A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression.

ABSTRACT:

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 differentia

SUBMITTER: Kalaitzis AA 

PROVIDER: S-EPMC3116489 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature