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Detecting separate time scales in genetic expression data
David A Orlando1, Siobhan M Brady, Thomas M A Fink
1Department of Biology and IGSP Center for Systems Biology, Duke University, Durham, NC, USA.
BMC Genomics
|June 23, 2010
Summary
This study introduces a new method to analyze gene expression data, successfully identifying biological processes occurring at different time scales in yeast and plants. This approach helps uncover complex biological functions from system-wide measurements.
Area of Science:
- Systems Biology
- Genomics
- Developmental Biology
Background:
- Biological processes operate across diverse and concurrent time scales.
- System-wide gene expression data captures simultaneous biological events.
- Distinguishing these processes and their time scales from data is a significant challenge, especially when unknown.
Purpose of the Study:
- To develop a statistically rigorous method for detecting multiple time scales in time-series gene expression data.
- To identify temporally shifted expression patterns between replicate datasets.
Main Methods:
- Developed a flexible statistical method to detect time scales in gene expression data.
- Applied the method to Saccharomyces cerevisiae cell-cycle and Arabidopsis thaliana root developmental datasets.
Main Results:
- The method successfully detected processes operating on several different time scales in both datasets.
- Identified associations between specific time scales and particular biological functions.
- Detected spatiotemporal modules indicating multiple biological processes in Arabidopsis root and yeast.
Conclusions:
- The developed method can identify biological processes acting at distinct time scales.
- This approach enables the identification of multi-time scale biological processes in various organisms using large-scale expression datasets.
