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Approaches to multiplicity issues in complex research in microarray analysis
Author(s) -
Yekutieli Daniel,
ReinerBenaim Anat,
Benjamini Yoav,
Elmer Gregory I.,
Kafkafi Neri,
Letwin Noah E.,
Lee Norman H.
Publication year - 2006
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2006.00343.x
Subject(s) - false discovery rate , multiple comparisons problem , multiplicity (mathematics) , statistical hypothesis testing , computer science , scalability , statistical analysis , data mining , computational biology , mathematics , biology , gene , statistics , genetics , mathematical analysis , database
The multiplicity problem is evident in the simplest form of statistical analysis of gene expression data – the identification of differentially expressed genes. In more complex analysis, the problem is compounded by the multiplicity of hypotheses per gene. Thus, in some cases, it may be necessary to consider testing millions of hypotheses. We present three general approaches for addressing multiplicity in large research problems. (a) Use the scalability of false discovery rate (FDR) controlling procedures; (b) apply FDR‐controlling procedures to a selected subset of hypotheses; (c) apply hierarchical FDR‐controlling procedures. We also offer a general framework for ensuring reproducible results in complex research, where a researcher faces more than just one large research problem. We demonstrate these approaches by analyzing the results of a complex experiment involving the study of gene expression levels in different brain regions across multiple mouse strains.