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Topics in Biostatistics

Volume 404 of the series Methods in Molecular Biology™ pp 409-430

Microarray Analysis

  • Grier P. PageAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham
  • , Stanislav O. ZakharkinAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham
  • , Kyoungmi KimAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham
  • , Tapan MehtaAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham
  • , Lang ChenAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham
  • , Kui ZhangAffiliated withDepartment of Biostatistics, University of Alabama at Birmingham

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Abstract

Microarrays and related technologies have allowed investigators to ask biological questions in far greater detail than has previously been possible. Microarrays had a troubled beginning, but most of these problems resulted from the growing pains of this technology, which, like many new things, was initially more promise than delivery. Nevertheless, over the past few years, investigators have learned how to achieve optimal performance of technology, and now exciting discoveries are made using microarray-based research. Many of the advances have come from the realization that microarrays are not a magic tool but rather are like any other measurement device. Unless microarray experimentation is coupled with good experimental practices, it will not yield valid results or, worse yet, may lead to misleading results. In this chapter, we highlight some of the important steps that should be taken to successfully conduct a microarray study. These steps include a clearly stated biological question, experimental design, careful experimental conduct, complete statistical analysis, validation/verification of results, and dissemination of the data.

Key Words

Experimental design microarray plants statistical analysis