A Time-Series DDP for Functional Proteomics Profiles |
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Authors: | Nieto-Barajas Luis E Müller Peter Ji Yuan Lu Yiling Mills Gordon B |
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Institution: | Department of Statistics, ITAM, Rio Hondo 1, Progreso Tizapan, 01080 Mexico City, Mexico Department of Mathematics, University of Texas at Austin, 1 University Station, Austin Texas 78712, U.S.A. Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, U.S.A. Department of Systems Biology, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, U.S.A. |
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Abstract: | 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. |
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Keywords: | Bayesian nonparametrics Dependent random measures Markov beta process Mixed effects model Stick‐breaking processes Time‐series analysis |
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