title: Reproducibility-optimized test statistic for ranking genes in microarray studies.
abstract: A principal goal of microarray studies is to identify the genes showing differential expression under distinct conditions. In such studies, the selection of an optimal test statistic is a crucial challenge, which depends on the type and amount of data under analysis. While previous studies on simulated or spike-in datasets do not provide practical guidance on how to choose the best method for a given real dataset, we introduce an enhanced reproducibility-optimization procedure, which enables the selection of a suitable gene- anking statistic directly from the data. In comparison with existing ranking methods, the reproducibilityoptimized statistic shows good performance consistently under various simulated conditions and on Affymetrix spike-in dataset. Further, the feasibility of the novel statistic is confirmed in a practical research setting using data from an in-house cDNA microarray study of asthma-related gene expression changes. These results suggest that the procedure facilitates the selection of an appropriate test statistic for a given dataset without relying on a priori assumptions, which may bias the findings and their interpretation. Moreover, the general reproducibilityoptimization procedure is not limited to detecting differential expression only but could be extended to a wide range of other applications as well.
authors.forename: Laura L
authors.initials: LL
authors.lastname: Elo
authors.forename: Sanna
authors.initials: S
authors.lastname: Filen
authors.forename: Riitta
authors.initials: R
authors.lastname: Lahesmaa
authors.forename: Tero
authors.initials: T
authors.lastname: Aittokallio
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005796:Genes / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity