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Modified FDR Controlling Procedure for Multi-Stage Analyses

  • Catherine Tuglus and Mark J. van der Laan
Published/Copyright: February 4, 2009
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 8 Issue 1

Multiple testing has become an integral component in genomic analyses involving microarray experiments where a large number of hypotheses are tested simultaneously. However, before applying more computationally intensive methods, it is often desirable to complete an initial truncation of the variable set using a simpler and faster supervised method such as univariate regression. Once such a truncation is completed, multiple testing methods applied to any subsequent analysis no longer control the appropriate Type I error rates. Here we propose a modified marginal Benjamini & Hochberg step-up FDR controlling procedure for multi-stage analyses (FDR-MSA), which correctly controls Type I error in terms of the entire variable set when only a subset of the initial set of variables is tested. The method is presented with respect to a variable importance application. As the initial subset size increases, we observe convergence to the standard Benjamini & Hochberg step-up FDR controlling multiple testing procedures. We demonstrate the power and Type I error control through simulation and application to the Golub Leukemia data from 1999.

Keywords: false discovery rate; modified FDR; targeted maximum likelihood
Published Online: 2009-2-4

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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https://doi.org/10.2202/1544-6115.1397
Keywords for this article
false discovery rate; modified FDR; targeted maximum likelihood

Articles in the same Issue

  1. Article
  2. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
  3. Orthology-Based Multilevel Modeling of Differentially Expressed Mouse and Human Gene Pairs
  4. Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis
  5. Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
  6. A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins
  7. Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates
  8. A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms
  9. Hypothesis Tests for Point-Mass Mixture Data with Application to `Omics Data with Many Zero Values
  10. Inferring Dynamic Genetic Networks with Low Order Independencies
  11. Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression
  12. A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
  13. Modified FDR Controlling Procedure for Multi-Stage Analyses
  14. Detecting Outlier Samples in Microarray Data
  15. Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
  16. Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis
  17. Score Statistics for Mapping Quantitative Trait Loci
  18. Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements
  19. A Multilocus Model for Constructing a Linkage Disequilibrium Map in Human Populations
  20. Testing of Chromosomal Clumping of Gene Properties
  21. Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction
  22. Univariate Shrinkage in the Cox Model for High Dimensional Data
  23. Multilevel Comparison of Dendrograms: A New Method with an Application for Genetic Classifications
  24. Weighted Multiple Hypothesis Testing Procedures
  25. Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
  26. Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates
  27. A Parametric Model for Analyzing Anticipation in Genetically Predisposed Families
  28. Bayesian Unsupervised Learning with Multiple Data Types
  29. Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
  30. A Non-Homogeneous Hidden-State Model on First Order Differences for Automatic Detection of Nucleosome Positions
  31. Adaptive Transmission Disequilibrium Test for Family Trio Design
  32. Model Selection Based on FDR-Thresholding Optimizing the Area under the ROC-Curve
  33. Estimation of Selection Intensity under Overdominance by Bayesian Methods
  34. A Multivariate Growth Curve Model for Ranking Genes in Replicated Time Course Microarray Data
  35. Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments
  36. Ancestral Recombination Graphs under Non-Random Ascertainment, with Applications to Gene Mapping
  37. Prediction of Motifs Based on a Repeated-Measures Model for Integrating Cross-Species Sequence and Expression Data
  38. Identifying Individuals in a Complex Mixture of DNA with Unknown Ancestry
  39. A Statistical Model for Genetic Mapping of Viral Infection by Integrating Epidemiological Behavior
  40. Calculating Asymptotic Significance Levels of the Constrained Likelihood Ratio Test with Application to Multivariate Genetic Linkage Analysis
  41. Modeling Dependence in Methylation Patterns with Application to Ovarian Carcinomas
  42. M-quantile Regression Analysis of Temporal Gene Expression Data
  43. MC-Normalization: A Novel Method for Dye-Normalization of Two-Channel Microarray Data
  44. Characterizing the D2 Statistic: Word Matches in Biological Sequences
  45. Transmission Disequilibrium Test Power and Sample Size in the Presence of Locus Heterogeneity
  46. A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy
  47. Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study
  48. A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments
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