A gene selection method for GeneChip array data with small sample sizes

Chen, Zhongxue
Liu, Qingzhong
McGee, Monnie
Kong, Megan
Huang, Xudong
Deng, Youpin
Scheuermann, Richard H.
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In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results: We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion: Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.

This article was originally published in BMC Genomics in 2011. doi:10.1186/1471-2164-12-S5-S7
microarray experiments, permutation-based methods, information shared among genes, false discovery rate (FDR), gene selection
Chen et al.: A gene selection method for GeneChip array data with small sample sizes. BMC Genomics 2011 12(Suppl 5):S7. doi:10.1186/1471-2164-12-S5-S7