Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods.
Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefit...
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doaj-116d20bef5684f2b92362fa882ff4fbb2020-11-24T22:16:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-09-015910.1371/journal.pone.0012693Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods.Brooke L FridleyGregory D JenkinsJoanna M BiernackaGene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher's method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.http://europepmc.org/articles/PMC2941449?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Brooke L Fridley Gregory D Jenkins Joanna M Biernacka |
spellingShingle |
Brooke L Fridley Gregory D Jenkins Joanna M Biernacka Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. PLoS ONE |
author_facet |
Brooke L Fridley Gregory D Jenkins Joanna M Biernacka |
author_sort |
Brooke L Fridley |
title |
Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
title_short |
Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
title_full |
Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
title_fullStr |
Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
title_full_unstemmed |
Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
title_sort |
self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2010-09-01 |
description |
Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher's method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits. |
url |
http://europepmc.org/articles/PMC2941449?pdf=render |
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