A unified mixed effects model for gene set analysis of time course microarray experiments

Lily Wang, Xi Chen, Russell D. Wolfinger, Jeffrey L. Franklin, Robert J. Coffey, Bing Zhang

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number47
JournalStatistical Applications in Genetics and Molecular Biology
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2009
Externally publishedYes

Fingerprint

Mixed Effects Model
Microarrays
Microarray
Genes
Gene
Random Coefficient Models
Experiment
Experiments
Trajectories
Statistical Models
Trajectory
Statistical Model
Sampling
Biological Phenomena
Statistical Power
Type I Error Rate
Resampling
Component Model
Systems Analysis
Gene Expression Data

Keywords

  • Gene expression
  • Gene set analysis
  • Microarray
  • Mixed models
  • Pathway analysis
  • Statistical significance

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

Cite this

A unified mixed effects model for gene set analysis of time course microarray experiments. / Wang, Lily; Chen, Xi; Wolfinger, Russell D.; Franklin, Jeffrey L.; Coffey, Robert J.; Zhang, Bing.

In: Statistical Applications in Genetics and Molecular Biology, Vol. 8, No. 1, 47, 01.12.2009.

Research output: Contribution to journalArticle

Wang, Lily ; Chen, Xi ; Wolfinger, Russell D. ; Franklin, Jeffrey L. ; Coffey, Robert J. ; Zhang, Bing. / A unified mixed effects model for gene set analysis of time course microarray experiments. In: Statistical Applications in Genetics and Molecular Biology. 2009 ; Vol. 8, No. 1.
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