Work

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

Year: 2007

Type: article

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.

Source: Bioinformation

DOI: 10.6026/97320630001436

Authors: Cheng Cheng, Stan Pounds

Institution: St. Jude Children's Research Hospital

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Funders: American Lebanese Syrian Associated Charities, National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences