NDLI: Reproducibility-Optimized Test Statistic for Ranking Genes in Microarray Studies

Content Provider	ACM Digital Library
Author	Lahesmaa, Riitta Filen, Sanna Elo, Laura L. Aittokallio, Tero
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.
Starting Page	423
Ending Page	431
Page Count	9
File Format	PDF
ISSN	15455963
DOI	10.1109/tcbb.2007.1078
Volume Number	5
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Microarray, gene expression, gene ranking, reproducibility, differential expression, bootstrap
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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