A new method for multi-group cancer outlier differential gene detection

by June Luo and Pengju G. Luo.

Abstract: There have been discussions about detecting differentially expressed (DE) genes that are over-expressed or down-expressed in some but not all samples in a disease group for a two-class microarray. This has become increasingly useful in cancer studies, where oncogenes are activated in only a minority of samples. But there are not many discussions about detecting DE genes for multi-group microarrays. I propose a new statistic called robust tail F-statistic (RTF) to detect DE genes in multi-group cancer expression datasets. In simulated examples, I compare RTF to the F-statistic, the outlier robust F-statistic by Liu andWu (2007) and the outlier F-statistic by Liu andWu (2007). I find RTF performs consistently as the best method under a wide range of conditions. In a real example, robust tail F-statistic provides useful findings beyond those of F-statistic, outlier robust F-statistic and outlier F-statistic.

Key Words: gene expression analysis; microarray; robust; differentially expressed genes; standard error; ROC curve

Authors:
June Luo, jluo@clemson.edu
Pengju G. Luo, lpengju@sherman.edu

Editor: Weiming Ke, Weiming.Ke@sdstate.edu

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