A Time-Series DDP for Functional Proteomics Profiles

作者：Nieto Barajas Luis E^*; Mueller Peter; Ji Yuan; Lu Yiling; Mills Gordon B

来源：Biometrics, 2012, 68(3): 859-868.

DOI：10.1111/j.1541-0420.2011.01724.x

• Bayesian nonparametrics
• Dependent random measures
• Markov beta process
• Mixed effects model
• Stick-breaking processes
• Time-series analysis

Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to coregulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers, we propose a mixed effects model with temporal and protein-specific components. We develop a sequence of random probability measures (RPM) to account for the dependence in time of the protein expression measurements. Marginally, for each RPM we assume a Dirichlet process model. The dependence is introduced by defining multivariate beta distributions for the unnormalized weights of the stick-breaking representation. We also acknowledge the pathway dependence among proteins via a conditionally autoregressive model. Applying our model to the RPPA data, we reveal a pathway-dependent functional profile for the set of proteins as well as marginal expression profiles over time for individual markers.

• 出版日期2012-9