Table 1.
Posterior predictive model diagnostics are given for 10 randomly selected genes from adenocarcinoma TCGA samples
| Gene ID | IQR ( S n ) | 95% int. for IQR ( x rep ) | p-value |
|---|---|---|---|
| UPK1A|11045 | 2.12 | [1.0, 3.0] | 0.09 |
| OR4P4|81300 | 0.00 | [0.0, 0.0] | 0.50 |
| PCDHA12|56137 | 139.22 | [107.8, 187.0] | 0.54 |
| MDS2|259283 | 1.85 | [2.0, 5.0] | 1.00 |
| AXIN2|8313 | 347.69 | [331.5, 439.3] | 0.85 |
| DYNLT1|6993 | 848.41 | [830.0, 1043.3] | 0.90 |
| RARA|5914 | 786.43 | [706.8, 881.3] | 0.62 |
| TMEM194A|23306 | 396.06 | [367.0, 471.3] | 0.76 |
| AGPS|8540 | 496.45 | [505.8, 636.5] | 0.97 |
| NLRP2|55655 | 854.47 | [381.3, 677.5] | 0.00 |
Inter-quartile distance (IQR) is used as a robust measure of dispersion. In the table, IQR(S n ) is the training data’s IQR, followed by the 95-th credible interval, and the posterior predictive P-value. In cases where the P-value is close to 0 or 1, the true test statistic’s distance from the 95-th credible interval can be used to determine the magnitude of the mis-fit.