Robust Tail-sum: Two-group Cancer Outlier Differential Gene Detection
by June Luo.
There have been discussions about detecting differentially
expressed (DE) genes that are over-expressed in some but not all
samples in a disease group. This has become increasingly useful in
cancer studies, where oncogenes are activated in only a minority of
samples. To minimize the variation of coefficient of the statistic, I
propose a new statistic called robust tail-sum (RTS) to detect DE
genes. In simulated examples, I compare RTS to the t-statistic, the
cancer outlier profile analysis, the outlier-sum and the outlier
robust t-statistic. I find RTS performs consistently as the best or
second best method when the number of oncogene outliers in a disease
group varies. In a real example, the new method does well in detecting
differentially expressed genes.
Cancer, COPA, Gene expression analysis, Microarray, Robust,
differentially expressed, false discovery rate
June Luo, firstname.lastname@example.org
Suojin Wang, email@example.com
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