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Linear combination test for gene set analysis of a continuous phenotype
BACKGROUND: Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary phenotype. Equally, if not more importantly however, is the ability to test the enri...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717275/ https://www.ncbi.nlm.nih.gov/pubmed/23815123 http://dx.doi.org/10.1186/1471-2105-14-212 |
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author | Dinu, Irina Wang, Xiaoming Kelemen, Linda E Vatanpour, Shabnam Pyne, Saumyadipta |
author_facet | Dinu, Irina Wang, Xiaoming Kelemen, Linda E Vatanpour, Shabnam Pyne, Saumyadipta |
author_sort | Dinu, Irina |
collection | PubMed |
description | BACKGROUND: Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary phenotype. Equally, if not more importantly however, is the ability to test the enrichment of a gene signature or pathway against the continuous phenotypes which are routinely and commonly observed in, for example, clinicopathological measurements. It is not always easy or meaningful to dichotomize continuous phenotypes into two classes, and attempting to do this may lead to the inaccurate classification of samples, which would affect the downstream enrichment analysis. In the present study, we have build on recent efforts to incorporate correlation structure within gene sets and pathways into the GSA test statistic. To address the issue of continuous phenotypes directly without the need for artificial discrete classification and thus increase the power of the test while ensuring computational efficiency and rigor, new GSA methods that can incorporate a covariance matrix estimator for a continuous phenotype may present an effective approach. RESULTS: We have designed a new method by extending the GSA approach called Linear Combination Test (LCT) from a binary to a continuous phenotype. Simulation studies and a real microarray dataset were used to compare the proposed LCT for a continuous phenotype, a modification of LCT (referred to as LCT(2)), and two publicly available GSA methods for continuous phenotypes. CONCLUSIONS: We found that the LCT methods performed better than the other two GSA methods; however, this finding should be understood in the context of our specific simulation studies and the real microarray dataset that were used to compare the methods. Free R-codes to perform LCT for binary and continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html. The R-code to perform LCT for a continuous phenotype is available as Additional file 1. |
format | Online Article Text |
id | pubmed-3717275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37172752013-07-22 Linear combination test for gene set analysis of a continuous phenotype Dinu, Irina Wang, Xiaoming Kelemen, Linda E Vatanpour, Shabnam Pyne, Saumyadipta BMC Bioinformatics Methodology Article BACKGROUND: Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary phenotype. Equally, if not more importantly however, is the ability to test the enrichment of a gene signature or pathway against the continuous phenotypes which are routinely and commonly observed in, for example, clinicopathological measurements. It is not always easy or meaningful to dichotomize continuous phenotypes into two classes, and attempting to do this may lead to the inaccurate classification of samples, which would affect the downstream enrichment analysis. In the present study, we have build on recent efforts to incorporate correlation structure within gene sets and pathways into the GSA test statistic. To address the issue of continuous phenotypes directly without the need for artificial discrete classification and thus increase the power of the test while ensuring computational efficiency and rigor, new GSA methods that can incorporate a covariance matrix estimator for a continuous phenotype may present an effective approach. RESULTS: We have designed a new method by extending the GSA approach called Linear Combination Test (LCT) from a binary to a continuous phenotype. Simulation studies and a real microarray dataset were used to compare the proposed LCT for a continuous phenotype, a modification of LCT (referred to as LCT(2)), and two publicly available GSA methods for continuous phenotypes. CONCLUSIONS: We found that the LCT methods performed better than the other two GSA methods; however, this finding should be understood in the context of our specific simulation studies and the real microarray dataset that were used to compare the methods. Free R-codes to perform LCT for binary and continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html. The R-code to perform LCT for a continuous phenotype is available as Additional file 1. BioMed Central 2013-07-01 /pmc/articles/PMC3717275/ /pubmed/23815123 http://dx.doi.org/10.1186/1471-2105-14-212 Text en Copyright © 2013 Dinu et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Dinu, Irina Wang, Xiaoming Kelemen, Linda E Vatanpour, Shabnam Pyne, Saumyadipta Linear combination test for gene set analysis of a continuous phenotype |
title | Linear combination test for gene set analysis of a continuous phenotype |
title_full | Linear combination test for gene set analysis of a continuous phenotype |
title_fullStr | Linear combination test for gene set analysis of a continuous phenotype |
title_full_unstemmed | Linear combination test for gene set analysis of a continuous phenotype |
title_short | Linear combination test for gene set analysis of a continuous phenotype |
title_sort | linear combination test for gene set analysis of a continuous phenotype |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717275/ https://www.ncbi.nlm.nih.gov/pubmed/23815123 http://dx.doi.org/10.1186/1471-2105-14-212 |
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