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Sample size calculation for multiple testing in microarray data analysis.

Publication ,  Journal Article
Jung, S-H; Bang, H; Young, S
Published in: Biostatistics
January 2005

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.

Duke Scholars

Published In

Biostatistics

DOI

ISSN

1465-4644

Publication Date

January 2005

Volume

6

Issue

1

Start / End Page

157 / 169

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Precursor Cell Lymphoblastic Leukemia-Lymphoma
  • Oligonucleotide Array Sequence Analysis
  • Leukemia, Myeloid, Acute
  • Humans
  • Gene Expression Regulation, Leukemic
  • Data Interpretation, Statistical
  • Computer Simulation
  • 4905 Statistics
 

Citation

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Jung, S.-H., Bang, H., & Young, S. (2005). Sample size calculation for multiple testing in microarray data analysis. Biostatistics, 6(1), 157–169. https://doi.org/10.1093/biostatistics/kxh026
Jung, Sin-Ho, Heejung Bang, and Stanley Young. “Sample size calculation for multiple testing in microarray data analysis.Biostatistics 6, no. 1 (January 2005): 157–69. https://doi.org/10.1093/biostatistics/kxh026.
Jung S-H, Bang H, Young S. Sample size calculation for multiple testing in microarray data analysis. Biostatistics. 2005 Jan;6(1):157–69.
Jung, Sin-Ho, et al. “Sample size calculation for multiple testing in microarray data analysis.Biostatistics, vol. 6, no. 1, Jan. 2005, pp. 157–69. Pubmed, doi:10.1093/biostatistics/kxh026.
Jung S-H, Bang H, Young S. Sample size calculation for multiple testing in microarray data analysis. Biostatistics. 2005 Jan;6(1):157–169.
Journal cover image

Published In

Biostatistics

DOI

ISSN

1465-4644

Publication Date

January 2005

Volume

6

Issue

1

Start / End Page

157 / 169

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Precursor Cell Lymphoblastic Leukemia-Lymphoma
  • Oligonucleotide Array Sequence Analysis
  • Leukemia, Myeloid, Acute
  • Humans
  • Gene Expression Regulation, Leukemic
  • Data Interpretation, Statistical
  • Computer Simulation
  • 4905 Statistics