Microarray experimental design: power and sample size considerations.

Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.

[1]  S. Dudoit,et al.  Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. , 2000, Genome research.

[2]  D. Rom,et al.  Testing for individual and population equivalence based on the proportion of similar responses. , 1996, Statistics in medicine.

[3]  R. W. Doerge,et al.  Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments , 2002, Bioinform..

[4]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Juliane Fluck,et al.  Microarrays: How Many Do You Need? , 2003, J. Comput. Biol..

[6]  R. Littell.  SAS System for Mixed Models , 1996 .

[7]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.

[8]  Carl J. Huberty,et al.  Group Overlap as a Basis for Effect Size , 2000 .

[9]  George Stephanopoulos,et al.  Determination of minimum sample size and discriminatory expression patterns in microarray data , 2002, Bioinform..

[10]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[11]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[12]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[13]  W. Pan,et al.  How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach , 2002, Genome Biology.

[14]  Samuel S. Wu,et al.  A statistical method for flagging weak spots improves normalization and ratio estimates in microarrays. , 2001, Physiological genomics.

[15]  P. Brown,et al.  A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. , 1996, Genome research.

[16]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[17]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.