Detecting separate time scales in genetic expression data.


Change log
Authors
Orlando, David A 
Brady, Siobhan M 
Fink, Thomas MA 
Benfey, Philip N 
Ahnert, Sebastian E 
Abstract

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Description
Keywords
Arabidopsis, Benchmarking, Cell Cycle, Gene Expression Profiling, Plant Roots, Saccharomyces cerevisiae, Time Factors, Transcription, Genetic
Journal Title
BMC Genomics
Conference Name
Journal ISSN
1471-2164
1471-2164
Volume Title
Publisher
Springer Science and Business Media LLC