We used PCR as a more sensitive method (rigorous statistical criteria had been applied to filter hits of the Omics approaches) to examine the regulation of ATF4.
Compared to second order statistics (like covariance and hence correlation) mutual information is a more sensitive measure for the statistical independence oft two random variables.
The clinical introduction of a new anticancer agent at a dose 1/10 the MTD in the more sensitive species carries a statistical risk of about 3%; that is, the initial doses of about 3 of every 100 new drugs introduced into the clinic will produce some toxic effects in man.
The most comprehensive previous study [11] used a large-scale dataset comparable in size to ours to identify 32 of the 68 characteristic sites found in the present work, indicating that MI may be a more sensitive measure of association than the statistical tests employed in that study.
Given that more reliable measures are expected to be more sensitive (i.e. give higher statistical power), we might expect the use of more modern video equipment to substantially improve the sensitivity of the MPOS.
As a result, we could have missed some true associations between SNPs and biomarkers because our statistical approach is more sensitive to false positives than to false negatives.
Although our data suggested that immunohistochemical analysis might be more sensitive than serology in early samples, statistical analysis showed no significant difference between the 2 techniques.
We believe that the positive effect of using deregressed EBV applies to most situations, whereas choosing the best statistical method is more sensitive to the particular situation.
However, we recognize that in several cases statistically significant differences did not translate to practical differences in RNA quantity or quality (i.e., the statistical analysis was more sensitive than analytically relevant).
