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Comparative Study
. 2007 Nov 7:8:431.
doi: 10.1186/1471-2105-8-431.

Comparative evaluation of gene-set analysis methods

Qi Liu  1 , Irina Dinu, Adeniyi J Adewale, John D Potter, Yutaka Yasui
Affiliations
Comparative Study

Comparative evaluation of gene-set analysis methods

Qi Liu et al. BMC Bioinformatics. .

Abstract

Background: Multiple 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.

Results: In 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.

Conclusion: An 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.

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Figures

Figure 1
Figure 1
The results of the simulation experiment, evaluating power of the three tests before the standardization, for correlation of 0 among 40 genes.
Figure 2
Figure 2
The results of the simulation experiment, evaluating power of the three tests after the standardization, for correlation of 0 among 40 genes.
Figure 3
Figure 3
The results of the simulation experiment, evaluating power of the three tests before the standardization, for correlation of 0.5 among 40 genes.
Figure 4
Figure 4
The results of the simulation experiment, evaluating power of the three tests after the standardization, for correlation of 0.5 among 40 genes.
Figure 5
Figure 5
The results of the simulation experiment, evaluating power of the three tests before the standardization, for correlation of 0.9 among 40 genes.
Figure 6
Figure 6
The results of the simulation experiment, evaluating power of the three tests after the standardization, for correlation of 0.9 among 40 genes.
Figure 7
Figure 7
P-values of 308 gene sets in the p53 data analysis: p-values of Global Test and ANCOVA Global Test after standardization vs. SAM-GS p-values before the standardization. The line indicates equal p-values between SAM-GS and Global Tests.
Figure 8
Figure 8
Lowest P-values in the p53 data analysis: p-values of Global Test and ANCOVA Global Test after standardization vs. SAM-GS p-values before the standardization. The line indicates equal p-values between SAM-GS and Global Tests.
Figure 9
Figure 9
Lowest P-values in the p53 data analysis: p-values of Global Test and ANCOVA Global Test after the VSN normalization vs. SAM-GS p-values after the VSN normalization. The line indicates equal p-values between SAM-GS and Global Tests.
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