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

Straube, Jasmin, Gorse, Alain-Dominique, Huang, Bevan Emma and Le Cao, Kim-Anh (2015) A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data. PLoS One, 10 8: e0134540-e0134540. doi:10.1371/journal.pone.0134540


Author Straube, Jasmin
Gorse, Alain-Dominique
Huang, Bevan Emma
Le Cao, Kim-Anh
Title A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2015-08
Year available 2015
Sub-type Article (original research)
DOI 10.1371/journal.pone.0134540
Open Access Status DOI
Volume 10
Issue 8
Start page e0134540
End page e0134540
Total pages 20
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Collection year 2016
Language eng
Formatted abstract
Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.
Keyword Gene Expression Data
Renal Allograft Rejection
Longitudinal Data
B Splines
Cancer
Curves
Identification
Mechanisms
Discovery
Series
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
Institute for Molecular Bioscience - Publications
UQ Diamantina Institute Publications
 
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