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

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 met...

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Autores principales: Chang, Lun-Ching, Lin, Hui-Min, Sibille, Etienne, Tseng, George C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898528/
https://www.ncbi.nlm.nih.gov/pubmed/24359104
http://dx.doi.org/10.1186/1471-2105-14-368
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author Chang, Lun-Ching
Lin, Hui-Min
Sibille, Etienne
Tseng, George C
author_facet Chang, Lun-Ching
Lin, Hui-Min
Sibille, Etienne
Tseng, George C
author_sort Chang, Lun-Ching
collection PubMed
description 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) HS( A ): DE genes with non-zero effect sizes in all studies, (2) HS( B ): DE genes with non-zero effect sizes in one or more studies and (3) HS( r ): 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 (HS( A ), HS( B ), and HS( r )). 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.
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spelling pubmed-38985282014-02-05 Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline Chang, Lun-Ching Lin, Hui-Min Sibille, Etienne Tseng, George C BMC Bioinformatics Research Article 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) HS( A ): DE genes with non-zero effect sizes in all studies, (2) HS( B ): DE genes with non-zero effect sizes in one or more studies and (3) HS( r ): 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 (HS( A ), HS( B ), and HS( r )). 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. BioMed Central 2013-12-21 /pmc/articles/PMC3898528/ /pubmed/24359104 http://dx.doi.org/10.1186/1471-2105-14-368 Text en Copyright © 2013 Chang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chang, Lun-Ching
Lin, Hui-Min
Sibille, Etienne
Tseng, George C
Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title_full Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title_fullStr Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title_full_unstemmed Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title_short Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
title_sort meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898528/
https://www.ncbi.nlm.nih.gov/pubmed/24359104
http://dx.doi.org/10.1186/1471-2105-14-368
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