Multiple testing and its applications to microarrays.

The large-scale multiple testing problems resulting from the measurement of thousands of genes in microarray experiments have received increasing interest during the past several years. This article describes some commonly used criteria for controlling false positive errors, including familywise err...

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Publicado en:Statistical Methods in Medical Research Vol. 18; no. 6; pp. 543 - 564
Autores principales: Ge Y, Sealfon SC, Speed TP, Ge, Yongchao, Sealfon, Stuart C, Speed, Terence P
Formato: research Journal Article
Publicado: Sage Publications Inc. Dec2009
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Multiple testing and its applications to microarrays.
      aug:
        au:
          Ge Y
          Sealfon SC
          Speed TP
          Ge, Yongchao
          Sealfon, Stuart C
          Speed, Terence P
        affil: Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, NY 10029, USA
      sug:
        subj:
          Algorithms
          False Positive Results
          Biochips
          Data Analysis, Statistical
          Genetic Techniques Methods
          Models, Statistical
      ab: The large-scale multiple testing problems resulting from the measurement of thousands of genes in microarray experiments have received increasing interest during the past several years. This article describes some commonly used criteria for controlling false positive errors, including familywise error rates, false discovery rates and false discovery proportion rates. Various statistical methods controlling these error rates are described. The advantages and disadvantages of these methods are discussed. These methods are applied to gene expression data from two microarray studies and the properties of these multiple testing procedures are compared.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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