EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Spectral preprocessing for clustering time-series gene expressions
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Detecting movement patterns with wireless sensor networks: application to bird behavior
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Mining event periodicity from incomplete observations
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of methods for identifying periodically varying genes
International Journal of Bioinformatics Research and Applications
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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Motivation: Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spaced gene expression time series has not been well substantiated. Methods: The Lomb--Scargle periodogram approach is used to search time series of gene expression to quantify the periodic behavior of every gene represented on the DNA array. The Lomb--Scargle periodogram analysis provides a direct method to treat missing values and unevenly spaced time points. We propose the combination of a Lomb--Scargle test statistic for periodicity and a multiple hypothesis testing procedure with controlled false discovery rate to detect significant periodic gene expression patterns. Results: We analyzed the Plasmodium falciparum gene expression dataset. In the Quality Control Dataset of 5080 expression patterns, we found 4112 periodic probes. In addition, we identified 243 probes with periodic expression in the Complete Dataset, which could not be examined in the original study by the FFT analysis due to an excessive number of missing values. While most periodic genes had a period of 48 h, some had a period close to 24 h. Our approach should be applicable for detection and quantification of periodic patterns in any unevenly spaced gene expression time-series data. Availability: The computations were performed in R. The R code is available from http://research.stowers-institute.org/efg/2005/LombScargle Contact: chenj@umkc.edu Supplementary information: The online supplement is available at http://research.stowers-institute.org/efg/2005/LombScargle