Analysis of DNA microarray expression data

Biometric Research Branch, Division of Cancer Treatment & Diagnosis, National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA.
Best practice & research. Clinical haematology (Impact Factor: 2.12). 07/2009; 22(2):271-82. DOI: 10.1016/j.beha.2009.07.001
Source: PubMed


DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. Microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling.

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Available from: Richard Simon, Sep 17, 2014
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    • "Developing predictive and prognostic classifiers to recognize the patient highly requires action and forms as the most excellent candidate form for specific treatments. As microarrays construct as much of data from every specimen, [2] the method provides with the greater opportunity for discovering huge dangers on misleading claims. DNA microarrays provide enormous occasion for discovery and progress of predictive oncology but with a greater tradeoff value between the opportunity and mounting false claims. "
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    06/2014; 2014:357873. DOI:10.1155/2014/357873
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