li-temporalpresedencebased-2010.pdf (6.48 MB)
A temporal precedence based clustering method for gene expression microarray data
journal contribution
posted on 2010-01-30, 00:00 authored by Ritesh Krishna, Chang-Tsun LiChang-Tsun Li, Vicky Buchanan-WollastonBACKGROUND: 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.
History
Journal
BMC bioinformaticsVolume
11Article number
68Pagination
1 - 25Publisher
BioMed CentralLocation
London, Eng.Publisher DOI
eISSN
1471-2105Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2010, Krishna et alUsage metrics
Categories
No categories selectedKeywords
ArabidopsisCluster AnalysisComputational BiologyDatabases, GeneticGene ExpressionGene Expression ProfilingOligonucleotide Array Sequence AnalysisGene OntologyGranger CausalityBiological NetworkDense RegionSynthetic DatasetScience & TechnologyLife Sciences & BiomedicineBiochemical Research MethodsBiotechnology & Applied MicrobiologyMathematical & Computational BiologyBiochemistry & Molecular BiologyMIXTURE MODELPATHWAY SEARCHCAUSALITYORGANIZATIONMODULARITYCYTOSCAPENETWORKSSOFTWAREDYNAMICSTOOL
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC