Microarrays Research Today is a free monthly online journal that collates and summarizes the latest research about Microarrays, including details on experiments, designs, statistics, analysis, software.

Assessing Stability of Gene Selection in Microarray Data Analysis.

Qiu X, Xiao Y, Gordon A, Yakovlev A

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 cross-validation techniques we have found that some genes are selected much less frequently (across cross-validation samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself depends on a specific multiple testing procedure and the choice of a test statistic. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations. CONCLUSIONS: Cross-validation represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using cross-validation 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.

Published 2 February 2006 in BMC Bioinformatics, 7(1): 50.
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