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Open Access Research article

Frequency-based time-series gene expression recomposition using PRIISM

Bruce A Rosa12, Yuhua Jiao2, Sookyung Oh2, Beronda L Montgomery23, Wensheng Qin1* and Jin Chen24*

Author Affiliations

1 Biorefining Research Initiative and Department of Biology, Lakehead University, 955 Oliver Road, Thunder Bay, ON, P7B 5E1, Canada

2 MSU-DOE Plant Research Laboratory, Plant Biology Laboratories, Michigan State University, 612 Wilson Road, Rm. 106, East Lansing, MI, 48824, USA

3 Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Road, Room 212, East Lansing, MI, 48824, USA

4 Department of Computer Sciences and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI, 48824, USA

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BMC Systems Biology 2012, 6:69  doi:10.1186/1752-0509-6-69

Published: 15 June 2012

Abstract

Background

Circadian rhythm pathways influence the expression patterns of as much as 31% of the Arabidopsis genome through complicated interaction pathways, and have been found to be significantly disrupted by biotic and abiotic stress treatments, complicating treatment-response gene discovery methods due to clock pattern mismatches in the fold change-based statistics. The PRIISM (Pattern Recomposition for the Isolation of Independent Signals in Microarray data) algorithm outlined in this paper is designed to separate pattern changes induced by different forces, including treatment-response pathways and circadian clock rhythm disruptions.

Results

Using the Fourier transform, high-resolution time-series microarray data is projected to the frequency domain. By identifying the clock frequency range from the core circadian clock genes, we separate the frequency spectrum to different sections containing treatment-frequency (representing up- or down-regulation by an adaptive treatment response), clock-frequency (representing the circadian clock-disruption response) and noise-frequency components. Then, we project the components’ spectra back to the expression domain to reconstruct isolated, independent gene expression patterns representing the effects of the different influences.

By applying PRIISM on a high-resolution time-series Arabidopsis microarray dataset under a cold treatment, we systematically evaluated our method using maximum fold change and principal component analyses. The results of this study showed that the ranked treatment-frequency fold change results produce fewer false positives than the original methodology, and the 26-hour timepoint in our dataset was the best statistic for distinguishing the most known cold-response genes. In addition, six novel cold-response genes were discovered. PRIISM also provides gene expression data which represents only circadian clock influences, and may be useful for circadian clock studies.

Conclusion

PRIISM is a novel approach for overcoming the problem of circadian disruptions from stress treatments on plants. PRIISM can be integrated with any existing analysis approach on gene expression data to separate circadian-influenced changes in gene expression, and it can be extended to apply to any organism with regular oscillations in gene expression patterns across a large portion of the genome.