A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments
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Claudia Angelini
The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data. The proposed procedure deals successfully with various technical difficulties that arise in typical microarray experiments such as a small number of observations, non-uniform sampling intervals and missing or replicated data. The procedure allows one to account for various types of errors and offers a good compromise between nonparametric techniques and techniques based on normality assumptions. In addition, all evaluations are performed using analytic expressions, so the entire procedure requires very small computational effort. The procedure is studied using both simulated and real data, and is compared with competitive recent approaches. Finally, the procedure is applied to a case study of a human breast cancer cell line stimulated with estrogen. We succeeded in finding new significant genes that were not marked in an earlier work on the same dataset.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- A Bayesian Model of AFLP Marker Evolution and Phylogenetic Inference
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- Case-Control Inference of Interaction between Genetic and Nongenetic Risk Factors under Assumptions on Their Distribution
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- Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge
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- Approximating the Variance of the Conditional Probability of the State of a Hidden Markov Model
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Articles in the same Issue
- Article
- Accounting for Dependence in Similarity Data from DNA Fingerprinting
- Normalization of Dye Bias in Microarray Data Using the Mixture of Splines Model
- A Generalized Sidak-Holm Procedure and Control of Generalized Error Rates under Independence
- Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates
- Likelihood-Based Inference for Multi-Color Optical Mapping
- Sparse Logistic Regression with Lp Penalty for Biomarker Identification
- Super Learning: An Application to the Prediction of HIV-1 Drug Resistance
- Supervised Detection of Conserved Motifs in DNA Sequences with Cosmo
- Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
- Statistical Inference for Quantitative Polymerase Chain Reaction Using a Hidden Markov Model: A Bayesian Approach
- A Bayesian Model of AFLP Marker Evolution and Phylogenetic Inference
- Sequential Quantitative Trait Locus Mapping in Experimental Crosses
- Case-Control Inference of Interaction between Genetic and Nongenetic Risk Factors under Assumptions on Their Distribution
- Inference on the Limiting False Discovery Rate and the P-value Threshold Parameter Assuming Weak Dependence between Gene Expression Levels within Subject
- Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge
- Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression
- A Method for Meta-Analysis of Case-Control Genetic Association Studies Using Logistic Regression
- Approximating the Variance of the Conditional Probability of the State of a Hidden Markov Model
- Using Linear Mixed Models for Normalization of cDNA Microarrays
- Experimental Design for Two-Color Microarrays Applied in a Pre-Existing Split-Plot Experiment
- The Cyclohedron Test for Finding Periodic Genes in Time Course Expression Studies
- H-Tuple Approach to Evaluate Statistical Significance of Biological Sequence Comparison with Gaps
- Multiple Testing Issues in Discriminating Compound-Related Peaks and Chromatograms from High Frequency Noise, Spikes and Solvent-Based Noise in LC - MS Data Sets
- A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments
- Super Learner
- Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference
- On the Operational Characteristics of the Benjamini and Hochberg False Discovery Rate Procedure
- A Comparison of Methods to Control Type I Errors in Microarray Studies
- Selection of Biologically Relevant Genes with a Wrapper Stochastic Algorithm
- T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data
- Population Structure and Covariate Analysis Based on Pairwise Microsatellite Allele Matching Frequencies
- Estimating the Arm-Wise False Discovery Rate in Array Comparative Genomic Hybridization Experiments
- An Expectation Maximization Approach to Estimate Malaria Haplotype Frequencies in Multiply Infected Children
- Estimation of Expression Levels in Spotted Microarrays with Saturated Pixels
- Improving Divergence Time Estimation in Phylogenetics: More Taxa vs. Longer Sequences
- Fully Bayesian Mixture Model for Differential Gene Expression: Simulations and Model Checks
- Multiple Testing for SNP-SNP Interactions