• A temporal precedence based cl...

Treffer: A temporal precedence based clustering method for gene expression microarray data.

Title:
A temporal precedence based clustering method for gene expression microarray data.
Authors:
Krishna R; Department of Computer Science, Warwick University, Coventry CV4 7AL, UK., Li CT, Buchanan-Wollaston V
Source:
BMC bioinformatics [BMC Bioinformatics] 2010 Jan 30; Vol. 11, pp. 68. Date of Electronic Publication: 2010 Jan 30.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Gene Expression Profiling/*methods , Oligonucleotide Array Sequence Analysis/*methods, Arabidopsis/genetics ; Cluster Analysis ; Computational Biology ; Databases, Genetic ; Gene Expression
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Grant Information:
BB/F005806/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council
Entry Date(s):
Date Created: 20100202 Date Completed: 20100511 Latest Revision: 20250529
Update Code:
20250530
PubMed Central ID:
PMC2841598
DOI:
10.1186/1471-2105-11-68
PMID:
20113513
Database:
MEDLINE

Weitere Informationen

Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.
Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system.
Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits.