A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data
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Alessio Farcomeni
Abstract
When modeling time course microarray data special interest may reside in identifying time frames in which gene expression levels follow a monotonic (increasing or decreasing) trend. A trajectory may change its regime because of the reaction to treatment or of a natural developmental phase, as in our motivating example about identification of genes involved in embryo development of mice with the 22q11 deletion. To this aim we propose a new flexible Bayesian autoregressive hidden Markov model based on three latent states, corresponding to stationarity, to an increasing and to a decreasing trend. In order to select a list of genes, we propose decision criteria based on the posterior distribution of the parameters of interest, taking into account the uncertainty in parameter estimates. We also compare the proposed model with two simpler models based on constrained formulations of the probability transition matrix.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- A New Explained-Variance Based Genetic Risk Score for Predictive Modeling of Disease Risk
- Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications
- Cluster-Localized Sparse Logistic Regression for SNP Data
- How to analyze many contingency tables simultaneously in genetic association studies
- Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure
- Estimating the Number of One-step Beneficial Mutations
- Testing clonality of three and more tumors using their loss of heterozygosity profiles
- Correction for Founder Effects in Host-Viral Association Studies via Principal Components
- A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
- An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
- A Novel and Fast Normalization Method for High-Density Arrays
- Performance of MAX Test and Degree of Dominance Index in Predicting the Mode of Inheritance
- A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data
- QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function
- Computing Posterior Probabilities for Score-based Alignments Using ppALIGN
