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A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments
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Lily Wang
Published/Copyright:
November 7, 2009
Methods for gene set analysis test for coordinated changes of a group of genes involved in the same biological process or molecular pathway. Higher statistical power is gained for gene set analysis by combining weak signals from a number of individual genes in each group. Although many gene set analysis methods have been proposed for microarray experiments with two groups, few can be applied to time course experiments. We propose a unified statistical model for analyzing time course experiments at the gene set level using random coefficient models, which fall into the more general class of mixed effects models. These models include a systematic component that models the mean trajectory for the group of genes, and a random component (the random coefficients) that models how each gene's trajectory varies about the mean trajectory.We show that the proposed model (1) outperforms currently available methods at discriminating gene sets differentially changed over time from null gene sets; (2) provides more stable results that are less affected by sampling variations; (3) models dependency among genes adequately and preserves type I error rate; and (4) allows for gene ranking based on predicted values of the random effects. We describe simulation studies using gene expression data with "real life" correlations and we demonstrate the proposed random coefficient model using a mouse colon development time course dataset. The agreement between results of the proposed random coefficient model and the previous reports for this proof-of-concept trial further validates this methodology, which provides a unified statistical model for systems analysis of microarray experiments with complex experimental designs when re-sampling based methods are difficult to apply.
Keywords: microarray; gene expression; mixed models; pathway analysis; gene set analysis; statistical significance
Published Online: 2009-11-7
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
microarray;
gene expression;
mixed models;
pathway analysis;
gene set analysis;
statistical significance
Articles in the same Issue
- Article
- Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
- Orthology-Based Multilevel Modeling of Differentially Expressed Mouse and Human Gene Pairs
- Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis
- Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
- A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins
- Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates
- 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
- Hypothesis Tests for Point-Mass Mixture Data with Application to `Omics Data with Many Zero Values
- Inferring Dynamic Genetic Networks with Low Order Independencies
- Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression
- A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
- Modified FDR Controlling Procedure for Multi-Stage Analyses
- Detecting Outlier Samples in Microarray Data
- Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
- Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis
- Score Statistics for Mapping Quantitative Trait Loci
- Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements
- A Multilocus Model for Constructing a Linkage Disequilibrium Map in Human Populations
- Testing of Chromosomal Clumping of Gene Properties
- Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction
- Univariate Shrinkage in the Cox Model for High Dimensional Data
- Multilevel Comparison of Dendrograms: A New Method with an Application for Genetic Classifications
- Weighted Multiple Hypothesis Testing Procedures
- Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
- Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates
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- Model Selection Based on FDR-Thresholding Optimizing the Area under the ROC-Curve
- Estimation of Selection Intensity under Overdominance by Bayesian Methods
- A Multivariate Growth Curve Model for Ranking Genes in Replicated Time Course Microarray Data
- Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments
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- A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy
- Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study
- A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments
