A new method for multi-group cancer outlier differential gene detection
by June Luo and Pengju G. Luo.
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.
gene expression analysis; microarray; robust; differentially expressed genes; standard error;
June Luo, email@example.com
Pengju G. Luo, firstname.lastname@example.org
Weiming Ke, Weiming.Ke@sdstate.edu
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (651139 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2565 times since January 8, 2009.
Return to the Home Page.