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Difference-based clustering of short time-course microarray data with replicates

Cited 12 time in Web of Science Cited 13 time in Scopus
Authors
Kim, Jihoon; Kim, Ju Han
Issue Date
2007-07-17
Publisher
BioMed Central
Citation
BMC Bioinformatics. 2007;8:253
Keywords
*Algorithms*Artificial Intelligence*Cluster AnalysisGene Expression Profiling/*methodsOligonucleotide Array Sequence Analysis/*methodsPattern Recognition, Automated/*methodsTime Factors
Abstract
BACKGROUND: There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically. RESULTS: We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods. CONCLUSIONS: Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.
ISSN
1471-2105 (Electronic)
Language
English
URI
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17629922

https://hdl.handle.net/10371/22188
DOI
https://doi.org/10.1186/1471-2105-8-253
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College of Medicine/School of Medicine (의과대학/대학원)Program in Clinical Pharmacology (협동과정-임상약리학전공)Journal Papers (저널논문_협동과정-임상약리학전공)
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