Title

Approaches to multiplicity issues in complex research in microarray analysis

Document Type

Conference Proceeding

Publication Date

11-1-2006

Journal

Statistica Neerlandica

Volume

60

Issue

4

DOI

10.1111/j.1467-9574.2006.00343.x

Keywords

False discovery rate; Hierarchical testing; High throughput analysis

Abstract

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. © 2006 VVS.

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