Model Selection for Mixtures of Mutagenetic Trees
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Junming Yin
The evolution of drug resistance in HIV is characterized by the accumulation of resistance-associated mutations in the HIV genome. Mutagenetic trees, a family of restricted Bayesian tree models, have been applied to infer the order and rate of occurrence of these mutations. Understanding and predicting this evolutionary process is an important prerequisite for the rational design of antiretroviral therapies. In practice, mixtures models of K mutagenetic trees provide more flexibility and are often more appropriate for modelling observed mutational patterns.Here, we investigate the model selection problem for K-mutagenetic trees mixture models. We evaluate several classical model selection criteria including cross-validation, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion. We also use the empirical Bayes method by constructing a prior probability distribution for the parameters of a mutagenetic trees mixture model and deriving the posterior probability of the model. In addition to the model dimension, we consider the redundancy of a mixture model, which is measured by comparing the topologies of trees within a mixture model. Based on the redundancy, we propose a new model selection criterion, which is a modification of the BIC.Experimental results on simulated and on real HIV data show that the classical criteria tend to select models with far too many tree components. Only cross-validation and the modified BIC recover the correct number of trees and the tree topologies most of the time. At the same optimal performance, the runtime of the new BIC modification is about one order of magnitude lower. Thus, this model selection criterion can also be used for large data sets for which cross-validation becomes computationally infeasible.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity
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- Treatment of Uninformative Families in Mean Allele Sharing Tests for Linkage
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- Combining Results of Microarray Experiments: A Rank Aggregation Approach
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- Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates
- A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
- Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
- Using Complexity for the Estimation of Bayesian Networks
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- Examining Protein Structure and Similarities by Spectral Analysis Technique
- Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
- Approximate Sample Size Calculations with Microarray Data: An Illustration
- Numerical Solutions for Patterns Statistics on Markov Chains
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- A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments
- Validation in Genomics: CpG Island Methylation Revisited
- An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
- Letter to the Editor
- Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
- Reader's Reaction
- Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)
Articles in the same Issue
- Article
- Low-Order Conditional Independence Graphs for Inferring Genetic Networks
- A Generalized Clustering Problem, with Application to DNA Microarrays
- A Bayes Regression Approach to Array-CGH Data
- Statistical Selection of Maintenance Genes for Normalization of Gene Expressions
- Predicting the Strongest Domain-Domain Contact in Interacting Protein Pairs
- Dimension Reduction for Classification with Gene Expression Microarray Data
- A New Type of Stochastic Dependence Revealed in Gene Expression Data
- A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity
- Quality Optimised Analysis of General Paired Microarray Experiments
- Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data
- Cross-Validated Bagged Prediction of Survival
- Treatment of Uninformative Families in Mean Allele Sharing Tests for Linkage
- Quantile-Function Based Null Distribution in Resampling Based Multiple Testing
- Combining Results of Microarray Experiments: A Rank Aggregation Approach
- Model Selection for Mixtures of Mutagenetic Trees
- Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates
- A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
- Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
- Using Complexity for the Estimation of Bayesian Networks
- Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies
- Examining Protein Structure and Similarities by Spectral Analysis Technique
- Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
- Approximate Sample Size Calculations with Microarray Data: An Illustration
- Numerical Solutions for Patterns Statistics on Markov Chains
- A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion
- A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments
- Validation in Genomics: CpG Island Methylation Revisited
- An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
- Letter to the Editor
- Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
- Reader's Reaction
- Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)
