Abstract
High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data.
Highlights
During the last decade, advances in Molecular Biology and substantial improvements in microarray technology have led biologists toward high-throughput genomic studies
The type-I error-rate is often referred to as false-positive rate. It differs from the false-discovery rate (FDR) in the sense that it represents the rate that truly null features are called significant whereas the FDR is the rate that significant features are truly null [21]
Gene lists resulting from the control-test are clearly independent from the other ones, since it selects genes uniformly
Summary
Advances in Molecular Biology and substantial improvements in microarray technology have led biologists toward high-throughput genomic studies. The use of microarrays to discover genes differentially expressed between two or more groups (patients versus controls for instance) has found many applications. These include the identification of disease biomarkers that may be important in the diagnosis of the different types and subtypes of diseases, with several implications in terms of prognostic and therapy [7,8]. FC lacks of a solid statistical footing [9]: it does not take the variance of the samples into account This point is especially problematic since variability in gene expression measurements is partially gene-specific, even after the variance has been stabilized by data transformation [10,11]
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