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Open Access Methodology article

Assessing stability of gene selection in microarray data analysis

Xing Qiu, Yuanhui Xiao, Alexander Gordon and Andrei Yakovlev*

Author affiliations

Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA

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Citation and License

BMC Bioinformatics 2006, 7:50  doi:10.1186/1471-2105-7-50

Published: 1 February 2006

Abstract

Background

The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data.

Results

Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations.

Conclusion

Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.