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An Adaptive Single‐step FDR Procedure with Applications to DNA Microarray Analysis
Author(s) -
Iyer Vishwanath,
Sarkar Sanat
Publication year - 2007
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200610316
Subject(s) - false discovery rate , multiple comparisons problem , thresholding , false positive paradox , computer science , independence (probability theory) , mathematics , algorithm , statistics , data mining , artificial intelligence , gene , biochemistry , chemistry , image (mathematics)
The use of multiple hypothesis testing procedures has been receiving a lot of attention recently by statisticians in DNA microarray analysis. The traditional FWER controlling procedures are not very useful in this situation since the experiments are exploratory by nature and researchers are more interested in controlling the rate of false positives rather than controlling the probability of making a single erroneous decision. This has led to increased use of FDR (False Discovery Rate) controlling procedures. Genovese and Wasserman proposed a single‐step FDR procedure that is an asymptotic approximation to the original Benjamini and Hochberg stepwise procedure. In this paper, we modify the Genovese‐Wasserman procedure to force the FDR control closer to the level alpha in the independence setting. Assuming that the data comes from a mixture of two normals, we also propose to make this procedure adaptive by first estimating the parameters using the EM algorithm and then using these estimated parameters into the above modification of the Genovese‐Wasserman procedure. We compare this procedure with the original Benjamini‐Hochberg and the SAM thresholding procedures. The FDR control and other properties of this adaptive procedure are verified numerically. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)