Title

False discovery rate paradigms for statistical analyses of microarray gene expression data

Authors

Cheng Cheng^* and Stan Pounds

Affiliation

Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN

Email

Cheng.Cheng@STJUDE.ORG; * Corresponding author

Article Type

Current Trends

Date

received December 05, 2006; accepted February 02, 2007; published online April 10, 2007

Abstract

The microarray gene expression applications have greatly stimulated the statistical research on the massive multiple hypothesis tests problem. There is now a large body of literature in this area and basically five paradigms of massive multiple tests: control of the false discovery rate (FDR), estimation of FDR, significance threshold criteria, control of family-wise error rate (FWER) or generalized FWER (gFWER), and empirical Bayes approaches. This paper contains a technical survey of the developments of the FDR-related paradigms, emphasizing precise formulation of the problem, concepts of error measurements, and considerations in applications. The goal is not to do an exhaustive literature survey, but rather to review the current state of the field.

Keywords

multiple tests; false discovery rate; q-value; significance threshold selection; profile information criterion; microarray; gene expression

Citation

Cheng & Pounds, Bioinformation 1(10): 436-446 (2007)

Edited by

Susmita Datta

ISSN

0973-2063

Publisher

Biomedical Informatics Publishing Group

Copyright

Publisher

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.