A Simulation Study on the Impact of Strong Dependence in High-Dimensional Multiple-Testing I: The Case without Effects

  • Antonio Carvajal-Rodríguez
  • Jacobo de Uña-Álvarez
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)


When working with high-dimensional biological data the so-called multiple hypothesis testing problem emerges. That is, when many separate tests are performed, several will be significant by chance provoking false positive results. Many statistical methods have been developed to deal with this problem. An important topic concerning multiple hypothesis testing efforts applied to high-throughput experiments is the intrinsic inter-dependency in gene effects. Here we simulate data resembling the testing scenario used in a well-known data set from breast cancer microarray studies. The objective of the study is to see the impact of high correlation within gene blocks onto the multiple-testing correction methods as Sequential Bonferroni (SB), Benjamini and Hochberg FDR (BH) and Sequential Goodness of Fit (SGoF).


Multiple testing microarrays false discovery rate FDR SGoF 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Carvajal-Rodríguez
    • 1
  • Jacobo de Uña-Álvarez
    • 2
  1. 1.Área de Genética Facultad de BiologíaUniversidad de VigoVigoSpain
  2. 2.Departamento de Estadística e Investigación Operativa Facultad de EconómicasUniversidad de VigoVigoSpain

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