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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
BMC Genomics. 2009; 10: 440.
Published online Sep 17, 2009. doi:  10.1186/1471-2164-10-440
PMCID: PMC2753555
Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast
Saumyadipta Pyne,corresponding author1,2 Roee Gutman,3 Chang Sik Kim,4 and Bruce Futchercorresponding author5
1Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
2Present address: Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
3Department of Statistics, Harvard University, Cambridge, MA 02138, USA
4Department of Biological Sciences, RCWD, Sookmyung Women's University, Seoul, Republic of Korea
5Department of Molecular Genetics and Microbiology, Stony Brook University, Stony Brook, NY 11794, USA
corresponding authorCorresponding author.
Saumyadipta Pyne: saumyadipta_pyne/at/; Roee Gutman: rgutman/at/; Chang Sik Kim: cskim/at/; Bruce Futcher: bfutcher/at/
Received May 29, 2009; Accepted September 17, 2009.
Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction.
Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes.
A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.
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