Estimating the Arm-Wise False Discovery Rate in Array Comparative Genomic Hybridization Experiments
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Daniel P Gaile
Array Comparative Genomic Hybridization (aCGH) is an array-based technology which provides simultaneous spot assays of relative genetic abundance (RGA) levels at multiple sites across the genome. These spot assays are spatially correlated with respect to genomic location and, as a result, the univariate tests conducted using data generated from these spot assays are also spatially correlated. In the context of multiple hypothesis testing, this spatial correlation complicates the question of how best to define a `discovery' and consequently, how best to estimate the false discovery rate (FDR) corresponding to a given rejection region.One can quantify the number of discoveries as the total number of spots for which the spot-based univariate test statistic falls within a given rejection region. Under this spot-based method, separate but correlated discoveries are identified. We show via a simulation study that the method of Benjamini and Hochberg (1995) can provide a reasonable estimate of the spot-wise FDR, but these results require that the simulated spot assays are categorized as true or false discoveries in a particular way. However, laboratory researchers may actually be interested in estimating a `regional' FDR, rather than a `local' spot-wise FDR. We describe an example of such circumstances, and present a method for estimating the (chromosome) arm-wise False Discovery Rate. In this framework, one can quantify the number of discoveries as the total number of chromosome arms for which at least one spot-based test statistic falls into a given rejection region. Defining the discoveries in this way, both the biological and testing objectives coincide. We provide results from a series of simulations which involved the analysis of preferentially re-sampled spot assay values from a real aCGH dataset.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- 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
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- 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
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- 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
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- Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference
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- A Comparison of Methods to Control Type I Errors in Microarray Studies
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