Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.

In the study of complex diseases, a major challenge is disease heterogeneity, where the dysregulation of different pathways often lead to similar disease phenotypes. As a result, a given pathway could be differentially expressed with respect to controls for some patients, but not for others. Therefore, to develop successful personalized treatment regime, in addition to identifying disease relevant pathways for the entire patient group, it's also important to test if a particular pathway is dysregulated for an individual patient. To this end, we compare pathway gene expression profile for a particular individual in the patient group to the "norm" (or standard) established by a group of control patients. We studied statistical analysis of patient-specific pathway activities under the mixed models framework. Using gene expression dataset with realistic correlation patterns, we showed the proposed hypothesis testing procedure had false positive rate (type I error) as expected. In addition, we illustrated the proposed methodology using a Type 2 Diabetes dataset. Our results showed a previously diabetes associated pathway was only differentially expressed (relative to the control group) in less than 30% of the diabetes patients, which provided an explanation for the moderate group level statistical significance seen in a previous study. This result also suggested targeting this particular pathway would likely be beneficial for only 30% of the patients. In addition to the case-control study we have illustrated, this model can be easily extended to handle more complex designs with additional covariates and multiple sources of variations. Moreover, the proposed model operates within a well-established statistical framework and can be implemented in common statistical packages.