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An Investigation on Performance of Significance Analysis of Microarray (SAM) for the Comparisons of Several Treatments with one Control in the Presence of Small‐variance Genes
Author(s) -
Lin D.,
Shkedy Z.,
Burzykowski T.,
Ion R.,
Göhlmann H. W. H.,
Bondt A. De,
Perer T.,
Geerts T.,
Van den Wyngaert I.,
Bijnens L.
Publication year - 2008
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.200710467
Subject(s) - false discovery rate , multiple comparisons problem , microarray , significance analysis of microarrays , variance (accounting) , computational biology , microarray analysis techniques , dna microarray , gene , statistics , computer science , mathematics , biology , data mining , bioinformatics , genetics , gene expression , accounting , business
Abstract One of multiple testing problems in drug finding experiments is the comparison of several treatments with one control. In this paper we discuss a particular situation of such an experiment, i.e., a microarray setting, where the many‐to‐one comparisons need to be addressed for thousands of genes simultaneously. For a gene‐specific analysis, Dunnett's single step procedure is considered within gene tests, while the FDR controlling procedures such as Significance Analysis of Microarrays (SAM) and Benjamini and Hochberg (BH) False Discovery Rate (FDR) adjustment are applied to control the error rate across genes. The method is applied to a microarray experiment with four treatment groups (three microarrays in each group) and 16,998 genes. Simulation studies are conducted to investigate the performance of the SAM method and the BH‐FDR procedure with regard to controlling the FDR, and to investigate the effect of small‐variance genes on the FDR in the SAM procedure. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)