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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

Lily Wang¹, Xi Chen, Russell D Wolfinger

¹Vanderbilt University, USA. lily.wang@vanderbilt.edu

Statistical Applications in Genetics and Molecular Biology

|December 4, 2009

Summary

This study introduces a novel random coefficient model for gene set analysis in time course experiments. The method enhances statistical power and stability for analyzing coordinated gene changes over time.

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Area of Science:

Bioinformatics
Systems Biology
Statistical Genetics

Background:

Gene set analysis (GSA) identifies coordinated gene changes in biological pathways.
Existing GSA methods are limited for time course experiments.
Microarray data analysis requires robust statistical approaches for complex designs.

Purpose of the Study:

To develop a unified statistical model for GSA in time course experiments.
To improve the statistical power and stability of GSA.
To enable gene ranking and analysis of complex experimental designs.

Main Methods:

Proposed a unified statistical model using random coefficient models (a type of mixed effects model).
The model incorporates a systematic component for mean gene trajectories and a random component for individual gene variations.
Applied the model to gene expression data from a mouse colon development time course.

Main Results:

The proposed model outperformed existing methods in discriminating differentially changed gene sets.
Achieved more stable results, less affected by sampling variations.
Adequately modeled gene dependency, preserved type I error rate, and enabled gene ranking.

Conclusions:

The random coefficient model offers a powerful and stable approach for GSA in time course experiments.
Provides a unified framework for systems analysis of microarray data with complex designs.
Validates the methodology for analyzing gene expression dynamics and biological pathways.