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Open Access Highly Accessed Methodology article

Detecting separate time scales in genetic expression data

David A Orlando12, Siobhan M Brady13, Thomas MA Fink45, Philip N Benfey1 and Sebastian E Ahnert6*

Author Affiliations

1 Department of Biology and IGSP Center for Systems Biology, Duke University, Durham, NC, USA

2 Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA, USA

3 Department of Plant Biology and Genome Center, University of California, Davis, CA, USA

4 INSERM U900 and CNRS UMR 144, Institut Curie, Paris, France

5 London Institute for Mathematical Sciences, London, UK

6 Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, UK

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BMC Genomics 2010, 11:381  doi:10.1186/1471-2164-11-381

Published: 16 June 2010



Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales.


We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions.


The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.