Identifying time-lagged gene clusters using gene expression data.
BACKGROUND
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.


BACKGROUND
The software is available upon request.


BACKGROUND
jiliping@comp.nus.edu.sg


BACKGROUND
Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.
Ji
Liping
L
Department of Computer Science, National University of Singapore 3 Science Drive 2, Singapore 117543, Singapore. jiliping@comp.nus.edu.sg
Tan
Kian-Lee
KL
D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D012984:Software; D013997:Time Factors
D029701:Saccharomyces cerevisiae Proteins