Abstract
BackgroundGene expression profiling experiments with few replicates lead to great variability in the estimates of gene variances. Toward this end, several moderated t-test methods have been developed to reduce this variability and to increase power for testing differential expression. Most of these moderated methods are based on linear models with fixed effects where residual variances are smoothed under a hierarchical Bayes framework. However, they are inadequate for designs with complex correlation structures, therefore application of moderated methods to linear models with mixed effects are needed for differential expression analysis.ResultsWe demonstrated the implementation of the fully moderated t-statistic method for linear models with mixed effects, where both residual variances and variance estimates of random effects are smoothed under a hierarchical Bayes framework. We compared the proposed method with two current moderated methods and show that the proposed method can control the expected number of false positives at the nominal level, while the two current moderated methods fail.ConclusionsWe proposed an approach for testing differential expression under complex correlation structures while providing variance shrinkage. The proposed method is able to improve power by moderation and controls the expected number of false positives properly at the nominal level.
Highlights
Gene expression profiling experiments with few replicates lead to great variability in the estimates of gene variances
Parameter setting For simulation, we chose a simple design for gene expression experiment without loss of generality
We simulated gene expression data under linear mixedeffects models, where subject is a random effect and variation due to technical replicates was included in the residual errors
Summary
Gene expression profiling experiments with few replicates lead to great variability in the estimates of gene variances. Several moderated t-test methods have been developed to reduce this variability and to increase power for testing differential expression Most of these moderated methods are based on linear models with fixed effects where residual variances are smoothed under a hierarchical Bayes framework. To understand the dynamics of gene function, many functional genomic studies have profiled transcriptomic data of individuals with multiple samples of different origins [1, 2], diverse cell types [3, 4], and various time points [5,6,7] These measurements of gene expression levels from multiple samples of the same individual are correlated in nature. Currently there is a lack of methods that use variance shrinkage techniques for linear mixed-effects models
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