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Multiple Testing and Data Adaptive Regression: An Application to HIV-1 Sequence Data.
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Merrill D. Birkner
Veröffentlicht/Copyright:
18. April 2005
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological insights regarding the replication ability of HIV-1. Determining specific target codons on the viral strand will facilitate the manufacturing of target-specific antiretrovirals. Various algorithmic and analysis techniques can be applied to this application. In this paper, we apply two techniques to a data set consisting of 317 patients, each with 282 sequenced protease and reverse transcriptase codons. The first application is recently developed multiple testing procedures to find codons which have significant univariate associations with the replication capacity of the virus. A single-step multiple testing procedure (Pollard and van der Laan 2003) method was used to control the family wise error rate (FWER) at the five percent alpha level as well as the application of augmentation multiple testing procedures to control the generalized family wise error (gFWER) or the tail probability of the proportion of false positives (TPPFP). We also applied a data adaptive multiple regression algorithm to obtain a prediction of viral replication capacity based on an entire mutant/non-mutant sequence profile. This is a loss-based, cross-validated Deletion/Substitution/Addition regression algorithm (Sinisi and van der Laan 2004), which builds candidate estimators in the prediction of a univariate outcome by minimizing an empirical risk. These methods are two separate techniques with distinct goals used to analyze this structure of viral data.
Keywords: Bootstrap; codon; generalized family wise error rate; HIV-1; multiple testing; prediction; tail probability of the proportion of false positives; type I error; variable selection.
Published Online: 2005-4-18
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Schlagwörter für diesen Artikel
Bootstrap;
codon;
generalized family wise error rate;
HIV-1;
multiple testing;
prediction;
tail probability of the proportion of false positives;
type I error;
variable selection.
Artikel in diesem Heft
- Article
- Estimating Motifs Under Order Restrictions
- Reproducible Research: A Bioinformatics Case Study
- Generalized Rank Tests for Replicated Microarray Data
- Stepwise Normalization of Two-Channel Spotted Microarrays
- Comparing Automatic and Manual Image Processing in FLARE Assay Analysis for Colon Carcinogenesis
- Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays
- Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels
- Multiple Testing and Data Adaptive Regression: An Application to HIV-1 Sequence Data.
- Early Diagnostic Marker Panel Determination for Microarray Based Clinical Studies
- Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors
- Combined Association and Linkage Analysis for General Pedigrees and Genetic Models
- Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data
- The Relative Inefficiency of Sequence Weights Approaches in Determining a Nucleotide Position Weight Matrix
- A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes
- Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction
- Error Distribution for Gene Expression Data
- A General Framework for Weighted Gene Co-Expression Network Analysis
- Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm
- Comparing Bacterial DNA Microarray Fingerprints
- Continuous Covariates in Genetic Association Studies of Case-Parent Triads: Gene and Gene-Environment Interaction Effects, Population Stratification, and Power Analysis
- Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models
- Empirical Bayes Estimation of a Sparse Vector of Gene Expression Changes
- Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data
- FADO: A Statistical Method to Detect Favored or Avoided Distances between Occurrences of Motifs using the Hawkes' Model
- Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation
- Fold-Change Estimation of Differentially Expressed Genes using Mixture Mixed-Model
- Test on the Structure of Biological Sequences via Chaos Game Representation
- Reverse Engineering Galactose Regulation in Yeast through Model Selection
- Empirical Bayes and Resampling Based Multiple Testing Procedure Controlling Tail Probability of the Proportion of False Positives.
- Weighted Analysis of Paired Microarray Experiments
- A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
- Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits
- Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
- Bayesian Statistical Studies of the Ramachandran Distribution
- On Reference Designs For Microarray Experiments
- Computing Asymptotic Power and Sample Size for Case-Control Genetic Association Studies in the Presence of Phenotype and/or Genotype Misclassification Errors
