Computational Method for Temporal Pattern Discovery in Biomedical Genomic Databases
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Active learning for sampling in time-series experiments with application to gene expression analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Signal Processing Systems
Gene Regulatory Network modelling: a state-space approach
International Journal of Data Mining and Bioinformatics
On Finding and Interpreting Patterns in Gene Expression Data from Time Course Experiments
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
International Journal of Data Mining and Bioinformatics
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
Ranking through integration of protein-similarity for identification of cell-cyclic genes
International Journal of Bioinformatics Research and Applications
Interval-focused similarity search in time series databases
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Approximate clustering of time series using compact model-based descriptions
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A novel statistical model for finding patterns in cell-cycle transcription data
Pattern Recognition Letters
Gene selection in time-series gene expression data
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Clustering gene expression data for periodic genes based on INMF
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Pooling evidence to identify cell cycle–regulated genes
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Comparison of methods for identifying periodically varying genes
International Journal of Bioinformatics Research and Applications
Time-Point specific weighting improves coexpression networks from time-course experiments
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.