Vol. 20 no. 1 2004, pages 5-20
Bioinformatics © Oxford University Press 2004; all rights reserved.
Identifying periodically expressed transcripts in microarray time series data
1 Department of Statistics, University of Munich, Ludwigstrasse 33, D-80539 Munich, Germany and 2 Department of Mathematics and Statistics, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus
Received on March 5, 2003
; revised on June 17, 2003
; accepted on July 8, 2003
Motivation: Microarray experiments are now routinely used to collect large-scale time series data, for example to monitor gene expression during the cell cycle. Statistical analysis of this data poses many challenges, one being that it is hard to identify correctly the subset of genes with a clear periodic signature. This has lead to a controversial argument with regard to the suitability of both available methods and current microarray data.
Methods: We introduce two simple but efficient statistical methods for signal detection and gene selection in gene expression time series data. First, we suggest the average periodogram as an exploratory device for graphical assessment of the presence of periodic transcripts in the data. Second, we describe an exact statistical test to identify periodically expressed genes that allows one to distinguish periodic from purely random processes. This identification method is based on the so-called g-statistic and uses the false discovery rate approach to multiple testing.
Results: Using simulated data it is shown that the suggested method is capable of identifying cell-cycle-activated genes in a gene expression data set even if the number of the cyclic genes is very small and regardless the presence of a dominant non-periodic component in the data. Subsequently, we re-examine 12 large microarray time series data sets (in part controversially discussed) from yeast, human fibroblast, human HeLa and bacterial cells. Based on the statistical analysis it is found that a majority of these data sets contained little or no statistical significant evidence for genes with periodic variation linked to cell cycle regulation. On the other hand, for the remaining data the method extends the catalog of previously known cell-cycle-specific transcripts by identifying additional periodic genes not found by other methods. The problem of distinguishing periodicity due to generic cell cycle activity and to artifacts from synchronization is also discussed.
Availability: The approach has been implemented in the R package GeneTS available from http://www.stat.uni-muenchen.de/~strimmer/software.html under the terms of the GNU General Public License.
Contact: strimmer{at}stat.uni-muenchen.de
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. E. Futschik and H. Herzel Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis Bioinformatics, April 15, 2008; 24(8): 1063 - 1069. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Usadel, O. E. Blasing, Y. Gibon, K. Retzlaff, M. Hohne, M. Gunther, and M. Stitt Global Transcript Levels Respond to Small Changes of the Carbon Status during Progressive Exhaustion of Carbohydrates in Arabidopsis Rosettes Plant Physiology, April 1, 2008; 146(4): 1834 - 1861. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Z. Bar-Joseph, Z. Siegfried, M. Brandeis, B. Brors, Y. Lu, R. Eils, B. D. Dynlacht, and I. Simon Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells PNAS, January 22, 2008; 105(3): 955 - 960. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. P. Gauthier, M. E. Larsen, R. Wernersson, U. de Lichtenberg, L. J. Jensen, S. Brunak, and T. S. Jensen Cyclebase.org a comprehensive multi-organism online database of cell-cycle experiments Nucleic Acids Res., January 11, 2008; 36(suppl_1): D854 - D859. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R.-S. Wang, Y. Wang, X.-S. Zhang, and L. Chen Inferring transcriptional regulatory networks from high-throughput data Bioinformatics, November 15, 2007; 23(22): 3056 - 3064. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. D. Mukhopadhyay and S. Chatterjee Causality and pathway search in microarray time series experiment Bioinformatics, February 15, 2007; 23(4): 442 - 449. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Cheng, X. Ma, X. Yan, F. Sun, and L. M. Li MARD: a new method to detect differential gene expression in treatment-control time courses Bioinformatics, November 1, 2006; 22(21): 2650 - 2657. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. E. Ahnert, K. Willbrand, F. C. S. Brown, and T. M. A. Fink Unbiased pattern detection in microarray data series Bioinformatics, June 15, 2006; 22(12): 1471 - 1476. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Qiu, Z. J. Wang, and K. J. R. Liu Polynomial model approach for resynchronization analysis of cell-cycle gene expression data Bioinformatics, April 15, 2006; 22(8): 959 - 966. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Zvonic, A. A. Ptitsyn, S. A. Conrad, L. K. Scott, Z. E. Floyd, G. Kilroy, X. Wu, B. C. Goh, R. L. Mynatt, and J. M. Gimble Characterization of peripheral circadian clocks in adipose tissues. Diabetes, April 1, 2006; 55(4): 962 - 970. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y.-H. Shi, S.-W. Zhu, X.-Z. Mao, J.-X. Feng, Y.-M. Qin, L. Zhang, J. Cheng, L.-P. Wei, Z.-Y. Wang, and Y.-X. Zhu Transcriptome Profiling, Molecular Biological, and Physiological Studies Reveal a Major Role for Ethylene in Cotton Fiber Cell Elongation PLANT CELL, March 1, 2006; 18(3): 651 - 664. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. F. Glynn, J. Chen, and A. R. Mushegian Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms Bioinformatics, February 1, 2006; 22(3): 310 - 316. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. E. Blasing, Y. Gibon, M. Gunther, M. Hohne, R. Morcuende, D. Osuna, O. Thimm, B. Usadel, W.-R. Scheible, and M. Stitt Sugars and Circadian Regulation Make Major Contributions to the Global Regulation of Diurnal Gene Expression in Arabidopsis PLANT CELL, December 1, 2005; 17(12): 3257 - 3281. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X. Mao, T. Cai, J. G. Olyarchuk, and L. Wei Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary Bioinformatics, October 1, 2005; 21(19): 3787 - 3793. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
U. de Lichtenberg, L. J. Jensen, A. Fausboll, T. S. Jensen, P. Bork, and S. Brunak Comparison of computational methods for the identification of cell cycle-regulated genes Bioinformatics, April 1, 2005; 21(7): 1164 - 1171. [Abstract] [Full Text] [PDF]
|
|