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

Chang, LC and Lin, HM and Sibille, E and Tseng, GC (2013) Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline. BMC Bioinformatics, 14 (1).

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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. © 2013 Chang et al.; licensee BioMed Central Ltd.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chang, LC
Lin, HMhul27@pitt.eduHUL27
Sibille, E
Tseng, GCctseng@pitt.eduCTSENG
Date: 21 December 2013
Date Type: Publication
Journal or Publication Title: BMC Bioinformatics
Volume: 14
Number: 1
DOI or Unique Handle: 10.1186/1471-2105-14-368
Schools and Programs: Graduate School of Public Health > Biostatistics
Graduate School of Public Health > Human Genetics
School of Medicine > Psychiatry
Refereed: Yes
Date Deposited: 02 Dec 2016 20:17
Last Modified: 01 Mar 2019 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/29626

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