A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process. Results: We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS)....

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Alfredo A. Kalaitzis, Neil D. Lawrence
• Keywords: Biometrics, BMCBI 2011, Confidence Intervals, Gene Expression Profiles, ROC Curves