MOTIVATION
Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters.
RESULTS
We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters.
AVAILABILITY
The software is available upon request.
CONTACT
jiliping@comp.nus.edu.sg
SUPPLEMENTARY INFORMATION
Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.
[1]
Ronald W. Davis,et al.
A genome-wide transcriptional analysis of the mitotic cell cycle.
,
1998,
Molecular cell.
[2]
Hong Yan,et al.
Dominant spectral component analysis for transcriptional regulations using microarray time-series data
,
2004,
Bioinform..
[3]
Nir Friedman,et al.
Context-Specific Bayesian Clustering for Gene Expression Data
,
2002,
J. Comput. Biol..
[4]
George M. Church,et al.
Biclustering of Expression Data
,
2000,
ISMB.
[5]
Steven Skiena,et al.
Identifying gene regulatory networks from experimental data
,
2001,
Parallel Comput..
[6]
Holger H. Hoos,et al.
Inference of Transcriptional Regulation Relationships from Gene Expression Data
,
2003,
Bioinform..
[7]
Ricardo Baeza-Yates,et al.
Information Retrieval: Data Structures and Algorithms
,
1992
.
[8]
Tatsuhiko Tsunoda,et al.
Lag Analysis of Genetic Networks in the Cell Cycle of Budding Yeast
,
2001
.