Statistical considerations for analysis of microarray experiments.
Microarray technologies enable the simultaneous interrogation of expressions from thousands of genes from a biospecimen sample taken from a patient. This large set of expressions generates a genetic profile of the patient that may be used to identify potential prognostic or predictive genes or genetic models for clinical outcomes. The aim of this article is to provide a broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials. An emphasis will be placed on how the lack of understanding and improper use of statistical concepts and methods will lead to noise discovery and misinterpretation of experimental results.
Department of Biostatistics and Bioinformatics, Duke University CALGB Statistical Center, Duke University, Durham, North Carolina, USA.
Kouros
K
Owzar
William T
WT
Barry
Sin-Ho
SH
Jung
D000465:Algorithms; D002986:Clinical Trials as Topic; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D015973:Gene Expression Regulation, Leukemic; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers / Q000235:genetics; D006801:Humans; D007938:Leukemia / Q000378:metabolism; D008175:Lung Neoplasms / Q000175:diagnosis / Q000378:metabolism; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010641:Phenotype; D011379:Prognosis; D012984:Software
D005819:Genetic Markers