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CGHpower: exploring sample size calculations for chromosomal copy number experiments

Ilari Scheinin123, José A Ferreira4, Sakari Knuutila2, Gerrit A Meijer1, Mark A van de Wiel45 and Bauke Ylstra1*

Author Affiliations

1 Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands

2 Department of Pathology, Haartman Institute and HUSLAB, University of Helsinki and Helsinki University Central Hospital, Finland

3 FIMM Technology Centre, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland

4 Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands

5 Department of Mathematics, Vrije Universiteit, Amsterdam, The Netherlands

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BMC Bioinformatics 2010, 11:331  doi:10.1186/1471-2105-11-331

Published: 17 June 2010

Abstract

Background

Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH).

Results

Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments.

Conclusions

The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case.

The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/ webcite