A Time-Series DDP for Functional Proteomics Profiles
Article first published online: 5 JAN 2012
© 2012, The International Biometric Society
Volume 68, Issue 3, pages 859–868, September 2012
How to Cite
Nieto-Barajas, L. E., Müller, P., Ji, Y., Lu, Y. and Mills, G. B. (2012), A Time-Series DDP for Functional Proteomics Profiles. Biometrics, 68: 859–868. doi: 10.1111/j.1541-0420.2011.01724.x
- Issue published online: 26 SEP 2012
- Article first published online: 5 JAN 2012
- Received February 2011. Revised October 2011. Accepted October 2011.
- Bayesian nonparametrics;
- Dependent random measures;
- Markov beta process;
- Mixed effects model;
- Stick-breaking processes;
- Time-series analysis
Summary 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.