A Time-Series DDP for Functional Proteomics Profiles

Luis E. Nieto-Barajas, Peter Müller, Yuan Ji, Yiling Lu, Gordon B. Mills

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish (US)
Pages (from-to)859-868
Number of pages10
Issue number3
StatePublished - Sep 2012
Externally publishedYes


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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