Modified F-statistic: Multi-group Cancer Outlier Differential Gene Detection

by June Luo.

Abstract: 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,

Editor: Kanchan Jain,

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