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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