Robust discovery of periodically expressed genes using the laplace periodogram

BackgroundTime-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.ResultsIn 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.ConclusionTime-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.

[1]  Peer Bork,et al.  Comparison of computational methods for the identification of cell cycle-regulated genes , 2005, Bioinform..

[2]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[3]  J. Scargle.  Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .

[4]  G. Moody,et al.  Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals , 1998, IEEE Transactions on Biomedical Engineering.

[5]  Nicola J. Rinaldi,et al.  Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle , 2001, Cell.

[6]  Michael F. Covington,et al.  The Circadian Clock Regulates Auxin Signaling and Responses in Arabidopsis , 2007, PLoS biology.

[7]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[8]  N. Lomb.  Least-squares frequency analysis of unequally spaced data , 1976 .

[9]  Søren Brunak,et al.  Cyclebase.org—a comprehensive multi-organism online database of cell-cycle experiments , 2007, Nucleic Acids Res..

[10]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Ta-Hsin Li,et al.  Laplace Periodogram for Time Series Analysis , 2008 .

[12]  Hongzhe Li,et al.  Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data , 2004, Bioinform..

[13]  Thomas Ruf,et al.  The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series , 1999 .

[14]  Tommi S. Jaakkola,et al.  Continuous Representations of Time-Series Gene Expression Data , 2003, J. Comput. Biol..

[15]  Dmitrij Frishman,et al.  MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..

[16]  M Schimmel,et al.  Emphasizing Difficulties in the Detection of Rhythms with Lomb-Scargle Periodograms , 2001, Biological rhythm research.

[17]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[18]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[19]  Anders Berglund,et al.  A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription , 2003, Bioinform..

[20]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

[21]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[22]  H P Van Dongen,et al.  A procedure of multiple period searching in unequally spaced time-series with the Lomb-Scargle method. , 1999, Biological rhythm research.

[23]  Jie Chen,et al.  Bioinformatics Original Paper Detecting Periodic Patterns in Unevenly Spaced Gene Expression Time Series Using Lomb–scargle Periodograms , 2022 .

[24]  Martin Straume,et al.  DNA Microarray Time Series Analysis: Automated Statistical Assessment of Circadian Rhythms in Gene Expression Patterning , 2004, Numerical Computer Methods, Part D.

[25]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[27]  P. Husar,et al.  A periodogram-based method for the detection of steady-state visually evoked potentials , 1998, IEEE Transactions on Biomedical Engineering.

[28]  Andrey A. Ptitsyn,et al.  Permutation test for periodicity in short time series data , 2006, BMC Bioinformatics.

[29]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[30]  J. Derisi,et al.  The Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum , 2003, PLoS biology.

[31]  C. Ball,et al.  Identification of genes periodically expressed in the human cell cycle and their expression in tumors. , 2002, Molecular biology of the cell.

[32]  Ronald W. Davis,et al.  Transcriptional regulation and function during the human cell cycle , 2001, Nature Genetics.

[33]  Jie Chen,et al.  Identification of significant periodic genes in microarray gene expression data , 2005, BMC Bioinformatics.

[34]  Ilya Shmulevich,et al.  Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data , 2007, BMC Bioinformatics.

[35]  P. Lio’,et al.  Periodic gene expression program of the fission yeast cell cycle , 2004, Nature Genetics.

[36]  Korbinian Strimmer,et al.  Identifying periodically expressed transcripts in microarray time series data , 2008, Bioinform..

[37]  Wolfram Liebermeister,et al.  Linear modes of gene expression determined by independent component analysis , 2002, Bioinform..