ArticleBMC bioinformatics2009
Comparison of small n statistical tests of differential expression applied to microarrays.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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34 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Variance adaptive shrinkage (vash): flexible empirical Bayes estimation of variances.Bioinformatics (Oxford, England) · 2016Article
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- Transcriptome wide annotation of eukaryotic RNase III reactivity and degradation signals.PLoS genetics · 2015Article
- A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.PloS one · 2015Article
- Ras-mediated deregulation of the circadian clock in cancer.PLoS genetics · 2014Article
- Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects.Frontiers in cell and developmental biology · 2014Article
- Identification of significant features in DNA microarray data.Wiley interdisciplinary reviews. Computational statistics · 2013Article
- Separate-channel analysis of two-channel microarrays: recovering inter-spot information.BMC bioinformatics · 2013Article
- Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset.BMC genomics · 2013Article
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4 authors.
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Abstract
backgroundDNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data.
resultsThree Empirical Bayes methods (CyberT, BRB, and limma t-statistics) were the most effective statistical tests across simulated and both 2-colour cDNA and Affymetrix experimental data. The CyberT regularized t-statistic in particular was able to maintain expected false positive rates with simulated data showing high variances at low gene intensities, although at the cost of low true positive rates. The Local Pooled Error (LPE) test introduced a bias that lowered false positive rates below theoretically expected values and had lower power relative to the top performers. The standard two-sample t-test and fold change were also found to be sub-optimal for detecting differentially expressed genes. The generalized log transformation was shown to be beneficial in improving results with certain data sets, in particular high variance cDNA data.
conclusionPre-processing of data influences performance and the proper combination of pre-processing and statistical testing is necessary for obtaining the best results. All three Empirical Bayes methods assessed in our study are good choices for statistical tests for small n microarray studies for both Affymetrix and cDNA data. Choice of method for a particular study will depend on software and normalization preferences.
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