Detecting Periodically Expressed Genes with the Laplace Periodogram
Author Information
Author(s): Liang Kuo-ching, Wang Xiaodong, Li Ta-Hsin
Primary Institution: Columbia University
Hypothesis
Can the Laplace periodogram provide a more robust detection of periodic gene expression in the presence of outliers compared to existing methods?
Conclusion
The Laplace periodogram effectively detects periodic gene expression even in noisy datasets with outliers.
Supporting Evidence
- The Laplace periodogram outperformed existing methods when outliers were present.
- It performed comparably to existing methods for datasets without outliers.
- The study included datasets from Saccharomyces cerevisiae and Arabidopsis.
Takeaway
This study shows a new way to find genes that turn on and off in cycles, even when the data is messy.
Methodology
The study used the Laplace periodogram, which employs least absolute deviation to detect periodic gene expression.
Potential Biases
The M-estimator method used in comparison may not perform well in all scenarios, particularly with unknown datasets.
Limitations
The performance of the Laplace periodogram may vary depending on the dataset and the presence of outliers.
Digital Object Identifier (DOI)
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