Robust discovery of periodically expressed genes using the laplace periodogram.
BACKGROUND
Time-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle.


RESULTS
In this work, we present the Laplace periodogram which employs the least absolute deviation criterion to provide a more robust detection of periodic gene expression in the presence of outliers. The Laplace periodogram is shown to perform comparably to existing methods for the Sacharomyces cerevisiae and Arabidopsis time-course datasets, and to outperform existing methods when outliers are present.


CONCLUSIONS
Time-course gene expression data are often noisy due to the limitations of current technology, and may include outliers. These artifacts corrupt the available data and make the detection of periodicity difficult in many cases. The Laplace periodogram is shown to perform well for both data with and without the presence of outliers, and also for data that are non-uniformly sampled.
Liang
Kuo-ching
KC
Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. kl2171@columbia.edu
Wang
Xiaodong
X
Li
Ta-Hsin
TH
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics