Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference
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Dan Lin
, Ziv Shkedy , Dani Yekutieli , Tomasz Burzykowski , Hinrich W.H. Göhlmann , An De Bondt , Tim Perera , Tamara Geerts and Luc Bijnens
Dose-response studies are commonly used in experiments in pharmaceutical research in order to investigate the dependence of the response on dose, i.e., a trend of the response level toxicity with respect to dose. In this paper we focus on dose-response experiments within a microarray setting in which several microarrays are available for a sequence of increasing dose levels. A gene is called differentially expressed if there is a monotonic trend (with respect to dose) in the gene expression. We review several testing procedures which can be used in order to test equality among the gene expression means against ordered alternatives with respect to dose, namely Williams' (Williams 1971 and 1972), Marcus' (Marcus 1976), global likelihood ratio test (Bartholomew 1961, Barlow et al. 1972, and Robertson et al. 1988), and M (Hu et al. 2005) statistics. Additionally we introduce a modification to the standard error of the M statistic. We compare the performance of these five test statistics. Moreover, we discuss the issue of one-sided versus two-sided testing procedures. False Discovery Rate (Benjamni and Hochberg 1995, Ge et al. 2003), and resampling-based Familywise Error Rate (Westfall and Young 1993) are used to handle the multiple testing issue. The methods above are applied to a data set with 4 doses (3 arrays per dose) and 16,998 genes. Results on the number of significant genes from each statistic are discussed. A simulation study is conducted to investigate the power of each statistic. A R library IsoGene implementing the methods is available from the first author.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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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