Show simple item record

dc.contributor.authorStraube, J
dc.contributor.authorGorse, A-D
dc.contributor.authorHuang, BE
dc.contributor.authorLe Cao, K-A
dc.date.accessioned2021-02-05T01:14:20Z
dc.date.available2021-02-05T01:14:20Z
dc.date.issued2015-08-27
dc.identifierpii: PONE-D-15-09693
dc.identifier.citationStraube, J., Gorse, A. -D., Huang, B. E. & Le Cao, K. -A. (2015). A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data. PLOS ONE, 10 (8), https://doi.org/10.1371/journal.pone.0134540.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/260321
dc.description.abstractTime 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.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.titleA Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0134540
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.affiliation.facultyCollected Works
melbourne.source.titlePLoS One
melbourne.source.volume10
melbourne.source.issue8
dc.rights.licenseCC BY
melbourne.elementsid1101557
melbourne.contributor.authorLe Cao, Kim-Anh
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record