Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

Authors:
Y. Luan;H. Li
Affiliations:
Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA;Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA
Venue:
Bioinformatics
Year:
2004

Citing 0
Cited 8

Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions

Computational Statistics & Data Analysis
Study of microarray time series data based on Forward-Backward Linear Prediction and Singular Value Decomposition

International Journal of Data Mining and Bioinformatics
Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge

EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data

Computational Statistics & Data Analysis
Data-driven smoothness enhanced variance ratio test to unearth responsive genes in 0-time normalized time-course microarray data

ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Microarray Time Course Experiments: Finding Profiles

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Comparison of methods for identifying periodically varying genes

International Journal of Bioinformatics Research and Applications
OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	3.84

Visualization

Abstract

Motivation: The expressions of many genes associated with certain periodic biological and cell cycle processes such as circadian rhythm regulation are known to be rhythmic. Identification of the genes whose time course expressions are synchronized to certain periodic biological process may help to elucidate the molecular basis of many diseases, and these gene products may in turn represent drug targets relevant to those diseases. Results: We propose in this paper a statistical framework based on a shape-invariant model together with a false discovery rate (FDR) procedure for identifying periodically expressed genes based on microarray time-course gene expression data and a set of known periodically expressed guide genes. We applied the proposed methods to the α-factor, cdc15 and cdc28 synchronized yeast cell cycle data sets and identified a total of 1010 cell-cycle-regulated genes at a FDR of 0.5% in at least one of the three data sets analyzed, including 89 (86%) of 104 known periodic transcripts. We also identified 344 and 201 circadian rhythmic genes in vivo in mouse heart and liver tissues with FDR of 10 and 2.5%, respectively. Our results also indicate that the shape-invariant model fits the data well and provides estimate of the common shape function and the relative phases for these periodically regulated genes. Availability: Matlab programs are available on request from the authors. Supplementary information: http://dna.ucdavis.edu/~hli/period.html