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Comparative Study
. 2010 Aug 3:11:408.
doi: 10.1186/1471-2105-11-408.

Comparison study of microarray meta-analysis methods

Anna Campain  1 , Yee Hwa Yang
Affiliations
Comparative Study

Comparison study of microarray meta-analysis methods

Anna Campain et al. BMC Bioinformatics. .

Abstract

Background: Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.

Results: We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable.

Conclusions: Existing meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.

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Figures

Figure 1
Figure 1
ROC curves for simulation. ROC curves for differing meta-analysis methods. GeneMeta, RankProd, POE with Bss/Wss and POE with IC appear to struggle with obtaining an accurate 'true' DE list, Fisher and mDEDS perform competitively.
Figure 2
Figure 2
Breast cancer classification. Plots of error rates in the binary classification of three breast cancer datasets as the number of genes used to build the classifier varies from 10 to 500. Classification error rates are displayed for the 8 different meta-analysis approaches. Plots are split into two sub-plots for reading ease, mDEDS appears in both for comparative purposes.
Figure 3
Figure 3
Lymphoma cancer classification. Plots of error rates in the binary classification of three lymphoma cancer datasets as number of feature used in classification varies from 10 to 500. Classification error rates are displayed for the 8 different meta-analysis approaches. Plots are split into two sub-plots for reading ease, mDEDS appears in both for comparative purposes.
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