The impact of sample imbalance on identifying differentially expressed genes

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 var...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Kun Yang, Jianzhong Li, Hong Gao
• Keywords: BMCBI 2006, Evaluation Models, Sample Imbalance, Unbalanced Data