A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Time course omics experiments generate high-dimensional data to study dynamic biological regulation.
• Analyzing this data is challenging due to its size, complexity, and the need to handle noise and missing values.

Purpose of the Study:

• To present a novel, robust, and computationally efficient framework for analyzing time course omics data.
• To improve statistical analysis, including differential expression and clustering, for dynamic biological systems.

Main Methods:

• A three-stage framework: quality assessment/filtering, linear mixed-model-based profile modeling with serial selection, and trajectory analysis (clustering, differential expression).
• The framework handles subject-specific variability, dimension reduction, and is implemented in the R package 'lmms'.

Main Results:

• Simulation studies demonstrate high sensitivity and specificity for differential expression analysis.
• The framework successfully identified novel insights in breast cancer and kidney rejection time course omics studies.

Conclusions:

• The presented framework offers a powerful and robust approach for analyzing complex time course omics data.
• This methodology facilitates deeper understanding of dynamic biological regulation and disease mechanisms.