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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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A mixture model with random-effects components for clustering correlated gene-expression profiles.

S K Ng¹, G J McLachlan, K Wang

¹Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.

Bioinformatics (Oxford, England)

|May 6, 2006

Summary

We developed a new random-effects model for clustering gene expression profiles, accounting for correlations and experimental covariates. This method accurately identifies gene clusters in various experimental designs, including time-course and cross-sectional studies.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression clustering is crucial for understanding gene function and biological processes.
Traditional methods often overlook correlations between gene profiles and experimental design structures, leading to potential inaccuracies.
Replicated array data and complex experimental designs necessitate advanced statistical approaches.

Purpose of the Study:

To propose a unified random-effects model for clustering gene expression profiles with correlated levels.
To extend normal mixture models to incorporate correlations and covariate information for robust gene clustering.
To provide a flexible model applicable to diverse experimental settings, including longitudinal and cross-sectional studies.

Main Methods:

Developed a random-effects model extending normal mixture models.
Incorporated methods to account for correlations between gene profiles.
Enabled the inclusion of covariate information (e.g., time, experimental classes) into the clustering process.
Utilized the Expectation-Maximization (EM) algorithm for maximum likelihood fitting with closed-form solutions.

Main Results:

Demonstrated the model's effectiveness on three real microarray datasets (time-course, repeated-measurement, cross-sectional).
Successfully identified relevant gene clusters supported by existing gene-function annotations.
Showcased deterministic model fitting without computationally intensive Monte Carlo approximations.
Validated the approach using a synthetic dataset.

Conclusions:

The proposed random-effects model offers a unified and effective approach for clustering correlated gene expression profiles.
The model accurately handles complex experimental designs and covariate information, providing reliable biological insights.
The EM algorithm implementation allows for efficient and deterministic fitting, making the method practical for large-scale genomic studies.