A statistical framework for the design of microarray experiments and effective detection of differential gene expression

Shu-Dong Zhang, T.W. Gant

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

Motivation: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment.
Original languageEnglish
Pages (from-to)2821-2828
Number of pages8
JournalBioinformatics
Volume20
Issue number16
DOIs
Publication statusPublished - 01 Nov 2004

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computational Theory and Mathematics
  • Computer Science Applications

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