Approaches to multiplicity issues in complex research in microarray analysis

Statistica Neerlandica - Tập 60 Số 4 - Trang 414-437 - 2006
Daniel Yekutieli1,2, Anat Reiner‐Benaim1, Yoav Benjamini1, Gregory I. Elmer3, Neri Kafkafi3, Noah Letwin4, Norman H. Lee4
1Department of Statistics and Operations Research, Sackler Faculty of Exact Sciences, Tel-Aviv University, Ramat Aviv, P.O.B. 39040, Tel Aviv 61390, Israel
2[email protected]
3Maryland Psychiatric Research Center, University of Maryland, Baltimore, MO, USA
4Department of Functional Genomics, The Institute for Genomic Research, Maryland & The George Washington University Medical Center, Washington. DC, USA

Tóm tắt

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.

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Statistica Neerlandica

Tập 60 Số 4

414-437

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