Modified F-statistic: Multi-group Cancer Outlier Differential Gene
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. The detection of DE genes has become increasingly
useful in cancer studies, where oncogenes are only activated in a
minority of samples. Since there are not many discussions about
detecting DE genes in multi-group microarrays, we are in need of more
applicable and efficient methods to detect DE genes in multi-group
microarrays. With the intention of minimizing the variation of the
coefficient and reducing the standard error of the statistic, I propose
a new method called modified F-statistic (MF) to detect DE genes in two
or multi-group cancer microarrays. In simulated examples, I compare MF
to the traditional F-statistic, the outlier robust F-statistic and the
outlier F-statistic. The aspects of comparison include the receiver
operating characteristic (ROC) score, accuracy rate and reproducibility
of gene lists. I find MF performs consistently as the best or the second
best method when the number of oncogene outliers in disease groups
varies. In a real example, the new method provides useful findings and
confirmation of literature results in detecting over-expressed genes.
Key Words: cancer microarray, robust, outlier, differential gene
detection, standard error, ROC curve, accuracy rate, concordance
June Luo, firstname.lastname@example.org
Kanchan Jain, email@example.com
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