Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies.

Uma Siangphoe, Kellie J Archer, Nitai D Mukhopadhyay

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Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Uma SiangphoeOffice of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. siangphoeu@vcu.edu.

Kellie J ArcherDivision of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.

Nitai D MukhopadhyayDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.

Funding

Informatic tools for predicting an ordinal response for high-dimensional dataR01LM011169 · NLM · VIRGINIA COMMONWEALTH UNIVERSITY · PI ARCHER, KELLIE J. · 2012 to 2015

$944k

Foundation for the National Institutes of Health R01LM011169NLM NIH HHS R01 LM011169

6 · The paper itself

Abstract

backgroundRandom-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets.

resultsSample quality adjusting within study variance (w

conclusionsThe application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.

Indexed as

Bayes TheoremGene ExpressionGene Expression ProfilingGenomeHumansMeta-Analysis as TopicResearch DesignAlzheimer’s diseaseBayesian random-effects modelGene expressionMeta-analysisRandom-effects modelSample quality weightsStudy heterogeneity

Identifiers

PMID30626315

DOI10.1186/s12859-018-2491-9

PMCPMC6327440

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