A simple method for assessing sample sizes in microarray experiments

Author:

Tibshirani Robert

Abstract

Abstract Background In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments. Results Our method starts with the output from a permutation-based analysis for a set of pilot data, e.g. from the SAM package. Then for a given hypothesized mean difference and various samples sizes, we estimate the false discovery rate and false negative rate of a list of genes; these are also interpretable as per gene power and type I error. We also discuss application of our method to other kinds of response variables, for example survival outcomes. Conclusion Our method seems to be useful for sample size assessment in microarray experiments.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Reference8 articles.

1. Gilbert Chu, Balasubramanian Narasimhan, Robert Tibshirani, Virginia Tusher: Significance analysis of microarrays (sam) software.[http://www-stat.stanford.edu/~tibs/SAM/]

2. Lee M-LT, Whitmore GA: Power and sample size for microarray studies. Statistics in Medicine 2002, (21):3543–3570. 10.1002/sim.1335

3. Li SS, Bigler J, Lampe JW, Potter JD, Feng Z: Fdr-controlling testing procedures and sample size determination for microarrays. Statistics in Medicine 2005, (24):2267–2280. 10.1002/sim.2119

4. Muller P, Parmigiani G, Robert C, Rousseau J: Optimal sample size for multiple testing: the case of gene expression microarrays. J Amer Statist Assoc 2005, 99: 990–1001. 10.1198/016214504000001646

5. Pawitan Y, Michiels S, Koscielny A, Gusnanto S, Ploner A: False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 2005, (21):3017–24. 10.1093/bioinformatics/bti448

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