Minas, Christopher, Waddell, Simon J and Montana, Giovanni (2011) Distance-based differential analysis of gene curves. Bioinformatics, 27 (22). pp. 3135-3141. ISSN 1367-4803

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Abstract

Motivation: Time course gene expression experiments are performed to study time-varying changes in mRNA levels of thousands of genes. Statistical methods from functional data analysis (FDA) have recently gained popularity for modelling and exploring such time courses. Each temporal profile is treated as the realization of a smooth function of time, or curve, and the inferred curve becomes the basic unit of statistical analysis. The task of identifying genes with differential temporal profiles then consists of detecting statistically significant differences between curves, where such differences are commonly quantified by computing the area between the curves or the l2 distance.

Results: We propose a general test statistic for detecting differences between gene curves, which only depends on a suitably chosen distance measure between them. The test makes use of a distance-based variance decomposition and generalizes traditional MANOVA tests commonly used for vectorial observations. We also introduce the visual l2 distance, which is shown to capture shape-related differences in gene curves and is robust against time shifts, which would otherwise inflate the traditional l2 distance. Other shape-related distances, such as the curvature, may carry biological significance. We have assessed the comparative performance of the test on realistically simulated datasets and applied it to human immune cell responses to bacterial infection over time.

Item Type:	Article
Schools and Departments:	Brighton and Sussex Medical School > Global Health and Infection
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics Q Science > QR Microbiology > QR0075 Bacteria Q Science > QR Microbiology > QR0180 Immunology
Depositing User:	Simon Waddell
Date Deposited:	17 Jul 2012 13:11
Last Modified:	02 Jul 2019 18:04
URI:	http://sro.sussex.ac.uk/id/eprint/7646

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