Abstract Details
Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #317480
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View Presentation
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Title:
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Estimation of Random Effects in Meta-Analysis of Gene Expression Studies
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Author(s):
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Uma Siangphoe* and Kellie J. Archer
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Companies:
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Virginia Commonwealth University and Virginia Commonwealth University
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Keywords:
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gene expression ;
meta-analysis ;
random effects
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Abstract:
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Combining effect sizes from individual studies using random effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. A high heterogeneity of effect sizes can reduce statistical power of the models. We propose a new weighted estimate based on a Gaussian linear mixed-model to estimate and test for significance of random effects for individual genes and applied the method to perform an Alzheimer's gene expression meta-analysis. This method provided a lower minimum sum of squared error than standard methods. We also examined the strength of study heterogeneity to compare gene variation among meta-analyses. Estimation methods used included unrestricted and restricted maximum likelihood as well as robust estimation to account for outliers and skewed distributions. A permutation method was applied to generate z-statistics for both common and random effects estimates and a modified Benjamini and Hochberg's method was used to control the false discovery rate. We demonstrate our method has relatively better precision among the four methods compared.
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Authors who are presenting talks have a * after their name.
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