Abstract
Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.
Highlights
Microarray technology is widely used to examine the activity level of thousands of genes simultaneously in human cells to better understand differential gene activation across diseases, such as heart diseases, infectious diseases, mental illness, and health disparities across ethnic groups
They further modified the penalized t-statistics through a parametric empirical Bayes approach using a simple mixture of normal models and a conjugate prior, and showed that their empirical Bayes method had both lower Type I error rates and Type II error rates compared to the penalized t-statistics
To reduce false discovery rates for nonnormally distributed microarray data, we propose a novel approach combining resampling with empirical Bayes methods: Resampling-based empirical Bayes Methods (RBMs)
Summary
Microarray technology is widely used to examine the activity level of thousands of genes simultaneously in human cells to better understand differential gene activation across diseases, such as heart diseases, infectious diseases, mental illness, and health disparities across ethnic groups. Genes with small sample variances are more likely to have large t-statistics even when they are not differentially expressed Both Tusher et al [4] and Efron et al [5] modified the ordinary t-statistic to have penalized t-statistics by adding a penalty to the standard deviation. Lonnstedt and Speed [6] showed that both forms of penalized t-statistics were far superior to the ordinary tstatistic for selecting differentially expressed genes They further modified the penalized t-statistics through a parametric empirical Bayes approach using a simple mixture of normal models and a conjugate prior, and showed that their empirical Bayes method had both lower Type I error rates and Type II error rates compared to the penalized t-statistics
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
