Abstract
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.
Original language | English (US) |
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Pages (from-to) | 859-868 |
Number of pages | 10 |
Journal | Biometrics |
Volume | 68 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2012 |
Externally published | Yes |
Keywords
- Bayesian nonparametrics
- Dependent random measures
- Markov beta process
- Mixed effects model
- Stick-breaking processes
- Time-series analysis
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics