An adaptive alpha spending algorithm improves the power of statistical inference in microarray data analysis


Jacob P.L. Brand1, 2, 3*, Lang Chen3, Xiangqin Cui3, Alfred A. Bartolucci3, Grier P. Page3, Kyoungmi Kim3, 4, Stephen Barnes5, Vinodh Srinivasasainagendra3, Mark T. Beasley3 and David. B. Allison3, 6



1Genomic Technologies Section - Research Technology Branch, NIH / NIAID, 50 South Drive, Room 5505, Bethesda, MD 20892-8005; 2Pennington Biomedical Research Center, Human Genomics Laboratory, Louisiana State University System, 6400 Perkins Rd, Baton Rouge, La 70808; 3Department of Biostatistics, 1665 University Boulevard, Ryals Public Health Building ,Birmingham, AL 35294-0022; 4Department of Public Health Sciences, One Shields Avenue, TB-168, University of California, Davis, Davis, California 95616-8638; 5Department of Pharmacology and Toxicology, 452 McCallum Research Building, University of Alabama at Birmingham,1918 University Boulevard, Birmingham, Alabama; 6Center for Research on Clinical Nutrition, University of Alabama at Birmingham, Birmingham, Alabama


Email;* Corresponding author


Article Type

Prediction Model



received October 10, 2006; accepted October 21, 2006; published online April 10, 2007



The adaptive alpha-spending algorithm incorporates additional contextual evidence (including correlations among genes) about differential expression to adjust the initial p-values to yield the alpha-spending adjusted p-values. The alpha-spending algorithm is named so because of its similarity with the alpha-spending algorithm in interim analysis of clinical trials in which stage-specific significance levels are assigned to each stage of the clinical trial. We show that the Bonferroni correction applied to the alpha-spending adjusted p-values approximately controls the Family Wise Error Rate under the complete null hypothesis. Using simulations we also show that the use of the alpha spending algorithm yields increased power over the unadjusted p-values while controlling FDR. We found the greater benefits of the alpha spending algorithm with increasing sample sizes and correlation among genes. The use of the alpha spending algorithm will result in microarray experiments that make more efficient use of their data and may help conserve resources.




microarray data; contextual evidence; adaptive alpha spending


Brand et al., Bioinformation 1(10): 384-389 (2007)

Edited by

Susmita Datta






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.