New Method for Analyzing Periodically Expressed Genes in Unevenly Sampled Microarray Data
Author Information
Author(s): Liew Alan Wee-Chung, Xian Jun, Wu Shuanhu, Smith David, Yan Hong
Primary Institution: Griffith University
Hypothesis
Can a new spectral estimation algorithm improve the detection of periodically expressed genes in unevenly sampled microarray time series data?
Conclusion
The proposed method effectively identifies periodic genes in unevenly sampled microarray time series data, outperforming existing methods.
Supporting Evidence
- The new algorithm was tested on simulated noisy gene expression profiles and showed superior performance compared to the Lomb-Scargle method.
- Experiments on real gene expression data from Plasmodium falciparum and Yeast confirmed the algorithm's ability to detect biologically meaningful periodic genes.
- The method can also be used for gene expression time series interpolation or resampling.
Takeaway
This study introduces a new way to find genes that turn on and off in cycles, even when the data is messy and not evenly spaced out.
Methodology
The study developed a spectral estimation algorithm based on signal reconstruction in a shift-invariant signal space using B-spline basis.
Limitations
The method's effectiveness may vary with different noise levels and missing data ratios.
Digital Object Identifier (DOI)
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