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Open Access Highly Accessed Research article

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline

Lun-Ching Chang1, Hui-Min Lin1, Etienne Sibille2 and George C Tseng13*

Author Affiliations

1 Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

2 Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

3 Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

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BMC Bioinformatics 2013, 14:368  doi:10.1186/1471-2105-14-368

Published: 21 December 2013

Abstract

Background

As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.

Results

We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HSA: DE genes with non-zero effect sizes in all studies, (2) HSB: DE genes with non-zero effect sizes in one or more studies and (3) HSr: DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.

Conclusions

The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HSA, HSB, and HSr). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website.