A temporal precedence based clustering method for gene expression microarray data

Krishna, Ritesh, Li, Chang-Tsun and Buchanan-Wollaston, Vicky 2010, A temporal precedence based clustering method for gene expression microarray data, BMC bioinformatics, vol. 11, pp. 1-25, doi: 10.1186/1471-2105-11-68.

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Title A temporal precedence based clustering method for gene expression microarray data
Author(s) Krishna, Ritesh
Li, Chang-TsunORCID iD for Li, Chang-Tsun orcid.org/0000-0003-4735-6138
Buchanan-Wollaston, Vicky
Journal name BMC bioinformatics
Volume number 11
Article ID 68
Start page 1
End page 25
Total pages 25
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2010-01-30
ISSN 1471-2105
Keyword(s) Arabidopsis
Cluster Analysis
Computational Biology
Databases, Genetic
Gene Expression
Gene Expression Profiling
Oligonucleotide Array Sequence Analysis
Gene Ontology
Granger Causality
Biological Network
Dense Region
Synthetic Dataset
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
Summary 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.
Language eng
DOI 10.1186/1471-2105-11-68
Field of Research 06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Krishna et al
Persistent URL http://hdl.handle.net/10536/DRO/DU:30119429

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