title: 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 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.

Authors:
 [1];  [2]; ORCiD logo [3];  [4]
  1. Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, No. 200, Lane 208, Jijinyi Road, Anle District, Keelung 204, Taiwan
  2. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, 3900 NCTR Road, HFT-20, Jefferson, AR 72079, USA
  3. Department of Agronomy, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan
  4. 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. doi: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. doi: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 = {2014},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2014/346074

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Cited by: 1 work
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