In Mean CORGs, the pathway's overall direction of expression change is first determined as the sign of mean t-statistic values of all its member genes.
Then, overall direction of expression change (that is, up or down) in the pathway is determined as the sign of the mean t-statistic values of all its member genes.
In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size.
The test statistic (mean t) was calculated as the mean of the pooled two-sample t-statistic between the colony diameters at each time weighted by the degrees of freedom.
Estimated values were also compared with measured values in terms of statistical evaluation indicators like the coefficient of determination ((R^{2}),) mean percentage error, mean bias error, root mean square error (RMSE), mean absolute relative error, and t statistic.
As we suspect that the two populations have different means, we use the t statistic as test statistic, but other statistics could be used as well and may be more powerful for particular types of deviations from the null.
The ROC curves in Figure 1 demonstrate that the performance of the sample mean and of the SAM t statistic were very close, and our hierarchical model, incorporating operon information, outperformed both of them.
For each gene i, we calculated the difference between sample means and a two-sample t statistic.
We calculated a variance-adjusted probability to account for differences in the distribution of minor allele dosage at each SNP, by computing a Welch-type t statistic for the mean change in MAF at SNP j as where s jGAM is the sample variance in minor allele dosage in the Gambian cohort, n jGAM is the number of recorded genotypes for SNP j, and so on.
