  Identification of differentially expressed genes (DE-genes) is commonly
conducted in modern biomedical researches. However, unwanted variation
inevitably arises during the data collection process, which could make the
detection results heavily biased. It is suggested to remove the unwanted
variation while keeping the biological variation to ensure a reliable analysis
result. Removing Unwanted Variation (RUV) is recently proposed for this purpose
by the virtue of negative control genes. On the other hand, outliers are
frequently appear in modern high-throughput genetic data that can heavily
affect the performances of RUV and its downstream analysis. In this work, we
propose a robust RUV-testing procedure via gamma-divergence. The advantages of
our method are twofold: (1) it does not involve any modeling for the outlier
distribution, which is applicable to various situations, (2) it is easy to
implement in the sense that its robustness is controlled by a single tuning
parameter gamma of gamma-divergence, and a data-driven criterion is developed
to select $\gamma$. In the Gender Study, our method can successfully remove
unwanted variation, and is able to identify more DE-genes than conventional
methods.
