Abstract
BackgroundMeta-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.ResultsWe 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.ConclusionsExisting 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.
Highlights
Meta-analysis methods exist for combining multiple microarray datasets
Meta-analysis refers to an integrative data analysis method that traditionally is defined as a synthesis or at times review of results from datasets that are independent but related [1]
Our method aims to simulate real situations where an additional dataset would need to be classified after a discriminate rule was developed
Summary
Meta-analysis methods exist for combining multiple microarray datasets. 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. Due to extensive usage of microarray technology, in recent years there has been an explosion in publicly available datasets. Examples of such repositories include Gene Expression Omnibus Nih.gov/geo/), ArrayExpress http://www.ebi.ac.uk/microarray-as/ae/ and Stanford Microarray Database The use of these datasets is not exhausted, when used wisely they may yield a depth of information. Power can be added to an analysis, obtained by the increase in sample size of the study. This aids the ability of the analysis to find effects that exist and is termed ‘integration-driven discovery’ [2]. Meta-analysis can be important when studies have conflicting conclusions as they may estimate an average effect or highlight an important subtle variation [1,3]
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