A linear mixed model spline framework for analysing time course 'Omics' data

Abstract

Time course 'omics' experiments are becoming increasingly important to study systemwide 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 consis..