Comparison of Clinical Subgroup aCGH Profiles through Pseudolikelihood Ratio Tests
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David Engler
Array-based Comparative Genomic Hybridization (aCGH) is a microarray-based technology that assists in identification of DNA sequence copy number changes across the genome. Examination of differences in instability phenotype, or pattern of copy number alterations, between cancer subtypes can aid in classification of cancers and lead to better understanding of the underlying cytogenic mechanism. Instability phenotypes are composed of a variety of copy number alteration features including height or magnitude of copy number alteration level, frequency of transition between copy number states such as gain and loss, and total number of altered clones or probes. That is, instability phenotype is multivariate in nature. Current methods of instability phenotype assessment, however, are limited to univariate measures and are therefore limited in both sensitivity and interpretability. In this paper, a novel method of instability assessment is presented that is based on the Engler et al. (2006) pseudolikelhood approach for aCGH data analysis. Through use of a pseudolikelihood ratio test (PLRT), more sensitive assessment of instability phenotype differences between cancer subtypes is possible. Evaluation of the PLRT method is conducted through analysis of a meningioma data set and through simulation studies. Results are shown to be more accurate and more easily interpretable than current measures of instability assessment.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Article
- Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays
- Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains
- Assessing Modularity Using a Random Matrix Theory Approach
- Choice of Summary Statistic Weights in Approximate Bayesian Computation
- Genetic Linkage Analysis in the Presence of Germline Mosaicism
- Fitting Boolean Networks from Steady State Perturbation Data
- Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing
- Bayesian Learning from Marginal Data in Bionetwork Models
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- Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures
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- Multiscale Characterization of Signaling Network Dynamics through Features
- A Calibrated Multiclass Extension of AdaBoost
- False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies
- A Markov-Chain Model for the Analysis of High-Resolution Enzymatically 18O-Labeled Mass Spectra
- Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery
- Learning Monotonic Genotype-Phenotype Maps
- A Comparison of Multifactor Dimensionality Reduction and L1-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies
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- A Three Component Latent Class Model for Robust Semiparametric Gene Discovery
- Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions
- A Robust Statistical Method to Detect Null Alleles in Microsatellite and SNP Datasets in Both Panmictic and Inbred Populations
- Large Sample Approximations of Probabilities of Correct Evolutionary Tree Estimation and Biases of Maximum Likelihood Estimation
- Interval Estimation of Familial Correlations from Pedigrees
- Information Metrics in Genetic Epidemiology
- Linear Combination Test for Hierarchical Gene Set Analysis
- Exploratory Analysis of Multiple Omics Datasets Using the Adjusted RV Coefficient
- Application of the Lasso to Expression Quantitative Trait Loci Mapping
- A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction
- Imputation Estimators Partially Correct for Model Misspecification
- On the Statistical Properties of SGoF Multitesting Method
- Meta-Analysis of Family-Based and Case-Control Genetic Association Studies that Use the Same Cases
- A Non-Parametric Method for Detecting Specificity Determining Sites in Protein Sequence Alignments
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- Inferring Gene Networks using Robust Statistical Techniques
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- Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests
- Weighted Lasso with Data Integration
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