Dataset Information

Microarray-based gene set analysis: a comparison of current methods.

ABSTRACT:

Background

The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.

Results

Each of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.

Conclusion

Based on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics.

SUBMITTER: Song S

PROVIDER: S-EPMC2607289 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Publications

Microarray-based gene set analysis: a comparison of current methods.

Song Sarah S Black Michael A MA

BMC bioinformatics 20081127

<h4>Background</h4>The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collec ...[more]

PMID: 19038052

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Project description:BackgroundMultiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated with a binary phenotype. Following Goeman and Bühlmann's recent review, we compared statistical performance of three methods, namely Global Test, ANCOVA Global Test, and SAM-GS, that test "self-contained null hypotheses" Via. subject sampling. The three methods were compared based on a simulation experiment and analyses of three real-world microarray datasets.ResultsIn the simulation experiment, we found that the use of the asymptotic distribution in the two Global Tests leads to a statistical test with an incorrect size. Specifically, p-values calculated by the scaled chi2 distribution of Global Test and the asymptotic distribution of ANCOVA Global Test are too liberal, while the asymptotic distribution with a quadratic form of the Global Test results in p-values that are too conservative. The two Global Tests with permutation-based inference, however, gave a correct size. While the three methods showed similar power using permutation inference after a proper standardization of gene expression data, SAM-GS showed slightly higher power than the Global Tests. In the analysis of a real-world microarray dataset, the two Global Tests gave markedly different results, compared to SAM-GS, in identifying pathways whose gene expressions are associated with p53 mutation in cancer cell lines. A proper standardization of gene expression variances is necessary for the two Global Tests in order to produce biologically sensible results. After the standardization, the three methods gave very similar biologically-sensible results, with slightly higher statistical significance given by SAM-GS. The three methods gave similar patterns of results in the analysis of the other two microarray datasets.ConclusionAn appropriate standardization makes the performance of all three methods similar, given the use of permutation-based inference. SAM-GS tends to have slightly higher power in the lower alpha-level region (i.e. gene sets that are of the greatest interest). Global Test and ANCOVA Global Test have the important advantage of being able to analyze continuous and survival phenotypes and to adjust for covariates. A free Microsoft Excel Add-In to perform SAM-GS is available from http://www.ualberta.ca/~yyasui/homepage.html.

Project description:BackgroundGene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).ResultsUsing a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53.ConclusionWe conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.

Dataset Information

Microarray-based gene set analysis: a comparison of current methods.

Background

Results

Conclusion

Publications

Microarray-based gene set analysis: a comparison of current methods.

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