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Reproducibility-Optimized Test Statistic for Ranking Genes in Microarray Studies

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4 Author(s)
Elo, L.L. ; Dept. of Math., Turku Univ., Turku ; Filen, S. ; Lahesmaa, R. ; Aittokallio, T.

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. Although previous studies on simulated or spike-in data sets do not provide practical guidance on how to choose the best method for a given real data set, we introduce an enhanced reproducibility-optimization procedure, which enables the selection of a suitable gene-ranking statistic directly from the data. In comparison with existing ranking methods, the reproducibility-optimized statistic shows good performance consistently under various simulated conditions and on Affymetrix spike-in data set. 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 data set without relying on a priori assumptions, which may bias the findings and their interpretation. Moreover, the general reproducibility-optimization procedure is not limited to detecting differential expression only but could be extended to a wide range of other applications as well.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:5 ,  Issue: 3 )