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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Research

The impact of sample imbalance on identifying differentially expressed genes

Kun Yang, Jianzhong Li* and Hong Gao

Author Affiliations

Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China

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BMC Bioinformatics 2006, 7(Suppl 4):S8  doi:10.1186/1471-2105-7-S4-S8

Published: 12 December 2006

Abstract

Background

Recently several statistical methods have been proposed to identify genes with differential expression between two conditions. However, very few studies consider the problem of sample imbalance and there is no study to investigate the impact of sample imbalance on identifying differential expression genes. In addition, it is not clear which method is more suitable for the unbalanced data.

Results

Based on random sampling, two evaluation models are proposed to investigate the impact of sample imbalance on identifying differential expression genes. Using the proposed evaluation models, the performances of six famous methods are compared on the unbalanced data. The experimental results indicate that the sample imbalance has a great influence on selecting differential expression genes. Furthermore, different methods have very different performances on the unbalanced data. Among the six methods, the welch t-test appears to perform best when the size of samples in the large variance group is larger than that in the small one, while the Regularized t-test and SAM outperform others on the unbalanced data in other cases.

Conclusion

Two proposed evaluation models are effective and sample imbalance should be taken into account in microarray experiment design and gene expression data analysis. The results and two proposed evaluation models can provide some help in selecting suitable method to process the unbalanced data.