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

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 s

SUBMITTER: Straube J 

PROVIDER: S-EPMC4551847 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature