To evaluate the performance of the proposed method in estimating gene expression levels and identifying DE genes, we compared the proposed method to the sample mean and SAM t statistics [ 1, 3].
The method using the sample mean has the advantages of simplicity and meaningful interpretation if speaker adaptation is to be applied.
Through the simulation studies, we showed that the proposed method outperformed the sample mean and the SAM t statistics in estimating gene expression levels and detecting DE genes.
For example, as the specificity equals to 0.8, the sensitivities of the methods using the sample mean or SAM t were about 0.91, 0.80, and 0.68 for simulations 1,2, and 3, respectively, while the sensitivities of the proposed method were 0.95, 0.89 and 0.83, respectively.
One method to acquire the sample mean with respect to the mixture weights is to use the median operator.
In this section, we propose a quantile method for estimating the sample mean and the sample standard deviation, respectively.
They proposed a simple method for estimating the sample mean and the sample variance (or equivalently the sample standard deviation) from the median, range, and the size of the sample.
Hozo et al. [ 2] proposed a simple alternative method for estimating the sample mean and the sample standard deviation from the median, minimum, maximum, and the size of the sample.
For each pair we determined the standard deviation of the consecutive samples and fitted the calculated values to the characteristic function SD = a + b Y mean, (1) at the logarithmic scale; here Y m is the sample mean (see the Method section, Method of analysis).
The simulation studies and the real data application demonstrated that the proposed method performed better than the sample mean and the SAM t statistics in estimating the gene expression levels as well as in detecting differentially expressed genes.
