Spring Seminar Series - April 28, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
Sample Size Calculation for Multiple Testing in Microarray Data
Analysis
Heejung Bang
Division of Biostatistics and Epidemiology
Cornell University
Thursday, April 28, 2005
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
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 found 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.