並列摘要
Discovering the influential genes through the detection of outliers in samples of disease group subjects is a very new and important approach for gene expression analysis. The outlier sum or outlier mean technique can detect the shift in central tendency for the outlier data but not other characteristics such as spreadness or others for the outlier data. It is desired to provide a test that is easy to implement and efficient in power performance as an alternative tool for gene expression analysis. We propose the concept of outlier proportion for developing a test based on asymptotic distribution of this statistics. We further compare it with the outlier mean for their power performances. To avoid the inefficiency in estimating densities at tail quantiles involved in estimation of outlier proportion variance, we further consider applying the empirical quantile as the cutoff point for an alternative outlier proportion based test which shows satisfactory role in gene expression analysis from the point of power performance.